EXAMINING THE ASSOCIATION BETWEEN REFLECTIVE-FUNCTIONING AND EXECUTIVE-FUNCTIONING By Evan W. Good A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Psychology—Doctor of Philosophy 2022 ABSTRACT EXAMINING THE ASSOCIATION BETWEEN REFLECTIVE-FUNCTIONING AND EXECUTIVE-FUNCTIONING By Evan W. Good Despite the growth in applied theory and research focused on the construct of reflective functioning (RF) in the recent decades, there has been minimal basic research examining RF. This lack of research has left gaps in understanding of the basic cognitive processes associated with RF. Understanding such processes is not only important for integrating RF within the broader clinical science landscape but also presents opportunities for developing new experimental paradigms for researching RF. Given overlaps between the functionality, developmental course, and the neurobiological bases of RF and executive-functioning (EF), examining the association between RF and EF presents a critical first step in connecting RF with basic psychological research. In the current study, I examine the association between RF and EF cross-sectionally in a community sample of individuals ranging in age from 15-45 years old. In addition to examining the basic association between RF and EF, I also examine the degree to which age, history of childhood maltreatment, and adult attachment style moderate this relationship. Results from this study suggest that RF is positively associated with domains of EF, such that higher EF is related to higher ability to identify, process, and express internal states of self and other. Age, childhood maltreatment, and adult attachment style are associated with EF and RF, and childhood maltreatment and adult attachment style tend to moderate the association between EF and one’s motivation towards RF. These results provide some initial understanding of the relationship between basic cognitive functions, relational experiences, and RF, and highlight the potential for future work to continue to integrate the construct of RF within the broader clinical science literature. ACKNOWLEDGMENTS I would like to express my deepest appreciation to my advisor, Dr. Alytia Levendosky, not only for her support and guidance throughout the conceptualization and writing of this dissertation, but also for her unparalleled role in my personal and professional development throughout my doctoral training. I would also like to thank the members of my doctoral committee, Drs. Joshua Turchan, Jason Moser, and Bill Chopik, for their helpful feedback on this dissertation and as well as the various mentoring roles they have played in my training the past several years. I would also like to thank those closest to me, my friends and family, who have provided love and support throughout my doctoral training. Mom, Dad, and Elyse, you gave me a secure base to venture into the world of academia and explore my passions. Alexandra, thank you for always believing in me. Your steadfast support—both intellectually and emotionally—has been a beacon of hope and inspiration in moments when my motivation and confidence were lowest. Finally, I would like to thank the patients that I have had the privilege to work with throughout my training. Each of you have taught me and inspired me. This dissertation would not exist without the experiences we have shared together. iv TABLE OF CONTENTS LIST OF TABLES vii LIST OF FIGURES viii INTRODUCTION 1 Reflective-Functioning 2 Self-and Social-Regulatory Functions 5 Development 11 Neurobiological Basis 14 Executive-Functioning 18 Working Memory 20 Inhibitory Control 22 Connecting EF and RF 25 Moderators of the EF and RF Relationship 29 Age 29 Childhood Maltreatment 31 Adult Attachment Style 34 Current Study 39 Exploratory Aims 40 Aim 1: Determine the Latent Structure of EF. 40 Aim 2: Examine the Association between Gender, SES, RF, and EF. 41 Predictive Aims & Hypotheses 41 Aim 1: Examine Association between RF and EF. 41 Aim 2: Examine the Association between Age, Childhood Maltreatment, and Adult Attachment Style and RF and EF. 41 Aim 3: Examine the Moderating Role of Age, Childhood Maltreatment, and Adult Attachment Style on the Association between RF and EF. 42 METHOD 44 Sample 44 Procedure 44 Measures 45 Demographics 45 Reflective-Functioning 46 Childhood Maltreatment 47 Adult Attachment Style 48 Working Memory 49 Inhibitory Control 51 Data Analysis 54 RESULTS 58 Exploratory Aim 1: Determine the Latent Structure of EF 58 Exploratory Aim 2: Examine the Association between Gender, SES, RF and EF 58 v Predictive Aim 1: Examine the Association between RF and EF 59 Predictive Aim 2: Examine the Association between Age, Childhood Maltreatment, and Adult Attachment Style and RF and EF 59 Predictive Aim 3: Examine the Moderating Role of Age, Childhood Maltreatment, and Adult Attachment Style on the Association between RF and EF 60 Gender 60 SES Growing-up 61 Current SES 62 Age 62 Childhood Maltreatment 63 Adult Attachment Anxiety 64 Adult Attachment Avoidance 65 DISCUSSION 67 Mentalization Orientation 68 Mentalization Ability 70 General Discussion 72 Limitations and Future Directions 75 CONCLUSION 79 APPENDICES 80 APPENDIX A: Tables 81 APPENDIX B: Figures 95 REFERENCES 103 vi LIST OF TABLES Table 1. Sociodemographic Characteristics of Participants 81 Table 2. Principal Component Factor Loadings for SES Growing-up and Current SES Indicators 83 Table 3. Principal Component Factor Loadings for Childhood Maltreatment Indicators 84 Table 4. CFA Factor Loadings for Correlated Two-Factor Model of EF Indicators 85 Table 5. Correlations Between Gender, SES, EF Factors, and RF Parameters 86 Table 6. Descriptive Statistics and Correlations for Study Variables 87 Table 7. Multiple Regression Results Predicting RF Parameters by Gender and EF Factor 88 Table 8. Multiple Regression Results Predicting RF Parameters by SES Growing-up and EF Factor 89 Table 9. Multiple Regression Results Predicting RF Parameters by Current SES and EF Factor 90 Table 10. Multiple Regression Results Predicting RF Parameters by Age and EF Factor 91 Table 11. Multiple Regression Results Predicting RF Parameters by Childhood Maltreatment and EF Factor 92 Table 12. Multiple Regression Results Predicting RF Parameters by Attachment Anxiety and EF Factor 93 Table 13. Multiple Regression Results Predicting RF Parameters by Attachment Avoidance and EF Factor 94 vii LIST OF FIGURES Figure 1. Interaction between SES Growing-up and Working Memory Predicting Mentalization Orientation 95 Figure 2. Interaction between SES Growing-up and Inhibitory Control Predicting Mentalization Orientation 96 Figure 3. Interaction between Childhood Maltreatment and Working Memory Predicting Mentalization Orientation 97 Figure 4. Interaction between Childhood Maltreatment and Inhibitory Control Predicting Mentalization Orientation 98 Figure 5. Interaction between Attachment Anxiety and Working Memory Predicting Mentalization Orientation 99 Figure 6. Interaction between Attachment Anxiety and Inhibitory Control Predicting Mentalization Orientation 100 Figure 7. Interaction between Attachment Avoidance and Working Memory Predicting Mentalization Orientation 101 Figure 8. Interaction between Attachment Avoidance and Inhibitory Control Predicting Mentalization Orientation 102 viii INTRODUCTION The construct of reflective-functioning (RF) has received growing attention from attachment and psychodynamically-oriented theorists and researchers since it was first described by Fonagy and colleagues (1991) three decades ago. A majority of extant theory and research on RF has focused on the association between RF and adult psychopathology (e.g., Fonagy, 1991; Fonagy & Bateman, 2008; Fonagy & Luyten, 2009), the role of early attachment relationships in the development of RF (e.g., Fonagy, Gergely, & Target, 2007; Fonagy, Steele, Steele, Moran, & Higgitt, 1991; Slade, 2005), and the role of RF as moderator and outcome of psychotherapy (e.g., Bateman & Fonagy, 2001; 2008; Fonagy & Bateman, 2004; Levy et al., 2006). This work has been highly impactful on the way that dynamically-oriented psychologists think about development and psychopathology, leading some to argue that RF is a crucial and foundational aspect of emotion regulation and social functioning in all humans (e.g., Fonagy, Gergely, Jurist, & Target, 2002; Slade, 2005). Despite this, there has been little empirical work to extend the construct of RF into the cognitive-affective neuroscience paradigm that dominates contemporary clinical science. While this may be in part due to the fact that RF has narrowly been measured by coding attachment interviews throughout much of its history (Luyten et al., 2009; Luyten & Fonagy, 2014)—a time and resource intensive process—the lack of research connecting RF to basic cognitive processes has also limited this integration. This is lack of integration is noteworthy as RF is posited to engage a wide range of basic social-cognitive processes, including perceptual-motor functioning, associative and semantic memory, and executive functioning. The lack of research connecting RF with executive functioning (EF) is particularly striking given that descriptions of RF often highlight its role in inhibiting automatic behavioral responses to emotions (e.g., Bateman & 1 Fonagy, 2008; Fonagy & Luyten, 2009) and a large, but disparate, body of research has similarly argued that deficits in EF are an underlying aspect of all most all forms of psychopathology (e.g., Berg, Latzman, Bliwise, & Lilienfeld, 2015; Carver, Johnson, & Timpano, 2017; Caspi et al., 2014; Nigg, 2000; Wright, Lipszyc, Dpuis, Thayapararajah, & Schachar, 2014). Thus, the empirical examination of the association between RF and EF would present a theoretically salient avenue for beginning to integrate RF within the larger clinical science landscape. Additionally, such research may help develop novel research paradigms for assessing RF, which may further catalyze the empirical examination of the construct. Given this, the aim of this study is to further develop the construct of RF by examining its association to EF. Additionally, this study aims to explore the moderating role of other constructs (age, history of childhood maltreatment, adult attachment style) on the association between RF and EF. To this end, I begin with a brief review of the theory and research on RF, including its functionality, development, and neurobiological basis. I then discuss the overlapping aspects of RF and EF in service of building a theoretical argument for their association, as well as for the constructs that are likely to moderate their association. I then present findings from my study in which I test several hypotheses regarding the associations between RF, EF, age, maltreatment experiences, and attachment style in a community sample of individuals ranging from adolescence to middle-adulthood,. Reflective-Functioning The term reflective functioning (RF) is derived from Fonagy and colleagues (1991) work examining the nature of self-organization, in which they draw distinction between the prereflective self—the immediate, unmediated experience of the self (i.e., feelings, thoughts, desires, and beliefs)—and the reflective self—the metacognitive capacity to observe and reflect 2 on the mental experiences of the prereflective self. Fonagy and colleagues (1989; 1991a; 1991b; 2002; 2007; 2008) argue that the development of “reflective self-functioning”, referred to simply as RF in more recent work, represents an essential aspect of psychosocial functioning that facilitates social understanding, bonding, identity development, and emotion-regulation throughout humans’ lifespan. The term mentalization is often used interchangeably with RF to describe the mental processes by which an individual interprets one’s own and other actions as an expression of intentional mental states, such as feelings, thoughts, desires, and beliefs (Bateman & Fonagy, 2004). From a theoretical vantage, RF aids in an individual’s ability to represent, understand, and predict causation within self-other relational systems (Fonagy et al., 2002; Slade, 2005). RF is suggested to be particular important given that humans largely rely on self-other relational systems to meet their physical (e.g., food, water, safety) and psychological needs (e.g., connection, control, meaning-making) (Fonagy et al., 1998; 2002). Theoretical descriptions of RF have been elaborated to suggest that it not only pertains to the capacity to understand behavior as an expression of internal state, but also that this understanding is based on an accurate model of the human mind (Fonagy et al., 1998; 2002; Fonagy & Luyten, 2016). This means that individuals with adaptive RF understand that internal states are often not objective or fully conscious, that they are able to be disguised or hidden from others, and that insight about internal states can be limited by other psychological processes (e.g., anxiety, defensiveness). Additionally, individuals with high RF have ability to recognize the developmental components of internal states, including understanding the influence of upbringing on internal states, understanding that internal states change over time, and showing an appreciation for family 3 dynamics and the normative differences in the capacity to have insight and ability to communicate internal states in children versus adults. Finally, RF not only describes one’s capacity or ability to accurately understand the connection between internal states and behavior, but also their motivation towards understanding and reflecting on internal states (Jurist, 2005). Thus, individuals with high RF are assumed to make explicit efforts to understand how internal states drive their own and others’ behavior and express a non-defensive willingness to engage with their internal experiences; to make meaning of internal experiences without becoming overwhelmed or shutting down (Slade, 2005). This orientation is also often assumed to connote an understanding (either implicitly or explicitly) that one can create new meaning of their internal experiences by reflecting on them (Jurist, 2005). RF and mentalization are conceptually similar to constructs such as theory of mind (ToM), empathy, perspective-taking, and mindfulness, which also are suggested to play important roles in adaptive psychosocial functioning. RF is differentiated theoretically from these constructs in important ways. In particular, ToM represents the generalized capacity of individuals to understand and appreciate how minds work (Premack & Woodruff, 1978), whereas RF is the implementation of ToM to understand the minds of specific individuals, often within attachment contexts (Fonagy et al., 1998). Further, while definitions of ToM have varied throughout the literature, ToM is often operationalized experimentally using tasks that focus on the ability of individuals to understand other’s knowledge states (i.e., the false-belief task; Wimmer & Perner, 1983), whereas RF connotes the ability to understand both cognitive and affective components of others’ mental states (Choi-Kain & Gunderson, 2008; Fonagy & Bateman, 2008; Fonagy & Luyten, 2017). 4 With regard to constructs such as empathy and perspective-taking, while RF certainly includes a focus on and orientation to understanding others’ mind-states, it is equally oriented to understanding the mind-states of the self (Allen, Fonagy, & Bateman, 2008; Choi-Kain & Gunderson, 2008). In addition to this, RF also includes interpersonal and affective regulatory features that are not necessarily inherent in empathy or perspective taking (Choi-Kain & Gunderson, 2008). Finally, with regard to mindfulness, RF is similar in that it also pertains to the ability to willingly and non-defensively reflect on one’s experience as a way of mitigating tendencies toward impulsivity and reactivity (Baer, Smith, & Allen, 2004; Bishop et al., 2004). However, unlike mindfulness, which is not inherently tied to relational experiences, RF is specifically related to being mindful about the bidirectional connection between internal states and behavior in the context of self-other relational situations (Choi-Kain & Gunderson, 2008). Further, mindfulness is often associated with orientation toward the present (Bishop et al., 2004), whereas RF may include orienting to experiences in the past present, and future (Choi-Kain & Gunderson, 2008). Unlike with many of these other constructs, some research has examined the empirical relationship between mindfulness and RF and found a small, but significant, relationship between the two constructs (Falkenstrom et al., 2014). This research generally supports the notion that mindfulness and RF are overlapping, but not interchangeable constructs. Self- and Social-Regulatory Functions From a theoretical perspective, RF is argued to facilitate a wide array of adaptive self- and social-regulatory functions. While the benefits of RF have been previously described in a myriad of ways (Fonagy et al., 1998; 2002), at a very functional level, RF is argued to reflect an individuals’ capacity to 1) maintain a coherent, and accurate view of oneself and others, 2) accept and tolerate the limitations and imperfections of oneself and others, and 3) experience 5 empathy and forgiveness for oneself and others, in the face of difficult or distressing life experiences (e.g., Bateman & Fonagy, 2008; Main, Goldwyn, & Hesse, 2002). These capacities are further suggested to promote one’s ability to, 1) accept and tolerate immediate and automatic emotional reactions to arousing interpersonal experiences, and 2) inhibit automatic responses to immediate emotional reactions in service of behavioral responses based on controlled, reflective social decision-making processes that enhance the individual’s ability to productively and sustainably maintain meaningful, intimate, communal, and mutually autonomous relationships with others (Bateman & Fonagy, 2008; Slade, 2005). There are several ways in which mentalizing helps individuals to maintain a “reflective” stance in the face of emotional arousal. This foremost includes the ability for individuals to understand their own and others’ minds as intentional agents, including understanding of basic mental capacities that lead to behavioral decision making, such as belief, desire, and awareness (Malle, Moses, & Bladwin, 2001). Understanding minds as intentional agents allows individuals to perceive behavior as goal-directed and meaningful, which increases the predictability of behavior and reduces the need for others to explain their actions (Fonagy et al., 1998; Malle et al., 2001; Slade, 2005). The ability to hold other’s intentions and goals in mind has many adaptive benefits, including allowing for individuals to more easily coordinate and cooperate in social groups (Fonagy et al., 1998) and promoting meaning-making processes in relationships (e.g., Echterhoff, Higgins, & Levine, 2009). Additionally, understanding intentionality allows individuals to separate behaviors from outcomes—such as recognizing that other’s limitations or failures to meet our needs may be separate from their intention to meet our needs. Having such understanding is argued to promote an individual’s ability to maintain attachment security and 6 experience empathy and forgiveness towards others even when their needs are not met (Fonagy et al., 1998; Main et al., 2002). While the association between RF and the capacity to understand intentionality has not been directly empirically examined, social psychological research suggests that perceiving intentionality is a basic component of ToM and often moderates how individuals respond to others’ behavior (Malle & Knobe, 1997; Malle, et al., 2001; Struthers, Eaton, Santelli, Uchiyama, & Shirvani, 2008). For example, when individuals perceive their own or other’s failures (e.g., burning dinner) as unintentional or accidental, they tend to make situational attributions to the cause of behavior (e.g., the cook was over-burdened) (Malle, Knobe, & Nelson, 2007) and respond with greater empathy, helping behavior, and/or forgiveness (Struthers et al., 2008). However, when the same behavior is judged to be intentional, they are more likely to make dispositional attributions about the cause of behavior (e.g., the cook is spiteful) and respond with anger, blame, punishment, aggression, and /or avoidance (e.g., Reeder, 1993). Social neuroscience research also provides support for the connection between perceiving intentionality and engaging in prosocial behavior. For example, research in humans and non-human primates suggest that the premotor and posterior parietal cortex regions of the brain tend to be activated when engaging in goal-directed action and when perceiving others engaging in goal-directed action (Decety & Jackson, 2004). Thus, it is argued that the capacity to perceive intentionality represents is a primary pathway for understanding the connection between self and other, or first-and third-person perspectives, which facilitates empathy, perspective- taking, and other social sharing processes (Decety & Ickes, 2009; Decety & Jackson, 2006), In addition to understanding intentionality, RF is also suggested to help individuals regulate automatic responses to emotions via the capacity to separate internal experiences from 7 external reality (i.e., to take a third-person perspective to the self) (Fonagy et al., 1998). This means that individuals with high RF do not only understand the mind as intentional, but also as subjective. The ability to distinguish between these internal experience and external reality is argued to have important implications for emotion regulation throughout the lifespan. For example, as individuals understand their internal experiences as related, but not equivalent to, external experiences, they are able to separate from or modify their perceptions form the immediate emotional reactions they experience (Jurist, 2005). This is suggested to promote individuals’ understanding of emotional experiences as manageable, and likewise, increase their sense of control and mastery in responding to emotions (Slade, 1999). At the current time, research has not directly examined the association between RF and the ability, or tendency, to separate internal and external experiences. However, basic cognitive theories suggest that strategies such as reframing—which inherently requires an individual to re- evaluate their immediate emotional responses to situations—are important mechanisms used in emotion regulation (Gross, 2014). Further, a growing body of research suggests that psychological distance from emotional experiences facilitates emotion regulation across a variety of contexts. Such research operationalizes the psychological distance as the use of distanced (referring to the self as “you”) versus immersed (referring to the self as “I”) self-talk when processing emotional experiences. Findings from this research suggest that distanced self-talk reduced anxiety (Kross et al., 2014) and physiological reactivity (Streamer, Seery, Kondrak, Lamarche, & Saltsman, 2017) when preparing for an anxiety-eliciting speech task. Additionally, distanced self-talk was associated with individuals perceiving themselves as competent in their ability to cope with the stress of the speech-task (Kross et al., 2014; Streamer et al., 2017). This research has been further extended to show that distanced self-talk also lower individuals’ 8 emotional reactivity to and promotes their ability to make new meaning of negative experiences in their daily life (Orvell et al., 2017). Together, the abilities to understand self and other as intentional agents as well as the ability to delineate and appreciate the distinct, yet overlapping, aspects of internal and external experiences, promotes individual’s capacities to inhibit automatic responses to behavior. In particular, these features may give rise to the inhibitory functions of RF via the capacity to attend to the other’s perspective and reduce the reliance on the prepotent, unmediated perspective of the self in determining behavior, similar to how cognitive neuroscientific models have described the role of empathy in social-cognitive decision making (see Decety & Jackson, 2004). RF is specifically argued to promote the inhibition of automatic responses that are associated with negative psychological and social consequences (e.g., anxious rumination, autonomic arousal, social withdrawal, aggression), and facilitate the replacement of these responses with more adaptive and prosocial behaviors (e.g., empathy, communication, curiosity). To date, there is relatively little research directly examining the association between RF and the inhibition of automatic responses to emotions. One study that examined aggression in adolescence found that RF moderated the role between psychopathology and aggression, such that psychopathology was significantly associated with aggression in adolescents with low RF, but not in those with high RF (Taubner et al., 2013). Additionally, some recent research on parental RF in young mothers found that RF was positively correlated with domain-general inhibitory control (Hakansson et al., 2018; Rutherford et al., 2016). However, both of these studies utilized relatively small samples of mothers (N=43; N=50, respectively), including one which was composed entirely of mothers who met criteria for a substance-use disorder 9 (Hakansson et al., 2018), which may limit the generalizability of these findings to the broader population. However, there is a much larger research base examining the association between RF and psychopathology, which may be extrapolated to support the association between inhibitory functions in RF. For example, diminished RF has been empirically linked to a wide variety of adult psychiatric disorders, including personality disorders (Fisher-Kern et al., 2010; Fonagy et al, 1996; Levy et al., 2006), depression (Fisher-Kern et al., 2013), panic disorder (Rudden, Milrod, Target, Ackerman, & Graf, 2006), eating disorders (Fonagy et a., 1996; Ward et al., 2001) , psychosis (Macbeth, Gumley, Schwannauer, & Fisher, 2011), and autism (Taylor, Target, & Charman, 2008). Further, research on RF in heterogenous clinical populations has shown that RF is negatively associated with severity of psychopathology (Bouchard et al., 2008), pathological personality organization (Muller, Kaufhold, Overbeck, & Grabhorn,, 2006), and previous experiences of childhood trauma (Fonagy et al., 1996). A disparate but related body of literature suggests that deficits in inhibitory control represents a common factor underlying psychopathology generally (e.g., Berg et al., 2015; Carver et al., 2017; Caspi et al., 2014; Nigg, 2000; Wright, et al., 2014) and predicts transdiagnostic concerns, such as aggression, impulsivity, substance-use, and suicidality (Carver et al., 2017). Additionally, deficits in inhibitory control are associated with pathological personality traits, such as negative affectivity and emotional instability, detachment, disagreeableness, disinhibition and disorganization, as well as odd, or eccentric ways of thinking, behaving and perceiving the world (e.g., Caspi et al., 2014; Castellanos-Ryan et al., 2016; Kotov et al., 2017). Some research has more specifically suggested that the inability to inhibit self- referential thinking (i.e. a bias towards negative, overly self-focused meaning-making of 10 situations) is a common marker of emotional disorders such as depression and anxiety (e.g., Mennin & Fresco, 2013; Lemogne, Delaveau, Freton, Gionnet, & Fossati, 2012; Wagner, Schachtzabel, Peikert, & Bar, 2015). Thus, given that RF is broadly associated with psychopathology, and deficits in inhibitory control has recently been understood as a common factor underlying psychopathology, these findings suggest that RF and inhibitory control may be associated. Building from this, the psychosocial regulatory functions of RF can largely be described as the ability to provide top- down modulation to perception and behavior to facilitate maintenance of positive, stable relationships with others in order to foster security and efficacy in one’s ability to meet their own and others’ socially embedded needs. Development A majority of extant theory on RF focuses on the role of the early relational experiences in the development of RF. Much of this work is built from attachment theory (e.g., Diamond, 2004; Eagle, 1995; Fonagy, 1991; Slade 1999), as well as related literature identifying the importance of the caregiver-child relationship in the formation of the child’s internal working models (Main, 1991) or cognitive-affective schemas (Blatt & Blass, 1996). It is suggested that the infant-caregiver relationship facilitates RF development by providing a relational context in which a child can safely and securely explore and understand the mental states of self and other (Fonagy & Target, 2005; Gergely & Watson, 1996). The predominant developmental perspective on RF is derived from two interrelated theories: Social Biofeedback Theory (Gergely & Watson, 1996) and a theory of the development of psychic realities (Fonagy & Target, 1996). These theories suggest that parental RF facilitates secure infant-caretaker attachment and the development of the child’s RF via parenting 11 behaviors, such as affective marking (Gergely & Watson, 1996) and play (Fonagy & Target, 1996; 2000; Target & Fonagy, 1996). These behaviors help the child develop understanding of the boundaries between external (i.e., behavior) and internal (i.e., thoughts and emotions) experiences, as well as promote the child’s autonomy, safety, and control in exploring, managing and altering their internal experiences (e.g., Fonagy & Target, 1996; Slade, 1999). Social Biofeedback Theory and the theory of psychic realities largely pertains to the development of RF from birth until approximately age 4, when most children are able to differentiate the boundaries and contingencies between internal and external experiences, and self and other (Fonagy & Target, 1996). It is suggested that the child’s RF ability continues to blossom and become more sophisticated as they begin to step outside of the child-caregiver relationship and begin interacting with other social roles and systems (e.g,, school, peers). Theoretically, the development of RF is often discussed until age 8, when children’s RF capacities are posited to be more adult-like, given that they have developed an understanding of nuanced emotions like shame, guilt, and pride, the impact of emotions and intentions on behavior, and have the language skills to communicate internal states (de Rosnay, Harris, & Pons, 2008). However, Fonagy and colleagues (1991) also note that RF continues to develop throughout the lifespan and across different relational contexts, although this point has not received much elaboration in the literature to date. Not surprisingly, a majority of research on the development of RF has focused on the association of parenting behavior and parent’s RF on child-caregiver attachment. Parental RF has almost exclusively been measured through the coding of interviews, such as the Adult Attachment Interview (AAI; George, Kaplan, & Main, 1996), Parent Development Interview (PDI; Slade, Aber, Bresgi, Berger, & Kaplan, 2004), or the Working Model of the Child 12 Interview (WMCI; Benoit, Parker, & Zeanah, 1997), and is operationalized as the parent’s 1) awareness of the nature of mental states, 2) effort to understand mental states underlying behavior, 3) recognition of the appropriate developmental stages of mental states, and 4) the recognition of mental states in relation to the interviewer (Slade, 2005). There is notable empirical support for the fact that the children of mothers with higher RF tend to have more secure attachment to their mothers (Fonagy et al., 1991; Main, Kaplan & Cassidy, 1985; Slade, Grienenberger, Bernbach, Levy, & Locker, 2005). While there is less research examining the mechanisms through which parental RF promotes attachment, recent research has demonstrated that mothers with higher RF verbalize more mental-state attributions to their children when interacting (Rosenblum, McDonough, Sameroff, & Muzik, 2008) and are more behaviorally and neuronally responsive to infant cries (Rutherford, Booth, Crowley, & Mayes, 2016; Rutherford, Goldberg, Luyten, Bridgett, & Mayes, 2013). This may support the role of parental mentalization in providing children with a safe and secure environment. Additionally, correlational research has shown a positive relationship between mothers RF and working memory (Rutherford, Maupin, Landi, Potenza, & Mayes, 2017), which may correspond to the notion that RF is related to the ability to engage with emotions without becoming overwhelmed or shutting down in emotionally charged contexts. Related research also identifies the attachment style of the child to the caregiver as a consistent predictor of the child’s concurrent (Moss, Parent & Gosselin, 1995) and future RF ability (Blair, 1995; Fonagy, Redfern, & Charman, 1997), such that children with more secure attachment tend to have better RF. However, as was noted previously, research on the development of RF has largely not extended beyond early to late-childhood. Only a handful of studies have examined RF in adolescence, and only one of these studies employed longitudinal methods. Cross-sectional 13 research on RF in adolescence has shown concurrent associations between low levels of RF and borderline personality problems (Fossati, Feeney, Maffei, & Borroni, 2014; Ha, Sharp, Ensink, Fonagy, & Cirino, 2013), non-suicidal self-injury (Badoud et al., 2015), aggression and antisocial behaviors (Guarino & Vismara, 2012; Taubner et al., 2013), depression (Fischer-Kern et al., 2008), and eating disorders (Jewell et al., 2016; Rothschild-Yakar, Waniel, & Stein, 2013). In the single longitudinal study on RF during adolescence, Borelli and colleagues (2019) found that higher RF ability in adolescents (M=14.6 years old, SD=3.5 years) predicted lower levels of anxiety and depression and increased experiences of positivity, self-control, vitality, and general well-being eight years later. While such research does not provide much insight into how RF development may be affected by experiences in adolescence and beyond, this research does highlight the role that RF plays in facilitating psychosocial functioning from an early age into adulthood. Neurobiological Basis The neurobiological basis of RF has largely been extrapolated from research on: 1) caregiving and bonding, and 2) general social cognition. To my knowledge there is no current research that explicitly examines the structural and functional neuroanatomy of RF. With regard to care-giving and bonding, a majority of theory has focused on the role of the mesocorticolimbic dopaminergic reward circuit and hypothalamic-midbrain-limbic-paralimbic-cortical circuits (Fonagy & Bateman, 2008; Fonagy, Luyten, & Strathearn 2011; Rutherford et al. 2011). These systems are mediated by neurotransmitters, including dopamine, oxytocin, and vasopresson, which are frequently associated with affiliative, bonding, and caregiving behavior (Insel and Young 2001; Neumann 2008). Extant theory regarding the neurobiological basis of RF has particularly focused on the role of oxytocin in these systems, suggesting that oxytocin is 14 involved in mentalizing in close attachment relationships (Feldman et al., 2007; Strathearn, Fonagy, Amico, & Montague, 2009), care-giving behavior (Bartels & Zeki, 2004; Champagne, Diorio, Sharma, & Meaney, 2001), and regulation of behavioral and neuroendocrininological stress responses (Neumann, 2008). Interestingly, oxytocin has also been shown to foster explorative behavior (Insel and Young, 2001; Neumann, 2008), which may highlight that individuals with greater RF capacities tend to also be more open, willing, and non-defensive in their orientation to understanding their own and other’s internal states (Slade, 2005). Basic research on neurobiological systems related to social cognition more generally points to the likely role of the medial prefrontal cortex (mPFC), posterior cingulate, precuneus, and tempo-parietal junction in facilitating RF (Luyten, Nijsssens, Fonagy & Mayes, 2017). It is argued that these systems underlie many of the controlled, metacognitive functions (i.e., response inhibition, abstract and semantic reasoning) associated with RF (Lieberman, 2007; Satpute & Lieberman, 2006; Uddin et al., 2007). Building from dual-process models of social-cognitive processing (e.g., Kahneman, 2011; Meltcalfe & Mischel, 1999; Nigg, 2000; Strack & Deustch, 2004), it is argued that these systems compete with neurobiological systems related to automatic processing, such as the amygdala, basal ganglia, inferior frontal gyrus, inferior parietal lobe, anterior insula, and anterior cingulate cortex in determining behavior (Fonagy & Luyten, 2009; Van Overwalle & Baetens, 2009). Dual-process theories often argue that automatic social-cognitive processing is based on associative learning that is built slowly, over time, and based on a large sample of experiences, which make this system most effective for determining behavior based on average or typical schemas of the environment (Smith & DeCoster, 2000). Given this, the automatic system is often viewed as helping to sustain homeostasis (Damasio, 1994;1998; Shore, 1994; 2003) as well as 15 allowing for the relational-system to respond expeditiously, non-effortfully, and unconsciously to the environment in situations of stress or high-arousal. Behaviors expressed due to automatic processing are often based on associative learning (e.g., Otto, Gershman, Markman, & Daw, 2013; Walsh & Anderson, 2014), habits (e.g., Ouellette & Wood, 1998) or automatic responses to emotions (e.g., Metcalfe & Mischel, 1999). Conversely, when the controlled system is engaged in processing (e.g., RF), behavior is determined by the ability to consciously apply symbolically represented rules or mental models to situations (Strack & Deutsch, 2004). Unlike associative structures, these semantic or rule-based structures are suggested to be able to be formed rapidly, potentially after a single experience, making this system effective for determining behavior based on idiosyncratic or individuating information about a context (Smith & DeCoster, 2000). Importantly, while both automatic and controlled processes are suggested to be advantageous in different contexts (Block & Block, 1980; Otto et al., 2013), the dual processing perspective argues that it is the ability to balance and appropriately activate automatic and controlled processes that connotes adaptive psychosocial functioning (Strack & Deustch, 2004). In particular, it is suggested that an overreliance on automatic processes accounts for many of the externalizing, internalizing, and thought disorder symptoms associated with psychopathology (Carver, Johnson, & Timpano, 2017). The tendency to rely on automatic processes is suggested to be driven by a high reactivity to emotions and/or low-threshold for threat activation, leading to an up-regulation of the autonomic nervous system and tendency to engage in “fight or flight” behaviors (i.e., avoidance, aggression) in response to the social environment (Bateman & Fonagy, 2008). Given the conceptual linkage between RF and the attachment system, current theory suggests that impairments in RF constitute a proclivity towards automatic responses 16 specifically in response to attachment-related threats (e.g., others seeking distance from the self; Luyten & Fonagy, 2017). These types of behaviors are largely seen as socially maladaptive, and often overreactive and/or counterproductive in helping individuals meet their own and others’ needs in relationships. The hypothesized connection of RF to neural systems associated with controlled, metacognitive functions also has important implications for the development of RF. That is, while many of the systems associated with automatic social processing begin developing during the first or second year of life (Baillargeon, Scott, & He 2010; Kovacs, Teglas, & Endress 2010), systems associated with controlled reflective functioning begin to develop closer to age four (Carpendale & Lewis, 2006), and are more closely tied to the development of other cognitive capacities such as language (Beeghly & Cicchetti 1994) and executive functions (Fonagy & Luyten, 2009), which continue to develop throughout adolescence and into early adulthood and are more reliant of social environmental influences (i.e., early learning, education, etc.). This suggests that the development of controlled social-cognitive processing occurs over a longer developmental trajectory than the automatic system, and that the development of RF may be yoked to development of the cognitive architecture required for domain-general controlled, reflective (e.g., semantic reasoning) processes. While there is, to my knowledge, no empirical examination of the overlapping development of RF and cognitive processes, such as EF, related research indicates that the development of EF is robustly related to the development of ToM in early childhood (e.g., Carlson, Moses, & Breton, 2002; Carlson, Moses, & Claxton, 2004; Hughes, 1998; Perner, Lang, & Kloo, 2002; Sabbagh, Xu, Carlson, Moses, & Lee, 2006). Additionally, EF is empirically associated with ToM and the expression of complex social 17 understanding across middle-childhood and adolescence (Bock, Gallaway, Hund, 2015; Charman, Carroll, & Sturge, 2001; Perner, Kain, & Barchfeld, 2002) Executive-Functioning The concept of executive functioning (EF) was initially borne out of clinical neuropsychological research examining the association between frontal lobe impairment and difficulties with self-control and regulation (e.g., Duncan, 1986; Luria, 1966; Shallice & Burgess, 1991). While a number of definitions have been given for EF since its conception (see Barkley, 2012), contemporary definitions suggest that EF refers to top-down cognitive functions that facilitate goal directed-behavior (Blair et al., 2016; Friedman & Miyake, 2017; Miyake et al., 2000), often by imposing internally guided rules to behavior (Verbruggrun et al., 2014). Further, definitions of EF also generally include capacities for complex cognition, or the ability to hold and manipulate two or more objects in mind simultaneously (Barkley, 1997; Diamond, 2013). While there has been some debate within the EF literature regarding the subcomponents of EF, it is frequently argued that working memory, response inhibition, and set/task shifting represent three related but distinct factors underlying EF (Friedman & Miyake, 2004; Fuster, 1997; Miyake et al., 2000). A recent review of the EF literature by Diamond (2013) argues that these factors can be organized hierarchically and developmentally; suggesting that working memory and response inhibition are generally seen as lowest-level of EF, as they often develop earlier in life and are foundational for more complex EF tasks. The emergence of working memory and response inhibition is suggested to give rise to intermediate level EF skills such as, cognitive-flexibility or set-shifting. Together, these lower- and intermediate-level capacities are 18 suggested to combine later in development and facilitate the usage of high-level EF, which includes reasoning, problem solving, and planning. While much of theory and research on EF has focused on the top-down processing of “cool” stimuli (e.g., information with minimal incentive or emotional intensity) (See Barrett, Tugade, Engle, 2004 for a review), there is a developing body of literature examining the role of EF in the processing of “hot”—or emotionally laden—stimuli (e.g., Barkley, 2012; Casey, 2015; Gross, 2014; Zelazo & Cunnigham, 2007). This work has subsequently been used to inform theories regarding the connection between EF and emotion regulation (e.g., Ochsner & Gross, 2005). Related research has suggested that EF is associated with several adaptive emotion regulation strategies, such as proactive situational strategies (e.g., choosing situations that favor goal attainment) (Duckworth, Gendeler, & Gross, 2016), attentional-deployment (e.g., Carlson & Beck, 2009), cognitive reappraisal (Fujita, 2011; Kross, Ayduk, & Mischel, 2005; Trope & Liberman, 2010), and response modulation (Carlson, Zelazo, & Faja, 2013; Diamond, 2013). Given the role of EF in emotion regulation, as well as its connection with the prefrontal cortex, it has recently been suggested that EF is associated with RF (e.g., Hakansson et al., 2018; Rutherford et al., 2016). This work has provided an initial empirical link between working memory and inhibitory control abilities and RF in two small samples of mothers. The connection between RF and EF makes conceptual sense as RF often requires the ability to hold multiple representations of self and other in mind simultaneously in order to simulate psychosocial causality (working memory), as well as inhibit prepotent, automatic, and/or overly-self- referential responses to emotions evoked in social situations in favor of maintaining goal- directed actions (inhibitory control). 19 Working Memory Working memory is commonly described as the capacity to hold and manipulate multiple units of information simultaneously in mind (Baddeley, 2003), including the ability to maintain goal-relevant information processing despite the presence of competing information or distraction (e.g., Conway et al., 2005). Although a majority of research on working memory has focused on the cognitive processing of information independent of emotions (i.e., cold cognition) (e.g., Barrett et al., 2004), such as the role of working memory in reading comprehension or logical reasoning, more recent research has begun to examine the relationship between working memory and emotional processing (i.e., hot cognition) (e.g., Barkus, 2020; Hofmann, Schmeichel, & Baddeley, 2012). From a theoretical perspective, working memory has been suggested to aid in emotion regulation in several ways. Foremost, working memory is posited to promote the ability to maintain active representations of one’s self-regulatory goals (Conway et al., 2005). Without the capacity to maintain these goals in mind, self-regulation is directionless and likely to fail (e.g., Baumeister & Heatherton, 1996). Similar to this, working memory is suggested to aid in providing top-down control of attention toward goal relevant information and away from attention grabbing stimuli, which in turn, helps to buffer from interference from more proximal or attention-grabbing demands from the environment. This may allow individuals to use working memory capacity to suppress ruminative or intrusive thoughts. Extant research has supported the role of working memory in emotion regulation. For example, working memory is positively associated with performance in thought suppression tasks (Brewin & Beaton, 2002; Brewin & Smart, 2005), as well as less mind-wandering during challenging tasks in daily life (Kane et al., 2007). Working memory has also been shown to aid 20 in the suppression of emotionally laden visual information, such as emotional faces (Schmeichel, Volokov, & Demaree, 2008), and is associated with lower reactivity to negative emotional states (Pe, Raes, & Kuppens, 2013). Research has also implicated the role of working memory in down-regulating affective and automatic behavioral responses. This includes promoting abilities for cognitive reappraisal of emotions (Gan, Yang, Chen, Zhang, & Yang, 2017; Hendricks & Buchanan, 2016; Riediger et al., 2010; Schmeichel et al, 2008; Schemichel & Demaree, 2010) and applying internalized rules to behavioral reactions, such as suppressing anger or aggression upon provocation (Hofmann et al., 2008). One study by Schmeichel and Demaree (2010) indicated that individuals with higher working memory also tend to engage in cognitive reappraisal and express less negative affect spontaneously (i.e., without instruction) in response to negative feedback. Given that psychopathology is often marked by deficits in emotion regulation (see Aldao & Nolen-Hoeksema, 2010 for a meta-analytic review), a related body of research has also examined the association between working memory and mental disorders. Working memory deficits have been associated empirically with a wide-range of disorders, including mood disorders (e.g., Roca et al, 2015; Yuksel et al., 2018), anxiety disorders (e.g., Moran, 2016; Waechter et al., 2018), psychotic disorders (e.g., Hou et al., 2016; Pflueger et al., 2018) substance use (e.g., Verdejo-Garcia, 2016), eating disorders (e.g., Reville, O’Conner, & Frampton, 2016), and ADHD (e.g., Alderson, Kasper, Hudec, & Patros, 2013; Pievsky & McGrath, 2018). While more research is needed to understand how deficits in working memory can manifest in such broad symptom clusters, research on the transdiagnostic effects of working memory training as an intervention on psychopathology suggest that working memory increases 21 activation of the prefrontal cortex, allowing individuals to maintain goal-directed behavior and engage in less effortful and spontaneous emotion regulation (see Barkas, 2020 for a review). Research on the development of working memory suggests that the ability to hold multiple pieces of information in mind develops fairly early, as even infants (9 to 12 months old) and young children can hold one or two things in mind for an extended period of time (Diamond, 1995; Nelson et al., 2012). However, ability to manipulate objects in mind tends to develop more slowly through adolescence (e.g., Anderson, Anderson, Northam, Jacobs, & Catroppa, 2001; Cowen et al., 2011; Lucianna et al., 2005). In particular, it is suggested that some of the more complex working memory tasks, such as the ability to maintain and manipulate spatial information and the ability to develop mnemonic strategies to organize information in mind generally reach peak development around 16 to 17 years of age (Diamond, 2013). This development is argued to be tied to the synaptic pruning and myelination of the frontal lobe during adolescence (McGivern et al., 2002). Meta-analytic studies of aging and working memory suggest that individuals start to decline in their working memory around age 30, and that this decline is relatively linear (e.g., Bopp & Verhaeghen, 2005; Verhaeghen & Salthouse, 1997). However, subsequent research has suggested that age is particularly related to declines in individual’s ability to update or manipulate information in working memory (Verhaeghen & Zhang, 2013), whereas simple storage tasks (i.e., maintaining a list in working memory) remain relatively intact as individuals grow older (Bopp & Verhaeghen, 2005) Inhibitory Control Inhibitory control refers to one’s ability to inhibit prepotent or automatic responses to stimuli, in service of goal attainment. This often includes being able to control one’s attention, behavior, thoughts, and/or emotions, to override impulses, habits or conditioned responses that 22 would be inappropriate or maladaptive in one’s current context (Diamond, 2013; Rothbart & Posner, 1985). As stated earlier, there is a large body of literature supporting the role of inhibitory control in emotion regulation broadly (see Ochsner & Gross, 2005 for a review), as well as related literature arguing that impairments in inhibitory control is a fundamental aspect of psychopathological functioning (e.g., Berg et al, 2015; Carver et al., 2017; Caspi et al., 2014; Nigg, 2000; Wright et al., 2014). While working memory is also associated with a degree of response inhibition, as discussed in the previous section, working memory is often argued to be a passive means of inhibiting automatic responding, whereas inhibitory control is assumed to be more active (e.g., Hofmann et al., 2012). That is, working memory facilitates inhibition of prepotent responding via sustained attention and activation of goal-relevant information which leads to selective advantage to attending to such information over competing, goal-irrelevant information. Conversely, inhibitory control is often deemed as active inhibition as its leads to the suppression of automatic responses via the explicit, volitional, and effortful application of rule-based knowledge (e.g., do not do X) to behavior. Research has largely supported the role of inhibitory control in emotion regulation. For example, there is consistent evidence that rumination—which is posited to reflect difficulties inhibiting, repetitive, perseverative thinking patterns—is negatively associated with inhibitory control (see Yang, Cao, Shields Teng, & Liu, 2017 for a meta-analysis). Additionally, inhibitory control has been repeatedly shown to be positively related to reappraisal (e.g., Lantrip et al., 2016; McRae et al., 2012; Vanderhaselt, Baeken, Van Schuerbeek, Luypaert, & De Raedt, 2013). Given that deficits in reappraisal are found in a wide range of psychopathology (e.g., Aldao & Nolen-Hoeksema, 2010), these results may further highlight the mechanistic role of inhibitory 23 control in adaptive psychosocial functioning. More recent meta-analytic work by Schweizer and colleagues (2018) has also highlighted the role of inhibitory control in psychopathology, suggesting that deficits in inhibitory control differentiate between psychologically healthy individuals and those with psychopathology, including depression anxiety, schizophrenia, and eating disorders. Research on the development of inhibitory control suggests that children generally have difficulty exercising inhibitory control (see Diamond, 2013 for a review; Carlson, 2005), and that they tend to perform much worse on many inhibitory control tasks, compared to adults, until about age 9 (Davidson, Amso, Anderson, & Diamond, 2006). However, there is also evidence that the changes in prefrontal cortex structure that occur in adolescence represents a critical time in the maturation of inhibitory control abilities (Diamond, 2013; Ordaz, Foran, Velanova, & Luna, 2013), and individual’s performance on inhibitory control tasks tends to improve linearly with age, until approximately age 18-20 (Leon-Carrion, Garcia-Orza, & Perez-Santamaria, 2004; Luna, Garver, Urban, Lazar, & Sweeney, 2004; Cohen-Gilbert & Thomas, 2013). Much like working memory, lifespan research has indicated that inhibitory control tends to remain relatively stable between the ages of 20-30 years old, however, as individuals move into middle and later adulthood, their inhibitory control tends to diminish linearly with age (Borella, Carretti, & De Beni, 2008). It is suggested that these deficits specifically relate to a decrease of efficacy in suppressing interference (e.g., Hasher & Zacks, 1988), which has led some to argue that deficits in suppressing interference may also account for the deficits in complex working memory tasks that are observed in older age (Borella, Carretti, & De Beni, 2008). 24 Connecting EF and RF As noted earlier, one of the most salient gaps in RF theory and research is the lack of work linking RF with basic cognitive processes. Converging with the perspectives of recent RF literature (e.g., Hakansson et al., 2018; Rutherford et al., 2016), the overlap between function and neurobiological structures associated with RF and EF suggest that these two constructs may be closely related. In particular, given that EF is often conceptualized as generalized cognitive abilities, it is likely that RF describes the use of EF to attend to, understand, and maintain in mind representations of self and others’ internal states, and direct behavior toward goal- fulfillment within interpersonal contexts. Based on this definition of RF, EF is necessary but not sufficient on its own, for defining RF. Instead, it is posited that the quality of one’s representations of self and other also plays a moderating role in the relationship between EF and RF. That is, RF requires not only the basic cognitive architecture to provide top-down modulation to behavior, but also relatively stable, coherent, accurate, and complex understanding of self and others, from which to base decision-making on. It may also be the case that specific domains of EF are more or related to specific aspects of RF. That is, while this paper has mostly described the ways that RF and EF broadly relate, working memory and inhibitory control may uniquely related to specific aspects of RF. A recent series of studies by Good and colleagues (in preparation) examined the conjoint factor-structure of existing self-report and performance-based measures of RF and identified two relatively distinct parameters that explain approximately 60% of the shared variance across these measures: 1) Mentalization ability, and 2) Mentalization orientation. Mentalization ability refers to one’s ability to identify, process and express internal states of self and other accurately and adaptively, as well as the ability to use executive control to engage in self- and other-emotion-regulation. 25 Mentalization orientation refers to one’s values and beliefs about the utility of engaging in mentalization. Orientation also reflects the motivation to mentalize self and other, as well as the tendency to engage in such processing regardless one’s ability. Findings from Good and colleagues (in preparation) suggest that mentalization ability and orientation have a marginal to moderate positive association with one another, but relate somewhat uniquely to constructs commonly associated with RF. In particular, mentalization ability tends to more specifically relate to lower personal distress, lower number of interpersonal problems, secure adult attachment (i.e., low attachment anxiety and avoidance), better self-regulation, less severity and quantity of pathological personality traits, higher extraversion and conscientiousness, and lower neuroticism. Conversely, mentalization orientation tends to more specifically relate to low attachment avoidance, a warm-non-domineering interpersonal style, better ability to appraise and regulate other’s emotions, higher empathy, less antagonistic pathological personality traits, and higher agreeableness and openness to experience. While there is currently no research that examines the association between domains of EF and parameters of RF, both working memory and inhibitory control have clear theoretical ties to mentalization ability. Particularly as mentalization ability is definitionally associated with the use of executive control to engage in emotion regulation, it is likely that working memory and inhibitory control provide the basic cognitive architecture to perform such tasks. For example, identifying and processing emotional experiences is considered important aspects of emotion regulation (Jurist, 2005), and working memory may facilitate these aspects of regulation via an individual’s ability to simultaneously hold in mind and compare multiple representations of one’s own and other’s emotions (Decety & Svetlova, 2012). While there is no current research directly exploring this hypothesis, some related research suggests that working memory is more 26 related to the accuracy of implicitly and explicitly recognizing emotions from the facial expressions of others (Mathersul et al., 2009). Further, research on individuals with depression suggests that such individuals have difficulty maintaining positive content about the self in working memory, and that these effects may be driven by negative mood (Levens & Gotlib, 2015). These findings may suggest that impairment in working memory functioning may affect the processing of social-cognitive information, via difficulties suppressing mood congruent attributions of the self. The adaptive expression of one’s emotions is also considered an important aspect of emotion regulation and inhibitory control is often considered an essential component of this ability (Jurist, 2005). In particular, one’s ability to inhibit automatic behavior is likely to promote their ability to act in a goal-directed and reflective manner, regardless of their capacity for working memory. This premise is largely supported by research connecting inhibitory control with personality traits associated with automatic, and often maladaptive, responses to emotions such as impulsivity (e.g., Enticott, Ogloff, & Bradshaw, 2006; Logan, Schachar, & Tannock, 1997), disinhibition (e.g., Enticott et al., 2006), aggression (Pawliczek et al., 2013), substance use (e.g., Ivanov, Shulz, London, & Newcorn, 2008; Lawrence, Luty, Bogdan, Sahakian, & Clark, 2009), and risk-taking behaviors (e.g., Lawrence et al., 2009). Additionally, research on executive functioning and empathy suggest that inhibitory control is a key aspect of regulating automatic responses to emotional contagion when attending to others’ minds (de Waal & Preston, 2017; Decety et al, 2016). While the theoretical association between mentalization orientation and working memory and inhibitory control is less clear, it may be that their association is explained by the overlap between mentalization orientation and ability. Holding mentalization ability constant, it is not 27 apparent how one’s values and beliefs about the utility of understanding self and others relates to EF domains. However, to the degree that one’s mentalization ability is related to increased perceived self-efficacy about their ability to mentalize and their beliefs about the utility of doing so, it is likely that they will be more orientated towards mentalization. With regard to working memory, we can imagine that if one does not have the ability to separate, hold, and compare multiple perspectives in mind simultaneously, they will be unlikely to be oriented to considering the minds of self and other. Similarly, with regard to inhibitory control, we can imagine that if one is unable to regulate emotions (primary or vicarious) they will be unlikely to have an approach orientation towards their own or others’ emotions. It is also important to consider how RF and EF may be connected throughout development. Given the perspective that the development of EF is necessary but not sufficient for the development of RF, I argue that the development of RF is largely yoked to the development of EF, but continues after the EF system is fully developed. Thus, from the current perspective, I argue that individuals have the basic cognitive architecture needed for RF (inhibition of automatic responses via working memory and inhibitory control) by the end of adolescence, but continue to grow in their capacity to engage adaptively in RF as they develop a more rich understanding of self and other through life experiences extending into middle adulthood. Many of the life experiences suggested to be important for forming these representations occur somewhat normatively with age (e.g., college, romantic relationships, beginning a career, starting a family). However, experiences from earlier in life (i.e., childhood maltreatment) or proximal individual differences (i.e., adult attachment difficulties) may negatively impact one’s ability to develop adaptive representations of self and other during this time and impair RF development regardless of EF. When considering this perspective in light of 28 the parameters of RF discussed earlier, it is likely the case that the development of EF is a notable driver for the development of mentalization ability. However, mentalization orientation may be more driven by experiences in close relationships and the social environment in which one develops their values and beliefs about emotions. Moderators of the EF and RF Relationship Age Current RF theory discusses age as a proxy for the normative psychosocial and neurological developments that occur from infancy into childhood and give rise to RF. This includes normative development of the cognitive architecture required to mentally represent self and other, as well as inhibit automatic responses in order to reflect and determine behavior based on those representations. However, given the perspective that RF continues to develop throughout the lifespan (Fonagy et al., 1991), age is also expected to correspond to RF development from adolescence and beyond. Social and personality research suggests that age is often associated with normative patterns of engaging and investing in social relationships, which have implications for one’s understanding of self and other (e.g., Lodi-Smith & Roberts, 2007). In particular, research on social investment— an individual’s commitment to adult social roles (Roberts & Wood, 2006)— suggests that as individuals begin to invest time and energy into social roles, they begin to solidify their identity around those roles. Work on social investment has largely focused on the domains of work, family, and community, and suggests that individuals tend to evaluate their own identity based on their performance in these areas of functioning (see Lodi-Smith & Roberts, 2007 for a meta-analysis). Converging with Erickson’s model of psychosocial development (1968), theory and research on social investment suggests that adulthood (ages 25- 29 65) is a prominent period of time when individual’s identity is tied to their investment in social roles, whether committing to marital roles during young adulthood (Erikson’s intimacy versus isolation phase) or committing to roles as parents and active community members in middle- adulthood (Erickson’s generativity versus stagnation phase). This period of role investment and self-concept clarity in adulthood is juxtaposed to late adolescence and emerging adulthood, which is often marked by the search for one’s identity and experimentation in a wide variety of novel social roles (e.g., Giordano, 2003). Thus, despite the fully mature EF capacities of late adolescents and emerging adults, the lack of life experience and self-concept clarity often experienced during this stage of life is suggested to limit one’s RF capacity. Whereas the solidification of one’s identity around social roles in middle to late adulthood is suggested to bolster RF capacities and may even help to buffer against impairment in RF that may be driven by age-related decline in EF. Lifespan research on the development of empathy and perspective taking may also support the notion that RF continues to develop into middle or late-adulthood. Theory and research has generally supported that empathy follows an inverse u-shape trajectory across the lifespan, in which individuals in middle adulthood express greater empathy than younger or older individuals (e.g., McAdams & Olson, 2010; O’Brien, Konrath, Gruhm, & Hagen, 2013; Labouvie-Vief, 2009). This finding has often been interpreted similarly to my perspective on RF development in this paper; that empathetic capacities increase during early life due to normative development in social-cognitive and metacognitive functions, and continue showing subtle increase throughout middle adulthood due to the accumulation of life experiences and consolidation of self-concept (Labouvie-Vief, Gruhn, & Struder, 2010). 30 When considering the mentalization ability and orientation parameters of RF, it is expected that these two aspects of RF develop with age via somewhat independent pathways. As mentioned previously mentalization ability is likely to increase with age in correspondence with the development of the EF system and overall emotion regulation capacities. However, it is likely that mentalization orientation develops in relation to many of the social-emotional developments discussed in this section. That is, it is expected that individual’s experiences in relationships as well as the solidification of one’s identity and social roles is likely to drive one’s orientation to understanding self and other. Childhood Maltreatment Extant theory suggests that experiences of childhood maltreatment (i.e., abuse and/or neglect) are particularly impairing in one’s ability to develop RF (Fonagy et al., 1996; Fonagy & Bateman, 2008; Fonagy & Luyten, 2009). This hypothesis has been supported empirically by research linking retrospective reports of child maltreatment with deficits in adult RF (Ensink, Berthelot, Bernazzani, Normandin, & Fonagy, 2014; Fonagy et al., 1996). Additionally, related research has indicated the deleterious effects of childhood maltreatment on the development of ToM (Cicchetti, Rogosch, Maughan, Toth, & Bruce, 2003; Fonagy, Gergely, & Target, 2007; Pears & Fisher, 2005; Sharp & Fonagy, 2008) emotional understanding (Camras, Sachs-Alter, & Ribordy, 1996; Meins et al., 2002; Raikes & Thompson, 2006; Rogosch, Cicchetti, & Aber, 1995; Shipman & Zeman, 1999; Shipman, Zeman, Penza & Champion, 2000), and the ability to discriminate between emotions (Edwards, Shipman, & Brown, 2005; Pollak, Cicchetti, Hornung, & Reed, 2000). These deficits may partially manifest from deficits in maltreated children’s development of emotion related vocabulary (Beeghly & Cicchetti, 1994). 31 RF theory suggests that the association between experiences of childhood maltreatment and RF development is mediated by the attachment system (Fonagy et al., 1996; Fonagy & Bateman, 2008; Fonagy & Luyten, 2009). In particular, it is suggested that maltreatment is fundamentally incompatible with a caregiver’s mentalization of their child and reflects the care- giver’s inability and/or unwillingness to establish the type of relationship in which the child can learn about their own and others’ minds (Allen, 2013; Fonagy & Luyten, 2009). While there are few studies which examine how relational dynamics in maltreatment contexts lead to deficits in self-other understanding, Edwards and colleagues (2005) have shown that maltreating parents tend to engage their children less often in discussions about emotions, suggesting that a lack of verbal communications about mental states may in part impair this development. Related research by Shipman and Zeman (2001) suggests that maltreating parents also tend to have difficulty understanding their children’s affective expressions. This may suggest that maltreating parents may avoid discourse around emotions with their children due to the opaqueness of their children’s mind. From a theoretical vantage, maltreating parents may further be discouraged from mentalizing their child given that this would come with the psychological impact of recognizing the suffering they inflict (Allen, Fonagy, & Bateman, 2008). In such circumstances, there is also little incentive or scaffolding for the child to imagine their caregiver’s mind either, as this would mean confronting their caregiver’s malevolent or distorted representations of themselves, and likely undermine their attachment needs (Allen, 2013; Fonagy et al., 2002). There is also empirical evidence that maltreated children, particularly those who live with an abusive or neglectful attachment figure, are likely to see attachment figures as a source of fear rather than security, leading to chronic activation of the child’s attachment system and preoccupation with scanning the environment and parent’s facial expressions for possible threats 32 (Cicchetti & Toth, 2005; De Bellis, 2005). When considered in light of the dual-process perspective on RF, experiences of maltreatment during childhood may serve to “hardwire” children’s relational system to detect threat, and engage in automatic processes, rather than engaging in more reflective modes of processing (Fonagy & Luyten, 2009). This perspective converges with theories that suggest that experiences of trauma more broadly lead to the consolidation of associative fear networks (e.g., Foa, Riggs, Dancu & Rothbaum, 1993; Foa, Steketee, & Rothbaum, 1989) which have low activation thresholds, strong automatic behavioral responses, and may be easily activated by a wide variety of environmental cues (Park, Mills, & Edmondson, 2012). Given this, there is substantial reason to believe that experiences of childhood maltreatment have negative implications for the development of RF. Given that the current dissertation is concerned with the development of RF during adolescence into middle-adulthood, childhood maltreatment is viewed as a distal risk factor that has significant ramifications for the development of RF, over and above the individual’s development of domain general EF abilities. In this way, childhood maltreatment may significantly disrupt the child’s ability to develop RF later in life in two primary ways: 1) the lack of development of the mental representational structures needed to process the mental states of self and other (e.g., self-other representations, verbal/semantic understanding of emotions, etc.), and 2) a proclivity towards threat activation in one’s attachment system, leading to hyperactivation of automatic behavioral responses in relational contexts. Notably, these two types of disruptions connect the development of both mentalization ability and orientation parameters of RF. It is suggested that disruptions in the development of the regulatory capacities and mental structure required for mentalization will impair the development of mentalization ability. Additionally, the tendency for childhood 33 maltreatment to lead to negative representations of self and other and frustration of individual’s need fulfillment in close relationships will limit the degree to which individuals view understanding the mind of self and other as an effective means of self- and other-regulation. Adult Attachment Style Much like experiences of childhood maltreatment, extant theory (e.g., Fonagy, 1991; Fonagy & Bateman, 2008; Fonagy & Luyten, 2009) and research (e.g., Fonagy et al., 1996; Kelly, Slade, & Grienenberger, 2005; Slade et al., 2005) have consistently indicated an association between RF and adult attachment style. Attachment style is generally conceptualized as one’s systematic patterns of expectations, needs, emotions, emotion-regulation strategies, and behavior that result from activation of one’s attachment system (Bowlby, 1969), which is based on relational models of self and other (i.e., internal working models) emanating from early relational experiences (Fraley & Shaver, 2000). Adult attachment problems are frequently conceptualized along two dimensions, attachment-related 1) anxiety, and 2) avoidance (e.g., Brennen, Clark, & Shaver, 1998; Crowell, Fraley, & Shaver, 1999). Attachment anxiety and avoidance are considered to be the converse of attachment security, which includes one’s positive expectations about others’ availability to provide support in distressing situations; positive views of the self as lovable, valuable, competent, and deserving of others’ support; and confidence in seeking others’ support as an effective emotion regulation strategy (Bowlby, 1973; Shaver & Mikulincer, 2002). Attachment anxiety is related to an intense preoccupation with needing to be close, supported, or reassured by others, whereas attachment avoidance is associated with discomfort with closeness, self-disclosure, or expressing feelings, needs, and/or vulnerabilities with others. 34 Previous research has demonstrated that attachment styles often exhibit continuity across developmental phases (Hamilton, 2000; McConnell & Moss, 2011; Waters, Merrick, Treboux, Crowell, & Albersheim, 2000; Zhang & Labouvie-Vief, 2004). It is argued that as youth move into adolescence, and eventually adulthood, they become less dependent on parents and begin to transfer their attachment needs to relationships with peers and romantic partners (Allen, 2008). Given this, it is expected that there is a degree of overlap between experiences of childhood maltreatment and adult attachment style. Indeed, meta-analytic work by Baer and Martinez (2006) examined data from 80 studies and more than 100 samples and found that maltreated children have 80% greater odds of having insecure attachment than children in the comparison group. A subsequent meta-analysis by Cyr and colleagues (2010) indicated that maltreated children were less secure (d=2.10) and more disorganized (d=2.19) than children with other socioeconomic risk factors associated with the development of attachment related concerns (e.g., low income, single parent household, low parental education; d=.48 and .48, respectively). Further, adult attachment styles have been shown to mediate the relationship between experiences of childhood maltreatment and a wide range of maladaptive outcomes in adulthood, including personality disorders (Cohen et al., 2017; Riggs et al., 2007), depressive symptoms (Bifulco et al., 2006; Hankin, 2005), post-traumatic stress symptoms (Muller, Thornback, & Bedi, 2012), anxiety symptoms (Bifulco et al., 2006), and eating disorders (Tasca et al., 2013). Experiences of early maltreatment also heighten one’s risk of continuing to experience interpersonal trauma in adulthood (Cloitre et al., 1997; Widom, 1999), which may potentially reinforce their attachment insecurity. However, there is strong rationale that adult attachment style may be important to consider, over and above experiences of childhood maltreatment, when examining adolescents’ 35 and adults’ development of RF. Foremost, while attachment problems are more common among individuals with history of maltreatment than not, attachment classifications are not reliable or valid for screening for maltreatment (Granqvist et al., 2017). This is largely because there is not a one-to-one ratio between experiences of childhood maltreatment and future attachment problems. For example, Collishaw and colleagues (2007) approximate that 45% of children who experience abuse show social-emotional resilience in adulthood, suggesting that there are likely multiple mediating factors affecting the relationship between experiencing maltreatment and maladaptive outcomes later in life. Additionally, research has shown that interpersonal experiences later in life can shift attachment styles. For example, romantic breaks-up were associated with increases in attachment anxiety, whereas starting new relationships was associated with decreases in avoidance over a 4-year period in a community sample of middle- aged adults (Kirkpatrick & Hazan, 1994). Psychotherapy, which, when successful, involves developing a close, trusting, and productive relationship with one’s therapist (e.g., therapeutic alliance; see Fluckiger, Del Re, Wampold, & Horvarth, 2018 for meta-analysis), has also been shown to increase attachment security in adults (e.g., Travis, Bliwise, Binder, & Horne-Moyer, 2001). Additionally, Simpson and colleagues (2003) found that first time parents’ attachment styles were particularly likely to shift from pre-birth to post-birth when they experienced their partner as behaving incongruent to their initial attachment style (e.g., when an avoidant mother experienced herself as seeking and receiving support from her husband). These findings largely converge with the theoretical notion that attachment styles are likely to be stable when the social environment is stable, but can adapt when the environment changes (Bowlby, 1969; 1988). When considering the development of RF, it may also be important to examine the role of adult attachment style as attachment anxiety and avoidance have implications for the use of 36 automatic versus controlled social cognitive processes (e.g., Luyten & Fonagy, 2017). As was discussed earlier in this section, anxious and avoidant attachment styles are differentiated definitionally by their tendency to perceive and respond to threats to the attachment system. These distinctions have been supported and extended empirically. For example, research has found that attachment anxiety is often related to a tendency to ruminate on one’s distress/negative experiences, difficulty suppressing negative thoughts, and high emotional reactivity to cues of interpersonal separation (Birnbaum et al., 1997; Gillath, Bunge, Shaver, Wendelken, Mukulincer, 2005; Mikulincer & Florian, 1998; Skowron & Dendy, 2004). Given this, attachment anxiety has been conceptualized as hyperactivation of the attachment system, which is posited to lead overwhelm anxious individuals’ cognitive systems and reduce their ability to rely on cognitive resources to engage in adaptive, reflective coping strategies (Shaver & Mikulincer, 2002; 2007). Conversely, attachment avoidance has been associated with a tendency to avoid, detach from, or be dismissive of the emotional experiences of self and other (Birnbaum et al., 1997; Mikulincer & Florian, 1995). Thus, it is often conceptualized as deactivation of the attachment system, in which individuals often do not consciously experience or cognitively engage with their emotions, despite exhibiting physiological and behavioral arousal and agitation (Shaver & Mikulincer, 2002; Mikulincer & Shaver, 2007). Following from these distinctions, Fonagy and Luyten (Fonagy & Luyten, 2009; Luyten & Fonagy 2017) have argued that attachment styles differentially predict the contexts and frequency that individuals engage in automatic versus reflective emotion regulation strategies. Because attachment anxiety is associated with high emotional reactivity and a tendency to be overwhelmed by emotions, such individuals are argued to have a low threshold for relational stress before their behavior becomes driven automatic social cognitive processes (i..e, not RF). 37 Conversely, because attachment avoidance is associated with a dissociation from one’s relational and emotional world, such individuals may have a relatively high stress threshold before turning to automatic social cognitive processes However, given this dissociation, such individuals may struggle to understand their own and others internal affective world, thus preventing them from using RF to set and pursue meaningful, interpersonally embedded goals. Additionally, when avoidant individuals’ level of stress does lead to non-reflective functioning, they may not be able to engage RF later to understand how their behavior related to their stress. As such, whereas attachment anxiety leads to deficits in RF due to a heightened stress response, attachment avoidance leads to deficits in RF due to the opaqueness of one’s own and others’ internal states. These differential patterns of engaging in emotion regulation have been supported by research indicating that both anxious and avoidant attachment is related to lack of emotional awareness, however, attachment anxiety is specifically associated with a lack of emotion regulation strategies and difficulty with impulse control, and attachment avoidance is specifically associated with nonacceptance of emotional responses and a lack of emotional clarity (e.g., Mikulincer, Shaver, & Pereg, 2003; Morel & Papouchis, 2015). Given this perspective, while childhood trauma presents a distal risk factor for deficient RF development, adult attachment style represents a proximal individual difference in one’s self- other regulatory functioning which is likely to impact their RF ability. Thus, while these two constructs are likely related, they are likely to provide incremental predictive power of RF, regardless of level of EF. When considering the parameters of RF, adult attachment style is likely to be differentially related to mentalization ability and orientation. In particular, the regulatory components of mentalization ability are likely to relate to a secure adult attachment style, and 38 thus, negatively associated with both attachment anxiety and avoidance. Conversely, as mentalization orientation is understood to one’s motivation to engage, or emotionally connect with self and others, is likely to relate positively to attachment anxiety but negatively to attachment avoidance. However, given that mentalization orientation is seen as an adaptive construct, and attachment anxiety and avoidance are considered maladaptive, it is likely that the association between these constructs is somewhat marginal and largely encapsulates that these constructs include information one’s approach vs. avoidance of relationships. Current Study The current study aims to examine the association between RF and EF, as well as the moderating role of age, childhood maltreatment, and adult attachment style on this association. This includes an understanding to what degree parameters of RF (mentalization ability and orientation) are differentially associated with different domains of EF (working memory and inhibitory control). Given that previous research has only examined the association between RF and EF in small samples of mothers, the current study is novel in that it aims to examine these associations cross-sectionally in a large community sample of individuals ranging in age from adolescence into adulthood. This study is also novel in that previous research has not investigated how subcomponents of RF relate to EF. The current study is particularly interested in examining the association between RF, EF, and the proposed moderators from approximately ages 15-45 years old. This age range was selected for several reasons. Foremost, extant research on RF has primarily focused on young children or adults (e.g., parents or clinical populations), with very few studies examining RF in non-clinical populations that include late-adolescents and emerging adults. Thus, an additional novel aim of the current study is to examine the association between RF and EF during this time- 39 period. Further, this study chose to only include late-adolescents (rather than early adolescents or prepubescent children) due to the perspective that RF is likely to be yoked to the development of EF during childhood and early adolescence. In this way, this study does not aim to examine how the development of EF corresponds to the development of RF, but rather, how fully matured EF abilities intersect with life experiences (i.e., age; childhood maltreatment) and individual differences (i.e. adult attachment style) to predict RF. The current study has multiple aims and hypotheses. This includes exploratory aims regarding the measurement model of EF and potential associations between RF, EF, and other demographic characteristics not previously reviewed in this paper, as well as several predictive aims and hypotheses regarding the associations between RF, EF, age, childhood maltreatment, and adult attachment style. The specific aims and hypotheses of this study are detailed below: Exploratory Aims Aim 1: Determine the Latent Structure of EF. Past research examining latent variable models of EF suggests that working memory and inhibitory have overlapping and distinct variance (e.g., Miyake & Friedman, 2012). However, it is likely that the fit of latent EF models is affected by the sample and types of indicators used. Some previous research suggests that tasks related to working memory and inhibitory control load on to separate factors that covary with one another (correlated two-factor model; e.g., Miyake et al., 2000), whereas other research suggests that working memory and inhibitory control load onto general and specific EF factors (bifactor model; e.g., Friedman et al., 2011). To the degree that working memory and inhibitory control are highly overlapping in a given sample, it is also possible that a single general EF factor is the best captures the variance of working memory and inhibitory tasks (single-factor model). To this end, the current study aims to examine the absolute and comparative fit of a 40 correlated two-factor, bi-factor, and single-factor models of EF with the current data. The model that fits the data best will be used in all subsequent analyses. While there is no clear rationale to expect one of these models to fit the data better than the others, all hypotheses for this study are based on assumption that the correlated two-factor model will best fit the data. Aim 2: Examine the Association between Gender, SES, RF, and EF. While this study does not specifically discuss or make hypotheses about the association between gender and SES and RF or EF, it is possible that these variables may be associated. As such, this study also aims to explore these potential associations. To the degree that basic associations are found between gender and SES and RF and EF, gender and SES will also be examined as potential moderators of the association between RF parameters and EF factors. Predictive Aims & Hypotheses Aim 1: Examine the Association between RF and EF. Hypothesis 1a: EF will broadly be positively associated with RF. Hypothesis 1b: Working memory and inhibitory control will be specifically positively associated with mentalization ability. Hypothesis 1c: To the extent that working memory and inhibitory control are associated with mentalization orientation, this association will be explained by the covariance between mentalization orientation and ability. Aim 2: Examine the Association between Age, Childhood Maltreatment, and Adult Attachment Style and RF and EF. Hypothesis 2a: Age will be positively associated with mentalization ability and orientation 41 Hypothesis 2b: Age will be negatively associated with working memory and inhibitory control Hypothesis 2c. Childhood maltreatment will be negatively associated with mentalization ability and orientation. Hypothesis 2d: Childhood maltreatment will be negatively associated with working memory and inhibitory control. Hypothesis 2e: Anxious adult attachment style will be negatively associated with mentalization ability and positively associated to mentalization orientation. Hypothesis 2f: Avoidant adult attachment will be negatively associated with both mentalization ability and orientation. Hypothesis 2g: Anxious and avoidant attachment will not be related to working memory or inhibitory control. Aim 3. Examine the Moderating Role of Age, Childhood Maltreatment, and Adult Attachment Style on the Association between RF and EF. Hypothesis 3a: Age will moderate the association between RF and EF parameters such that the older an individual is, the less that RF and EF parameters will be associated. Hypothesis 3b. Childhood maltreatment will moderate the association between RF and EF parameters such that the more childhood maltreatment experiences, the less that RF and EF parameters will be associated. Hypothesis 3c: Attachment anxiety will moderate the association between RF and EF such that the more anxiously attached one is, the less that RF and EF parameters will be associated. 42 Hypothesis 3d: Attachment avoidance will moderate the association between RF and EF such that the more avoidantly attached one is, the less that RF and EF parameters will be associated. 43 METHOD Sample Four-hundred twenty-nine (N=429) participants were included in the final sample for this study. All participants were recruited via a Qualtrics market research panel and were required to be living in the United States, fluent in English, between the ages of 15-45 years old, and to have access to a computer with mouse, keyboard, and high-speed internet connection to complete the study. Consistent with these restrictions, participants ranged in age from 15-45 years old and the average age of the sample was 33.34 (SD=9.94). Approximately half of the sample identified as female (51.5%), with a majority of others identifying as male (47.3%). A small minority of participants identified as gender non-binary, transgender, or a gender identity that was not listed (1.1%). With regard to race, the sample was predominantly white (84.6%); 7.7% of participants identified as Black, 2.3% Asian, 2.1% Hispanic, 1.2% Indigenous/Native American/American Indian/Alaska Native, 0.7% Middle Eastern/North African, 0.7% multiracial, and 0.7% identified with a racial identity that was not listed. In terms of education level, most (91%) participants had at least completed high school. About a fifth of the sample completed some college (17.5%), 35.7% completed a college degree only, and 19.6% had completed a graduate degree. Participants’ current income growing up and at the current time was relatively evenly distributed across the income brackets assessed by this study. Descriptive statistics for participant demographic characteristics are presented in Table 1. Procedure All study procedures were conducted remotely, using Qualtrics.com’s online survey platform. Due to the types of tasks included in the study, participants were required to complete the study using a computer with mouse, keyboard, and reliable, high-speed internet connection. 44 Participants who attempted to complete the study using a smartphone or other mobile device were screened out of the study as part of the consent process. All participants provided consent to participate in the study. Participants were compensated $6 for completing the study, which was estimated to take approximately 30-45 minutes. Upon consenting to participate in the study, participants completed self-report questionnaires related to their demographics, RF, adult attachment style, and experiences of childhood maltreatment. Following completion of these questionnaires, participants were administered six EF tasks: three assessing working memory and three assessing inhibitory control. The ordering of EF tasks was partially counterbalanced across participants using a 6x6 Latin square design. Following the completion of the EF tasks, participants were debriefed and compensated for their participation. All study materials and procedures were IRB approved. Measures Demographics A brief demographic questionnaire was used to assess a range of participant characteristics, including age, race/ethnicity, gender, and socioeconomic status (SES). As part of the assessment of SES, participants were asked to report on their current level of education, current income, and subjective SES, as well as the education, income, and subjective SES of their caregivers while growing up. For the purposes of this study “while growing up” was defined as before turning 18 years old. Thus, participants who were 18 or younger only completed one report of their SES. Subjective SES was specifically assessed using the MacArthur Scale of Subjective Social Status (MacArthur SSS; Adler, Epel, Castellazzo, & Ickovics, 2000). The MacArthur SSS presents participants with an image of a ladder with 10 rungs as a metaphor for where individuals 45 stand in society, stating “At the top of the ladder are the people who are the best off—those who have the most money, most education and best jobs. At the bottom are the people who are worst off—those who have the least money, least education, and worst or no jobs”. Participants are then asked to select the rung the best represents where they perceive themselves standing in society. Given that this study did not make hypotheses about how RF and EF are associated with specific aspects of SES (e.g., income, education, subjective SES), principal components analyses were used to examine the degree to which these measures could be combined to create composite scores for participant’s SES. A separate principal components analysis was conducted for measures of SES while growing up and for current SES. Results from both analyses suggested that a single principal component factor captured a significant amount of variance in SES indicators and should be retained. In particular, 59.7% of the variance in SES indicators while growing up was accounted for by a single principal component, and 60.3% of the variance in current SES indicators was accounted for by a single principal component. In both analyses, all three indicators of SES had comparable, large factor loadings onto the principal component factor, providing further support that the principal component could be used as a composite for SES indicators (see Table 2). Given these results, factor scores for these principal components were used for all analyses examining the effects of SES in the current study. Reflective-Functioning The Brief Mentalization Measures (BMM; Good et al., in preparation) is a 12-item self- report measure that assesses RF in terms of two parameters: 1) mentalization ability, and 2) mentalization orientation. The mentalization ability subscale included items that assess one’s ability to identify, process, and adaptively express internal states of self and other (e.g., “People 46 get confused when I try to express my feelings.” [R]). The mentalization orientation subscale included items that assessed one’s values and beliefs about the utility of engaging in mentalization (e.g., “I find it important to understand the reasons for my behavior). Items on the BMM are measured using a 5-point Likert-type response scale (1=Never True, 2=Rarely true, 3=Sometimes true, 4=Often true, 5=Very often true). The internal consistency for both the ability and orientation subscales was .76 in the current sample. Childhood Maltreatment The Childhood Trauma Questionnaire (CTQ; Bernstein, Fink, Handelsman, & Foote 1998) is a 28-item retrospective self-report questionnaire designed to assess five-types of childhood maltreatment: 1) emotional neglect, 2) emotional abuse, 3) physical neglect, 4) physical abuse, and 5) sexual abuse. The emotional neglect subscale includes items that assess the failure of caretakers to meet the individual’s basic emotional and psychological needs as a child (e.g., “I felt loved.” [R]). The emotional abuse subscale includes items that assess whether the individual experienced verbal assaults on their sense of worth or well-being and/or humiliating or demeaning behavior directed to them as a child (e.g., “People in my family said hurtful or insulting things to me.”). The physical neglect subscale contains items that assess whether their caretakers failed to provide them physical needs as a child (e.g., I didn’t have enough to eat.”). The physical abuse subscale contains items that assess whether the individual experienced any bodily assaults by an older person that posed risk of, or resulted in, injury (e.g., “I was punished with a belt, a board, a cord, or some other hard object.”). The sexual abuse subscale includes items that assess whether the individual experienced sexual contact or conduct from an adult or older person as a child (e.g., “Someone tried to make me do sexual things or watch sexual things.”). Items on the CTQ are measured on a 5-point likert-type scale (1=Never 47 True, 2=Rarely true, 3=Sometimes true, 4=Often true, 5=Very often true). The internal consistency of CTQ subscales ranged from .74-.95 (Mdn=.91) in the current sample. Given that this study did not make explicit hypotheses about how specific types of maltreatment were associated with EF and RF, a principal components analysis was conducted to examine to what degree CTQ scales could be combined as a single measure of childhood maltreatment. Results from this analysis suggested that a single factor that accounted for 63.7% of the variance in subscales scores should be retained. All CTQ indicators tended to have moderate-to-large loadings on this principal component (see Table 3). Given these results, factor scores for this principal component were used in all analyses examining the effect of childhood maltreatment. Adult Attachment Style The Experiences in Close Relationships – Revised (ECR-R; Fraley, Waller, & Brennan, 2000) is a 36-item self-report questionnaire that assesses individual differences in adult attachment style. The ECR-R is composed of two subscales: 1) Attachment anxiety, and 2) Attachment avoidance. The attachment anxiety subscale includes items that assess an individual’s fear of rejection or abandonment in close relationships (e.g., I’m afraid I will lose my partner’s love). The attachment avoidance subscale is composed of items that assess and individual’s discomfort with intimacy and tendency to seek independence from close relationships (e.g., I am nervous when partners get too close to me). Items the ECR-R are rated on a 7-point likert-type scale (1=Strongly disagree, 2=Disagree, 3=Slightly disagree, 4=Neutral, 5=Slightly agree, 6=Agree, 7=Strongly agree). In the current sample the internal consistency of the attachment anxiety and avoidance scales was .75 and .76, respectively. 48 Working Memory Working memory was assessed using three separate working memory tasks, 1) the digit span task, 2) the reading span test, and 3) the n-back test. The Digit Span task (e.g., Case, Kurland, & Golberg, 1982) is a measure of working memory that includes two conditions: 1) digit span forward, and 2) digit span backward. Digit span forward requires respondents to view and repeat a sequence of numbers as a measure of working memory capacity. The digit span backward requires respondents to view a sequence of numbers and repeat them in backwards order as a measure of an individual’s ability to hold and manipulate information in working memory. Both conditions begin by presenting a sequence of two digits. After each successful trial of the forward and backward tasks, the number of digits in the subsequent trial increases by one, to a maximum of nine digits in the forward condition, and eight digits in the backwards condition. After a failed trial (i.e., if any digits are missing or in the wrong order), the number of digits stays the same for the next trial. The task ends after a respondent makes an error for two trials in a row for a given digit span. In the current study, all digits were presented in the center of their screen in bolded, black, 48point Ariel font for 1000ms and there was a 1000ms interstimulus interval (ISI) between the presentation of each digit in a sequence. After all digits in a sequence were presented, participants were asked to type the sequence in a text box that was presented. There was no time limit for how long participants could take to enter their response. Both conditions of the task were scored as the sum of correct responses (i.e., longest digit span answered correctly). A total correct score was computed by summing the total correct responses across the two conditions. In the current study, the split-half reliability for the digit span forward and backwards conditions were .94 and .97, respectively. The split-half reliability when combining conditions was .97. 49 The Reading Span Test (e.g., Daneman & Carpenter, 1980) requires respondents to read a series of unconnected sentences and remember the final word in each sentence within a series. The reading span test assesses complex working memory functioning as it requires respondents to hold information in mind (i.e., previous read words), while processing new information (i.e., reading new sentences). The current study included three trials for each level of sentence set size (i.e., number of sentences presented). Five set sizes were used, ranging from two to six sentences, making a total of 15 trials in the task. Consistent with previous research using this task (e.g., Klaus & Schriefers, 2016), the order of these trials was randomized within participants to avoid the predictability of storage demands. Within each trial, participants were presented with each sentence for a maximum of 10 seconds or until the participants provided a keyed response to move on to the next sentence. All sentences were presented in black, 14point Ariel font in the center of their screen and there was a 1000ms ISI between each sentence presentation. After the final sentence was presented in a trial, participants were provided with a blank text box and asked to enter the last word of each sentence they were just shown. There was no time limit for participants to provide their responses for each trial. The sentences used for the task were taken from previous studies using the reading span test (Engle et al., 1999; Kane & Engle, 2004) and had a mean length of 14.62 words (SD=1.41). A total correct score was computed for the task by summing the total number of correct trials for each participant. The split-half reliability for the reading span task in the current sample was .98. The N-back test (e.g., Kirchner, 1958) is a continuous performance task that assesses working memory. In the task, respondents are presented one-at-a-time with a series of letter stimuli. Respondents are asked to indicate whether the letter they are currently presented with is the same or different from the letter that was presented n trials earlier. The current study used an 50 n=2 design, meaning that participants were required to indicate whether the letter they were currently viewing was the same as the letter they viewed two trials earlier, or the letter before the last letter they viewed. The current task was separated into three blocks of 25. During each trial, a letter was presented in the center of the participants screen in bolded, black, 48point Arial font for a maximum of 1000ms. There was a 2000ms ISI between each trial. A total of 15 different letters were used in trials across the three blocks (A, B, D, E, F, H, I, J, K, L, O, Q, R, S, and T). During each trial, participants were asked to provide a keyed response to indicate whether the current letter matched or did not match the letter two trials prior. Responses that were not keyed during trial were coded as omissions. A brief training block, which included five trials and provided feedback to participants (i.e., whether they were correct, incorrect, or not responding fast enough), was used prior to the start of the task in order to help participants with their comprehension of the task and pace in which they needed to provide responses. The n-back task was scored by summing the total number of correct responses made for each block, as well as the total number correct responses across the three blocks. The internal consistency of scores at the block level ranged from .67-.76, and was .90 when looking at the total score across all trials. Inhibitory Control Inhibitory control was assessed using three separate tasks: 1) the Stroop task, 2) the Hayling task, and 3) the Go/No-go task The Stroop test (MacLeod, 1991) is a neuropsychological test that assesses inhibitory control via the respondent’s capacity to inhibit interference that occurs when the processing of a specific stimulus feature impedes the simultaneous processing of a second stimulus feature. In the version of the Stroop test used in the current study, participants were required to read a series of color words (i.e., Blue, Red, Yellow, Green) that were presented in a range of colored font 51 (i.e., Blue, Red, Yellow, Green), and respond by indicating the color of the font that they were presented with. A total of 40 trials were used, 20 of which were congruous (i.e., the word and color of the font matched) and 20 non-congruous (i.e., the word and color of font did not match). There were an even number of congruous and non-congruous trials for each color word, however, the order of trials was randomized between participants. All words were presented in bolded, 14point Ariel font in the center of the participants screen and participants used a computer mouse to select the response that matched the color of the font the word was presented in. There was no time limit for participants to respond, however, the instructions for the task requested that participants answer as fast as possible without making mistakes. Upon selecting a response, the next trial was presented automatically following a 1000ms ISI. Responses were coded both in terms of reaction time and correctness. However, due to the potential that reaction times may systematically vary in relation to participant’s skill using a computer mouse, the total number of correct responses on trials was used as the main measure of inhibitory control for this study. The internal consistency for the total number of correct responses on the Stroop task for this sample was .96. The Hayling task (Burgess & Shallice, 1996) is a behavioral measure of inhibitory control. In the task, respondents are presented with 30 incomplete sentences (i.e., missing the final word), and are asked to choose a word to complete the sentence. There are two conditions for the Hayling task, which affect which word the respondent should choose. In the automatic condition (i.e., the first set of 15 sentences) respondents are asked to choose the word that both grammatically and semantically fits the sentence. In the inhibitory condition (i.e., the second set of 15 sentences) the respondent is asked to choose a word that fits grammatically, but not semantically. The 30 sentences included in the Hayling task for this study were selected from 52 pool of sentences that almost all (99%) of participants chose the same word to complete (Burgess & Shallice, 1996). Thus, each sentence used was considered high-cloze (i.e., high probability that all respondents will give the same response). The 30 sentences were randomly assigned to either the automatic or inhibitory condition for each participant. However, each participant always completed the automatic condition prior to the inhibitory condition. While there was no time limit for how long participants were given to provide a response, the instructions for the task requested that participants responded as quickly as possible. For each trial, participants were presented with a sentence with the last word missing (e.g., “The children went outside to _____.”) in black, 14point Ariel font, in the center of their screen. Participants were also provided with a text box below the sentence where they could enter their response. Upon entering a response and pressing the enter key, the next trial was presented following a 1000ms ISI. Responses for both automatic and inhibitory conditions were coded using a 3-point scale in accord with previous methods for scoring the Hayling Task (Burgess & Shallice, 1997). For the automatic condition, a response that matched the high-cloze response (e.g., play, for the above example) was coded as 0, a response that fit semantically and grammatically but did not match the high-cloze response was coded as 1 (e.g., explore), and responses that did not fit grammatically and/or semantically were coded as 3 (e.g., car). For the inhibitory condition, a response that did not fit semantically but fit grammatically was coded as 0, a response that fit semantically and grammatically but did not include the high-cloze response was coded as 1, and a response that matched the high-cloze response or did not fit grammatically was coded as 3. A total error score was calculated within both conditions, as well as across conditions by summing coded scores. The internal consistency was .96 and .98 for the automatic and inhibitory conditions respectively, and .96 across both conditions. 53 The Go/No-go task (Criaud & Boulingeuz, 2012; Verbruggen & Logan, 2008) is a continuous behavioral measure of inhibitory control. In the task, participants are presented with a set of stimuli one-by-one and instructed to provide a response when the stimuli match a certain condition and omit a response when that condition is not met. The current study used numbers ranging from one to eight as the stimuli, and participants were instructed to provide a keyed response (i.e., press ‘space bar’ key) whenever they saw a number that was not an eight. The current study included a single block of 80 trials. Each number (1-8) was presented in a total of 10 trials each, creating a ratio of 87.5% ‘go’ trials and 12.5% ‘no-go’ trials for the block. The order in which each trial as presented was randomized between participants. During each trial, a number was presented in bolded, black, 48point Ariel font in the center of the participants screen for 300ms, followed by a 1000ms ISI prior to the following trial. Participants were required to respond on ‘go’ trials during the 300ms the number was presented on screen. Scores for the Go/No-go task in this study were calculated as the total number of commission errors on ‘no-go’ trials, as this metric was assumed to best capture participants ability to inhibit automatic responses. For the current sample, the internal consistency of commission errors was .70. Data Analysis The initial data was examined to determine whether any participant’s data should be removed due to missingness or poor effort on the cognitive tasks. In particular, participants were dropped from the sample if more than 10% of their data was missing or if they provided nonsensical and/or off-task responses during the cognitive task portion of the study (e.g., responding with “xxxxx” or “this is boring” in the digit span task). Responses such as “I don’t remember” were considered on-task and errors. From a total of 511 initial participants, 82 were dropped from the sample due to meeting one or both of the above criteria, leading to a final 54 sample of 429 participants. Following this, each measure of the study was coded and scored using the procedures described in the Measures section. Confirmatory factor analysis (CFA) was used to examine the latent structure of EF, consistent with the first exploratory aim for this study. All CFA analyses were completed using Mplus (version 8.2; Muthén & Muthén, 2017) using maximum likelihood estimation. A total of three CFA models were examined for their fit in the current sample. The first model examined was a correlated two-factor model that estimated working memory and inhibitory control as separate factors that were allowed to covary. In this model, the number of correct responses on the digit span, reading span, and n-back tasks were used as indicators of working memory, and the number of correct responses on the Stroop task, number of errors on the Hayling task, and number of commission errors on the ‘no-go’ trials of the go/no-go task were used as indicators of inhibitory control. The second model examined was a bi-factor model that estimated all indicators to load onto a general EF factor as well a specific working memory or inhibitory control factors. The specific factor that each indicator was estimated to load onto was identical to the correlated two-factor model. However specific factors were not allowed to covary with one another or the general factor in the bifactor model as the covariance between specific factors was expected to be captured by the general factor. The third model examined was a single factor model which estimated all indicators loaded onto a single, general EF factor. The fit of each of these models was examined based on its RMSEA, CFI, TLI, and SRMR indices. Models were considered to fit the data adequately when RMSEA and SRMR were less than .08, and CFI and TLI were greater than .90. In the case that multiple of the models fit the data adequately, the comparative fit of these models was examined using a chi-square difference test. 55 Zero-order correlations were used to examine the basic associations between the variables included in this study. This included examining how gender and SES (growing-up and current) were associated with RF parameters and EF factors (Exploratory Aim 2), how RF parameters were associated with EF factors (Predictive Aim 1), and how age, experiences of childhood maltreatment, and adult attachment style were associated with RF parameters and EF factors (Predictive Aim 2). Multiple regression analyses were used to examine the moderating role of age, childhood maltreatment, and adult attachment style on the association between RF parameters and EF factors (Predictive Aim 3). Additionally, to the degree that gender and SES were associated with RF parameters and EF factors, multiple regression was also used to examine the moderating role of these variables. All independent variables included in these models were mean-centered within the sample and all interaction terms were computed by taking the product of the variables included in the interaction. For models examining the moderating role of gender, gender was dummy coded such that males=1 and females=0. Those identifying as transgender, gender non- binary, or a gender identity not-listed in the demographic form were not included in these analyses due to the low prevalence of such individuals in the sample. However, these individuals were included in all other analyses that did not examine the effect of gender. A total of four multiple regression models was analyzed for each moderator variable: 1) one model examined the effect of the moderator on the association between working memory and mentalization orientation, 2) one examined the effect of the moderator on the association between inhibitory control and mentalization orientation, 3) one examined the effect of the moderator on the association between working memory and mentalization ability, and 4) one examined the effect of the moderator on the association between inhibitory control and 56 mentalization ability. In instances when the interaction between a moderator and EF factor was significant in predicting an RF parameter, a simple slopes analysis was conducted to examine the association between EF factors and RF parameters at high and low levels of the moderator. For the purposes of these analyses, high levels of the moderator were conceptualized as one standard deviation above the sample mean and low levels of the moderator were conceptualized as one standard deviation below the sample mean. 57 RESULTS Exploratory Aim 1: Determine the Latent Structure of EF Results from the CFA analyses of EF indicators suggested that the correlated two-factor model (x2(df) = 18.14 (8); RMSEA = .054; CFI = .980; TLI = .963, SRMR = .026) and single- factor model (x2(df) = 28.06 (9); RMSEA = .070; CFI = .963; TLI = .938, SRMR = .032) fit the data adequately. The bi-factor model could not be identified in the current sample due to a non- positive definite first-order derivative product matrix. A chi-square difference test was used to examine the comparative fit of the correlated two-factor and single-factor models in the current sample and indicated that the correlated two-factor model significantly fit the data better (Δx2(Δdf) = 9.92 (1); p = <.001). Given this, the correlated two-factor model was selected as the best fitting model in the sample and participant’s factor scores were computed based on this model and used for all subsequent analyses. The specific factor loadings for indicators in the correlated two-factor model are presented in Table 4 and were in the direction expected. Model results indicated that the working memory and inhibitory latent factors were correlated at r = .85, suggesting that the two factors were highly related. Exploratory Aim 2: Examine the Association between Gender, SES, RF, and EF Results from the correlational analyses examining the association between gender and SES and RF parameters and EF factors are presented in Table 5. Gender was associated with both working memory (r = -.27, p = <.001) and inhibitory control (r = -.25, p = <.001), suggesting that EF performance was better in those identifying as female compared to those identifying as male. Gender was not associated (r = .01, p = .80) with mentalization orientation, but was associated with mentalization ability (r = -.10, p = .03), suggesting that those identifying as female tended to report higher mentalization ability than those identifying as male. 58 SES while growing up was positively associated with working memory (r = .29, p = <.001), inhibitory control (r = .28, p = <.001), and mentalization orientation (r = .20, p = <.001), but unrelated to mentalization ability (r = .00, p = .94; see Table 5). However, current SES, was only significantly associated with mentalization orientation (r = .18, p = <.001), and was unrelated to working memory (r = .09, p = .09), inhibitory control (r = .07, p = .18), and mentalization ability (r = .06, p = .27). Predictive Aim 1: Examine the Association between RF and EF Results from the correlational analyses examining the association between RF and EF are presented in Table 6. Both working memory and inhibitory control were significantly positively associated with mentalization ability (r = .21, p = <.001; r = .20, p = <.001, respectively). However, neither working memory or inhibitory control were significantly related to mentalization orientation (r = .03, p = .55; r = .06, p = .25, respectively). Steiger Z tests (Steiger, 1980) were used to examine the degree to which the association between EF factors and mentalization ability were statistically different from the association between EF factors and mentalization orientation. Results from these analyses suggested that, for both working memory and inhibitory control, the association with mentalization ability was significantly greater than with mentalization orientation (Z = 2.89, p = .004; Z = 2.25, p = .02, respectively). Predictive Aim 2: Examine the Association between Age, Childhood Maltreatment, and Adult Attachment Style and RF and EF Results for the correlational analyses examining the association between RF parameters and EF factors and the proposed moderators in the study are presented in Table 6. Age was positively associated with mentalization ability (r = .20, p = <.001), inhibitory control (r = .29, p = <.001), working memory (r = .31, p = <.001) and negatively associated with mentalization 59 orientation (r = -.22, p = <.001). Childhood maltreatment was negatively associated with mentalization ability (r = -.35, p = <.001), inhibitory control (r = -.38, p = <.001), and working memory (r = -.40, p = <.001) and positively associated with mentalization orientation (r = .11, p = .02). Adult attachment anxiety was negatively associated with mentalization ability (r = -.42, p = <.001), inhibitory control (r = -.29, p = <.001), and working memory (r = -.33, p = <.001) and positively associated with mentalization orientation (r = .15, p = .001). Adult attachment avoidance was negatively associated with mentalization orientation (r = -.10, p = .036), mentalization ability (r = -.42, p = <.001), inhibitory control (r = -.22, p = <.001), and working memory (r = -.24, p = <.001). Predictive Aim 3: Examine the Moderating Role of Age, Childhood Maltreatment, and Adult Attachment Style on the Association Between RF and EF Given that gender, SES while growing up, and current SES were found to be associated with RF parameters and EF factors, multiple regression analyses were also conducted post-hoc to examine whether these variables moderated the association between RF and EF. Gender Results examining the moderating role of gender are presented in Table 7. Both the model predicting mentalization orientation by gender and working memory (F(3, 225) = .311, p = .82; R2 = .002) and the model predicting mentalization orientation by gender and inhibitory control (F(3, 225) = .836, p = .475; R2 = .006) were insignificant. Consistent with this neither of these models had significant main effects or interaction terms, suggesting that mentalization orientation was not predicted by gender, EF factors, or their interaction. However, both the model predicting mentalization ability by gender and working memory (F(3, 225) = 7.473, p = <.001; R2 = .05) and the model predicting ability by gender and inhibitory control (F(3, 225) = 60 6.572, p = <.001; R2 = .04) were significant. In both models, the main effect of the EF factor included in the model was significant. In particular, higher working memory (β = .23, p = <.001) and inhibitory control (β = .18, p = <.001) predicted higher mentalization ability. SES Growing-Up Results for multiple regression models examining the moderating role of SES while growing up are presented in Table 8. The model predicting mentalization orientation based on SES and working memory was significant (F(3, 219) = 4.333, p = .005; R2 = .06), as was the model predicting mentalization orientation based on SES and inhibitory control (F(3, 219) = 3.746, p = .012; R2 = .05). In the former model, only the interaction between SES and working memory significantly predicted mentalization orientation (β = -.22, p = .002), whereas in the latter model, both the main effect of inhibitory control (β = .14, p = .05) and the interaction between inhibitory control and SES while growing up (β = -.18, p = .01) were significant. A simple slopes analysis was used to understand the significant interaction terms in these models. Results suggested that at low levels of SES while growing up, there is a positive relationship between working memory and mentalization orientation (β = .34, t = 2.92, p = .004), however, at high levels of SES while growing up, working memory and mentalization orientation were unrelated (β = -.06, t = -.765, p = .45). A similar pattern of results was found for inhibitory control, such that at low levels of SES while growing up inhibitory control and mentalization orientation were positively related (β = .31, t = 2.821, p = .005), but not at high levels of SES while growing up (β = -.03, t = -.322, p = .75). Depictions of the simple slopes for these models are presented in Figures 1 and 2. The models predicting mentalization ability based on SES while growing up and EF factors were significant for both working memory (F(3, 219) = 7.316, p = <.001; R2 = .091) and 61 inhibitory control (F(3, 219) = 6.636, p = <.001; R2 = .083). However, similar to the findings with regard to gender, only the main effect of the EF factor was significant in predicting mentalization ability. Both working memory (β = .30, p = <.001) and inhibitory control (β = .27, p = <.001) were positively associated with mentalization ability in these models and did not differ in their association at different levels of SES while growing up. Current SES Results examining the moderating role of current SES are presented in Table 9. Models predicting mentalization orientation from current SES and EF factors were insignificant for both working memory (F(3, 323) = 2.290, p = .08; R2 = .02) and inhibitory control (F(3, 323) = 2.388, p = .07; R2 = .02), suggesting that these models did not explain a significant amount of variance in mentalization orientation. However, the models predicting mentalization ability based on current SES and EF factor were significant for both working memory (F(3, 323) = 3.428, p = .02; R2 = .03) and inhibitory control (F(3, 323) = 2.968, p = .03 ; R2 = .03). However, again, only the main effect of the EF factor was significant in these models, suggesting that working memory (β = .16, p = <.001) and inhibitory control (β = .15, p = .01) were positively related with mentalization ability and this association did not differ at different levels of current SES. Age Results examining the moderating role of age are presented in Table 10. Models predicting mentalization orientation based on age and EF factors were significant for working memory (F(3, 421) = 9.303, p = <.001; R2 = .06) and inhibitory control (F(3, 421) = 9.803, p = <.001; R2 = .07). In both of the models, the main effects of age and EF factor were significant. In the model with working memory, higher mentalization orientation was predicted by being younger (β = -.27, p = <.001) and having higher working memory ability (β = .13, p = .01). In 62 the model with inhibitory control, higher mentalization orientation was predicted by being younger (β = -.26, p = <.001) and having higher inhibitory control (β = .15, p = <.001). Models predicting mentalization ability based on age and EF factors were also significant for both working memory (F(3, 421) = 9.411, p = <.001; R2 = .06) and inhibitory control (F(3, 421) = 8.869, p = <.001; R2 = .06). Similar to the models for mentalization orientation, only the main effects of age and EF factor was significant in predicting mentalization ability. In the model with working memory, higher mentalization ability was associated with being older (β = .14, p = <.001) and having higher working memory (β = .16, p = <.001). In the model with inhibitory control, higher mentalization ability was predicted by being older (β = .15, p = <.001) and having greater inhibitory control (β = .15, p = <.001). Childhood Maltreatment Results examining the moderating role of childhood maltreatment are presented in Table 11. Models predicting mentalization orientation based on experiences of childhood maltreatment and EF factors were significant for both working memory (F(3, 425) = 5.135, p = .002; R2 = .04) and inhibitory control (F(3, 425) = 5.050, p = .002; R2 = .03). The model with working memory, both the main effect of working memory (β = .12, p = .02) and the interaction between working memory and childhood maltreatment (β = -.15, p = .01) significantly predicted mentalization orientation. In the model with inhibitory control, the main effects of inhibitory control (β = .13, p = .01) and childhood maltreatment (β = .11, p = .05) and their interaction (β = -.12, p = .03) were significant. Simple slopes analyses were used to examine how the association between mentalization orientation and EF factors differed at high and low levels of childhood maltreatment. These results suggested that at low levels of childhood maltreatment, greater mentalization orientation is predicted by having higher working memory (β = .24, t = 3.082, p = 63 .002) and inhibitory control (β = .23, t = 3.076, p = .002). However, at high levels of childhood maltreatment, mentalization orientation is unrelated to working memory (β = .00, t = .044, p = .97) and inhibitory control (β = .03, t = .497, p = .62). Depictions of the simple slopes for these models are presented in Figures 3 and 4. Models predicting mentalization ability based on history of childhood maltreatment and EF factors were also significant for both working memory (F(3,425) = 20.682, p = <.001; R2 = .13) and inhibitory control (F(3, 425) = 20.572, p = <.001; R2 = .13). However, in both of these models only the main effect of childhood maltreatment was significant. In the model with working memory, greater mentalization ability was predicted by having fewer experiences of childhood maltreatment (β = -.32, p = <.001). The direction and size of the main effect of childhood maltreatment was similar in the model that included inhibitory control (β = -.33, p = <.001). Adult Attachment Anxiety Results examining the moderating role of adult attachment anxiety are presented in Table 12. Models predicting mentalization orientation based on attachment anxiety and EF factors were significant for both working memory (F(3, 425) = 8.515, p = <.001; R2 = .06) and inhibitory control (F(3, 425) = 8.628, p = <.001; R2 = .06). In the model with working memory, the main effects of attachment anxiety (β = .20, p = .<.001) and working memory (β = .13, p = .01) and their interaction (β = -.17, p = <.001) were significant predictors of orientation. In the model with inhibitory control, the main effects of attachment anxiety (β = .20, p = <.001), inhibitory control (β = .14, p = .01) and their interaction (β = -.15, p = <.001) were also significant. Simple slopes analyses were used to examine how the relationship between mentalization orientation and EF factors differed at high and low levels of attachment anxiety. These results suggested that 64 at low levels of attachment anxiety, greater mentalization orientation is predicted by higher working memory (β = .30, t = 3.778, p = <.001) and higher inhibitory control (β = .29, t = 3.878, p = <.001). However, at high levels of attachment anxiety, mentalization orientation was unrelated to working memory (β = -.03, t = -.489, p = .63) and inhibitory control (β = -.01 t = - .202, p = .84). Depictions for the simple slopes of these models are presented in Figures 5 and 6. Models predicting mentalization ability based on attachment anxiety and EF factors were also significant for both working memory (F(3, 425) = 32.040, p = <.001, R2 = .18) and inhibitory control (F(3, 425) = 32.094, p = <.001; R2 = .19). In both of these models only the main effect of attachment anxiety was significant in predicting mentalization ability. In the model with working memory, greater mentalization ability was predicted by less adult attachment anxiety (β = -.39, p = <.001). The direction and size of the main effect of attachment anxiety was similar in the model that included inhibitory control (β = -.39, p = <.001). Adult Attachment Avoidance Results examining the moderating role of adult attachment avoidance are presented in Table 13. Models predicting mentalization orientation based on attachment avoidance and EF factors were significant for both working memory (F(3, 425) = 3.721, p = .01; R2 = .03) and inhibitory control (F(3, 425) = 3.159, p = .03; R2 = .02). In the model for working memory, only the interaction between working memory and attachment avoidance (β = -.13, p = .01) was significant in predicting mentalization orientation. Similarly, in the model with inhibitory control the interaction between inhibitory control and attachment avoidance (β = -.11, p = .03) was the only significant predictor. Simple slopes analyses were used to understand how the association between mentalization orientation and EF factors differs at high and low levels of attachment avoidance. Results indicated that when attachment avoidance is low, greater mentalization 65 orientation is predicted by higher working memory (β = .19, t = 2.186, p = .03) and higher inhibitory control (β = .18, t = 2.126, p = .03). However, at high levels of attachment avoidance, mentalization orientation is unrelated to working memory (β = -.10, t = -1.528, p = .13) and inhibitory control (β = -.05, t = -.829, p = .41). Depictions of the simple slopes for these models are presented in Figures 7 and 8. Models predicting mentalization ability based on attachment avoidance and EF factors were also significant for both working memory (F(3, 425) = 33.539, p = <.001; R2 = .19) and inhibitory control (F(3, 425) = 33.009, p = <.001; R2 = .19). In the model with working memory, both the main effect of working memory (β = .13, p = .01) and attachment avoidance (β = -.39, p = <.001) were significant in predicting mentalization ability. In the model with inhibitory control, the main effects of inhibitory control (β = .11, p = .02) and attachment avoidance (β = - .40, p = <.001) were also found to be significant. These results suggest that both attachment avoidance and EF factors predict mentalization ability, however, the effect of EF factors on ability does not differ at high and low levels of attachment avoidance. 66 DISCUSSION The current study aimed to investigate the association between RF and EF, based on the argument that basic EF domains—working memory and inhibitory control—constitute the basic cognitive architecture required for RF. This study built upon previous research that has examined the association between RF and EF in small samples of mothers, by examining this association in a large community sample, looking at the association between EF domains and specific parameters of RF, and exploring how age, experiences of childhood maltreatment, and adult attachment style moderate the association between RF and EF. In addition to this, due to the fact that this study found gender and SES to relate to RF parameters and EF factors, this study also examined how these characteristics moderate the relationship between RF and EF. Consistent with the main hypothesis of this study, results highlighted that EF is indeed positively associated with RF. In particular, both working memory and inhibitory control were fairly robustly associated with the mentalization ability parameter of RF. While working memory and inhibitory control were not associated with mentalization orientation at the zero-order level, results from the multiple regression analyses suggested that mentalization orientation has differential relationships with working memory and inhibitory control at different levels of SES while growing up, experiences of childhood maltreatment, adult attachment anxiety, and adult attachment avoidance. In general, the results from this study were consistent regardless of whether EF was operationalized as working memory or inhibitory control, suggesting that these domains do not differentially relate to RF parameters in the current study. As such, the discussion of the results from this study is organized in terms of RF parameters, rather than domains of EF. 67 Mentalization Orientation This study did not find a basic relationship between mentalization orientation and EF factors. This is largely consistent with the notion that, at face value, one’s values about and motivation to engage in mentalization are not notably associated with one’s level of EF ability. However, mentalization orientation was found to have basic positive associations with SES while growing up, current SES, experiences of childhood maltreatment, and attachment anxiety, and negative associations with age and attachment avoidance. This would suggest that life experiences and individual differences in attachment style tend to be associated with one’s values and motivation to mentalize themselves and others. While this study did not hypothesize about the relationship between mentalization orientation and SES, the general pattern of associations with the other moderator variables included in the study was consistent with the hypotheses for Aim 2. Two exceptions to this are the fact that orientation was found to negatively relate to age and positively relate to experiences of childhood maltreatment. While the finding that age is negatively associated with mentalization orientation runs somewhat contrary to previous findings that empathy tends to increase from young adulthood into middle adulthood (e.g., McAdams & Olson, 2010; O’Brien, Konrath, Gruhm, & Hagen, 2013; Labouvie-Vief, 2009), these results may alternatively reflect the idea that middle- adulthood is marked by the consolidation of one’s close relationships (e.g., Lodi-Smith & Roberts, 2007). That is, whereas young adulthood frequently involves being curious about and exploring a wide range of relationships, social roles and identities, middle adulthood is often includes one’s more focused investment is specific social roles, such as that of a parent or spouse. Thus, middle-aged adults may be less motivated or preoccupied with understanding 68 themselves and others generally, given that they are allocating most of their cognitive and emotional resources towards a smaller selection of relationships. Although I hypothesized that experiences of childhood maltreatment would be negatively associated with mentalization orientation, it not terribly surprising that the opposite association was found. When coupled with the finding that maltreatment is negatively associated with mentalization ability, this may suggest that maltreatment leads one to focus more on understanding themselves and others while having limited ability to adaptively do so. The idea that maltreatment leads to an increased focus on self and other may mirror the hypothesis and finding that attachment anxiety is related to higher mentalization orientation. Importantly, these results may highlight that individuals’ motivation to attend to self and other may be driven by various factors, including those considered to have deleterious effects on one’s relational functioning. For the most part, mentalization orientation is assumed to be driven by one’s intellectualized values—because one believes that it is useful, important, or virtuous to understand self and/or other, they are motivated to do so. However, the associations between mentalization orientation, maltreatment, and attachment anxiety, adds nuance to this and may suggest that mentalization orientation may also be driven by more basic, emotional needs for security and self-protection. This is consistent with previous research that suggests that maltreatment leads to children to be preoccupied with scanning the environment and parent’s facial expressions for possible threats to self (Cicchetti & Toth, 2005; De Bellis, 2005), and that experiences of trauma catalyze the development of associative fear networks that tend to have low activation thresholds and are responsive to a wide range of environmental cues (Park, Mills, & Edmondson, 2012). With this in mind, in the context of abuse and neglect, one is likely to become more focused on understanding self and other, independent of their beliefs about the 69 morality of doing such, given that one’s ability to have their needs met via others is either unpredictable or constantly threatened. The results from the multiple regression analyses for mentalization orientation largely converged with the hypotheses of Aim 3. These results suggested that mentalization orientation is only related to EF factors at low levels of maltreatment, attachment anxiety, and attachment avoidance. At high levels of these moderators, orientation was unrelated to EF. On one hand, these results may provide further evidence that individual’s predisposition to focus on self and other may be driven more so by their difficult relational experiences during childhood and in adult relationships. On the other hand, these results suggest that for individuals who do not have many difficult experiences in relationships throughout their life, variance in how much one thinks about and is motivated to think about themselves in relation to others (and vice versa), is related to their working memory and inhibitory control abilities. While this study does not elucidate the mechanisms by which mentalization orientation and EF are associated at low levels of maltreatment and adult attachment difficulties, these findings may reflect the effortful nature of mentalization orientation. That is, to the degree that mentalization orientation reflects one’s higher-order values or goal-direction towards relationships, one’s ability to maintain this goal in mind and inhibit interference is likely to drive their ability to maintain this orientation toward self and other. In the absence of high EF ability or a dispositional focus on self and other as means for protection, individuals may be limited in their tendency to attend to the complexity of mentalizing self and other. Mentalization Ability Consistent with the hypotheses in Aim 1, this study found that mentalization ability was positively associated with both working memory and inhibitory control. Further, this study found 70 support for the hypotheses of Aim 2, that higher mentalization ability was associated with being older, having fewer experiences of childhood maltreatment, and lower levels of adult attachment anxiety and adult avoidance difficulties. These results fit with the perspective that working memory and inhibitory control are essential components of one’s ability to identify, process, and adaptively express their internal states in relation to others. Further, these results converge with the notion that relational difficulties during childhood and adulthood compromise one’s mentalization ability and that one’s mentalization ability generally increases as one gets older. Given that EF ability was also positive associated with age, it is unclear to what degree the improvement of one’s RF ability is related to the improvement of one’s EF ability versus the accumulation of life experiences which may promote understanding self and other. However, given that both the main effects of age and EF were significant in predicting mentalization ability, it is likely EF ability and the increased experiences that come with age increment each other in predicting RF. Interestingly, results from the multiple regression analyses examining the moderating role of age, childhood maltreatment, and attachment style on the association between mentalization ability and EF did not support the hypotheses of Aim 3. There was no evidence that the association between mentalization ability and EF differed at different levels of the proposed moderators. In fact, in the models examining the moderating role of childhood maltreatment, attachment anxiety, and attachment avoidance, the main effect of EF was no longer predictive of mentalization ability. On one level these results may offer some support for the notion that the quality of representations of self and other are potentially more potent in predicting mentalization ability than EF. That is, while EF is necessary for adaptively identifying, processing, and expressing the content of relational experiences, it is insufficient in promoting 71 good mentalization ability if the content (i.e., representations of self and other) is maladaptive. However, there is also the potential that these results are driven by the fact that there is more shared method variance in how mentalization ability, childhood maltreatment, and attachment style were measured as compared to working memory and inhibitory control. As such, these results may be an artifact of the mentalization ability and the moderators being assessed via self- report items which focus on relationships, whereas the EF tasks were performance based and were not focused on relational functioning. General Discussion Overall, the results of this study highlight that there is a somewhat complex relationship between RF, EF, demographic characteristics, and relational experiences. While at a basic level it is clear that these constructs are closely tied with one another, the specific ways that EF and RF are related when considered in conjunction with experiences of childhood maltreatment and attachment style is rather nuanced. At a fundamental level it is quite clear that one’s previous relational experiences and current attachment styles impact one’s level of RF, EF, and their association. One somewhat consistent finding was that having more relational difficulties during childhood and adulthood tends to reduce the relationship between RF and EF. In particular, it appears that relational difficulties supersede one’s EF abilities in determining their level of RF. When considering the mentalization orientation parameter of RF, results from this study suggest that one’s tendency to focus their attention on understanding self and other is driven by attachment insecurity in individuals who have experienced and/or experience relational difficulties, whereas as it is driven more by EF in individuals who have or do not experience such difficulties. This may suggest that EF is only connected to mentalization orientation to the extent that orientation is a conscious, higher-order, aspirational value of an individual. When 72 considering the mentalization ability parameter of RF, it appears that the basic association between ability and EF disappears when considered in conjunction with difficult relational experiences. This would suggest that relational experiences that affect the adaptiveness of one’s internal representations of self and other (e.g., internal working models, schemas) predict mentalization ability over and above EF. When these results are considered together, they may suggest that the conceptualization of RF should include not only the basic cognitive abilities required to engage in mentalization but also the quality of one’s representations of self and other. At a broad level the results of this study help to connect RF, a construct central to the psychodynamic understanding of adaptive psychosocial functioning, with the basic clinical science literature which predominantly identifies deficits in EF as a core component of psychopathology. However, while this study highlights the overlap in RF and EF, it also suggests that these constructs are not interchangeable. Rather, it appears that RF constitutes a suite of basic (social-)cognitive processes which include, but are not limited to, EF. Thus, it would appear that a more comprehensive basic cognitive model of RF should not only include EF but cognitive processes that underlie the formation and maintenance of representations of self and other. In particular, it is likely that processes related to attention and memory—including how one attends to, encodes, consolidates, and retrieves memories related to self and other—are essential to building a more comprehensive basic cognitive model of RF. Given that RF is often a considered an important moderator and outcome of psychodynamic psychotherapy, it is also interesting to consider the clinical applications of the results from this study. On one hand, these results suggest that having basic executive functioning skills may be an essential foundation for improving RF. As such, it may be important for clinicians to consider patient’s level of EF before targeting improvement of RF as a treatment 73 outcome. Particularly in individuals with neurocognitive deficits affecting EF, it may be unlikely that treatments that explore and aim to shift internal representations of self and other will lead to notable improvement in RF. For such individuals, interventions that improve EF may be a more effective in promoting the basic cognitive skills needed for RF. Interestingly, such skill-based interventions are not frequently associated with the psychodynamic orientation. For patients with average or high EF ability, improving RF will likely not come from continuing to build EF skills, but rather, by shifting internal representations of self and others. For those with lower mentalization orientation, treatment may include helping the patient develop an internal value system for attending to the internal states of self and other. For individuals with high orientation manifesting from attachment insecurity, this work may also include helping the patient shift their motivation to attending to self and other from avoidance of being harmed by others to approaching feeling connected and secure in relationships. However, the results from this study may suggest that age may be an important variable to consider when assessing a patient’s mentalization orientation. That is, given the interpretation that mentalization orientation may dimmish with age due to normative developmental shifts in quantity of social relationships one invests in, improving mentalization orientation may be a particularly important target for treatment adolescent or young adult patients, for whom being curious about themselves in relation to a wide range of others is of developmental significance. For patients in middle- adulthood, it may be less important to consider their mentalization orientation towards relationships broadly, but rather, their curiosity and motivation to attend to the specific relationships in which they are more heavily invested. For patients with lower mentalization ability, treatment may include helping them to build insight into how previous difficult experiences in relationships lead them to see themselves 74 and others in the manner that they do. Importantly, this work would also likely necessarily include helping the patient to build more adaptive representations and self and other via corrective emotional experiences within the therapeutic relationship. Similar to mentalization orientation, it may also be important to consider the patient’s age when assessing mentalization ability. Given that this study found that mentalization ability tends to increase with age into middle-adulthood, difficulties with mentalization ability may be somewhat more developmentally normative in younger patients. This may suggest that for such patients, their mentalization ability may improve somewhat naturally as their EF ability improves and they continue to acquire life experience. In such cases, therapeutic interventions might not only focus on helping to shift current representations via the therapeutic relationship, but also supporting the patient in engaging in social environments that will help to facilitate the continued growth of their mentalization ability. Limitations and Future Directions There were several limitations with the current study. Foremost, while the study attempted to recruit a large diverse sample of participants from the community, the sample particularly lacked diversity with regard to race, gender, and sexual orientation. As such, it is difficult to generalize the current findings to non-white, non-cis-gender, non-heterosexual individuals. Along with these limitations, the current study was also limited by that fact that it was conducted online. While this was done due to limitations to in-person research during the COVID-19 pandemic, the online format for the current study made it difficult, and sometimes impossible, to ensure participants’ attention to and comprehension of the EF tasks used in this study. While participants were required to have a high-speed internet connection to participate in the study, the study was also unable to ensure the fidelity of participants’ internet connections 75 throughout the protocol. Finally, the online format of the study also limited the duration of the study, and thus, the number of trials that could be administered for each EF task. Per the recommendations of Qualtrics.com, the study was developed to take a maximum of 30-45 minutes based on data that suggests that participant attention and effort tends to diminish in online procedures lasting longer than 30 minutes. While there were no explicit concerns about the reliability and validity of the EF tasks, it is assumed that increasing the number of trials for each task would also improve reliability and validity, and potential help with the discriminability of working memory and inhibitory control in the study. This study was also limited in that it measured RF using a self-report measure. While the BMM has been shown to be a reliable and valid indicator of RF that allows for the measurement of different parameters of RF, interview/narrative coding methods such as the Reflective- Functioning Scale (RFS; Fonagy et al., 1998) are frequently considered the gold standard for measuring RF within the field. However, such methods were not feasible given the time and resource constraints of the current study. Related to this, the reliance on self-report of RF also limits the ability to connect EF with specific social-cognitive processes that are indicative of RF. For example, both the cross-sectional nature and specific content of the self-report measure used limited this study’s ability to differentially connect EF domains with specific processes that make up mentalization ability, such as identifying, processing, and expressing emotions. With these limitations in mind, future research should aim to replicate the results from this study using an in-person data collection and with methods that allow for the examination of how different domains of EF relate to different measures and aspects of RF. Additionally future research, should aim to replicate these findings in a more diverse sample and examine the degree to which the current results generalize across different participant characteristics. 76 Building upon this research, it would also be interesting for future research to employ longitudinal designs for examining the association between EF, RF, age, and relational experiences. In particular, the cross-sectional nature of the current study limited the ability to examine how EF and RF develop in relation to one another across different phases of development and lifespan. Additionally, while this study found that age was associated with both RF and EF, the cross-sectional nature of this study makes it difficult to determine whether these effects were truly due to age, or could be explained by, for example, a cohort effect. That is, it is possible that the finding that older individuals had lower mentalization orientation may relate to increases in social values around mental health and diversity over the past several decades. Additionally, the use of longitudinal designs in future research may also shed light on the degree to which the timing of life experiences, including childhood maltreatment and/or other relational trauma, may impact the development of EF and RF and their association to one another. It would also be interesting for future research to examine the association between EF and RF when EF is operationalized in terms tasks that use “hot” or emotionally laden stimuli. A natural hypothesis would be that RF is more strongly related to EF tasks using “hot” stimuli than “cool” stimuli. Such work may not only provide additional support to the notion that RF is the specific deployment of EF to the task of understanding self and other (which frequently include the emotions of self and other), but also be a next step toward developing a novel basic cognitive paradigm for operationalizing RF in experimental research. Finally, per the conclusion made above—that RF constitutes a suite of basic cognitive processes including EF, attention, and memory—ultimately future research should continue to consider how EF intersects with how individuals encode, consolidate, and retrieve representations of self and other via long-term memory. This may include investigating how 77 basic memory abilities relate to RF—similar to how this study examined the association between EF and RF. However, it would also be interesting to use microlongitudinal methods to examine how RF and EF relate to how individual’s representations of self and other are activated and/or shift within or between interpersonal interactions. For example, such research could employ daily diary or ecological momentary assessment methods to assess how relational events in individual’s day-to-day lives lead to shifts in representations as a function of RF and EF. Alternatively, an experiment in which examines pre-post changes in mental representations of self and other following a specific type of interpersonal interaction (e.g., attachment threat inducing), may offer another way to examine how RF and EF relate to how individuals store and access representations of self and others in mind. 78 CONCLUSION The current study replicated and built upon findings from previous research that suggest that EF is associated with RF. Consistent with the main hypotheses of this study, EF was particularly associated with the regulatory aspects of RF. However, EF ability also tended to be associated with one’s tendency to be curious and motivated to understand self and others in individuals with fewer experiences of childhood maltreatment and less adult attachment anxiety, and/or attachment avoidance. When considered together, these results suggest that both the ability to engage in RF and the value and motivation to engage in RF are related to EF. However, these results also highlight that the motivation to engage in RF and RF ability may also be determined by distal and proximal factors outside of one’s EF ability, such as one’s past experiences of maltreatment and current attachment style. Ultimately, these findings only provide a preliminary understanding of how basic cognitive ability intersects with age and social experiences to predict one’s capacity to effectively understand and regulate their own and other’s internal experiences. However, this work is particularly important for establishing an empirical connection between EF and RF, which are viewed as central mechanisms underlying adaptive psychosocial functioning across different theories of psychopathology within clinical psychology. As such, this study is ideally an initial stepping-stone for future research to continue to integrate the construct of RF with the broader clinical science literature. 79 APPENDICES 80 APPENDIX A: Tables Table 1. Sociodemographic Characteristics of Participants Demographic Variable n % Gender Male 203 47.3 Female 221 51.5 Trans-Male 1 0.2 Trans-Female 0 0 Non-Binary 3 0.7 Not listed 1 0.2 Race Indigenous/Native American/American Indian/Alaska Native 5 1.2 Asian 10 2.3 Black 33 7.7 Latinx/Hispanic 9 2.1 Middle Eastern-North African 3 0.7 White 363 84.6 Multiracial 3 0.7 Not listed 3 0.7 Sexual Orientation Asexual 7 1.6 Bisexual 18 4.2 Gay/Lesbian 10 2.3 Heterosexual 389 90.7 Pansexual 3 0.7 Not listed 2 0.5 Highest Level of Education <8th grade 1 0.2 Junior High 2 0.5 Partial Highschool 36 8.4 Highschool Grad 78 18.2 Partial College 75 17.5 College Graduate 153 35.7 Graduate Degree 84 19.6 Household Income (growing up) 0-15k 73 17 16-25k 85 19.8 26-35k 65 15.2 35-50k 71 16.6 51-75k 38 8.9 81 Table 1 (cont’d) 76-100k 31 7.2 101-200k 51 11.9 >200k 14 3.3 Household Income (current) 0-15k 48 11.2 16-25k 52 12.1 26-35k 59 13.8 35-50k 71 16.6 51-75k 65 15.2 76-100k 40 9.3 101-200k 79 18.4 >200k 14 3.3 Note. N=429. Participants were on average 33.34 years old (SD=9.94). 82 Table 2. Principal Component Factor Loadings for SES Growing-up and Current SES Indicators Principal Component Indicator Loading SES Growing-up Household Income .81 Parent's Education .72 Subjective SES .79 Current SES Household Income .85 Highest Education .70 Subjective SES .77 Note. SES Growing-up principal component accounted for 59.7% of variance in indicators; Current SES principal component accounted for 60.3% of variance in indicators. 83 Table 3. Principal Component Factor Loadings for Childhood Maltreatment Indicators Indicator Loading Emotional Abuse .90 Physical Abuse .90 Sexual Abuse .81 Emotional Neglect .45 Physical Neglect .84 Note. Childhood maltreatment principal component accounted for 63.8% of variance in CTQ scale indicators. 84 Table 4. CFA Factor Loadings for Correlated Two-Factor Model of EF Indicators Working Memory Inhibitory Control Indicator Estimate S.E. Estimate S.E. Digit Span Total Correct .735 .035 -- -- Reading Span Total Correct .789 .034 -- -- 2-Back Total Correct .403 .048 -- -- Stroop Total Correct -- -- .593 .045 Hayling Total Errors -- -- -.627 .045 Go/No-go Commission Errors -- -- -.504 .048 Note. Working memory and inhibitory control factors correlated at .85. 85 Table 5. Correlations between Gender, SES, EF Factors, and RF Parameters 1 2 3 4 5 6 1. Working Memory -- 2. Inhibitory Control -.85** -- 3. Mentalization Orientation .03 .06 -- 4. Mentalization Ability .21** .20** .15** -- 5. Gender -.27** -.25** .01 -.10* -- 6. SES Growing-up .29** .28** .20** .00 .21** -- 7. SES Current .09 .07 .18** .06 .15** .54** Note. * indicates p<.05; ** indicates p<.001; Gender dummy-coded as Male=1, Female=0 86 Table 6. Descriptive Statistics and Correlations for Study Variables Variable n M SD 1 2 3 4 5 6 7 1. Working Memory 429 0.0 .89 -- 2. Inhibitory Control 429 0.0 .85 .85** -- 3. Mentalization Orientation 429 25.28 5.12 .03 .06 -- 4. Mentalization Ability 429 25.41 5.42 .21** .20** .15** -- 5. Age 429 33.34 9.94 .31** .29** -.22** .20** -- 6. Childhood Maltreatment 429 0.0 1.00 -.40** -.38** .11* -.35** -.30** -- 7. Attachment Anxiety 429 3.02 1.18 -.33** -.29** .15** -.42** -.25** .45** -- 8. Attachment Avoidance 429 3.72 1.27 -.24** -.22** -.10* -.42** -.18** .40** .38** Note. * indicates p<.05; ** indicates p<.001 87 Table 7. Multiple Regression Results Predicting RF Parameters by Gender and EF Factor DV Model Variable B SE β t p Orientation 1 (Constant) 25.09 .36 70.28 <.001 Gender .39 .52 .04 .75 .46 WM .28 .45 .05 .62 .54 Gender x WM -.09 .59 -.01 -.15 .88 2 (Constant) 25.08 .35 70.81 <.001 Gender .44 .51 .04 .87 .39 IC .37 .46 .06 .80 .42 Gender x IC .04 .61 .01 .07 .95 Ability 3 (Constant) 25.71 .37 69.66 <.001 Gender -.71 .53 -.07 -1.34 .18 WM 1.42 .46 .23 3.06 <.001 Gender x WM -.41 .61 -.05 -.67 .50 4 (Constant) 25.80 .37 70.16 <.001 Gender -.81 .53 -.07 -1.52 .13 IC 1.17 .48 .18 2.44 .02 Gender x IC -.06 .63 -.01 -.09 .93 Note. DV = Dependent variable, WM = working memory, IC = inhibitory control; Gender dummy-coded as Male=1, Female=0; Model 1 F(3, 225)=.311, p=.82; R2=.002; Model 2 F(3, 225)=.836, p=.475; R2=.006; Model 3 F(3, 225)=7.473, p=<.001; R2=.05; Model 4 F(3, 225)=6.572, p=<.001; R2=.044. 88 Table 8. Multiple Regression Results Predicting RF Parameters by SES Growing-up and EF Factor DV Model Variable B SE β t p Orientation 1 (Constant) 25.215 .32 78.06 <.001 SES .69 .38 .13 1.81 .07 WM .74 .40 .14 1.86 .06 SES x WM -1.268 .41 -.22 -3.08 .002 2 (Constant) 25.247 .32 77.94 <.001 SES .73 .38 .13 1.91 .06 IC .80 .40 .14 1.98 .05 SES x IC -1.127 .44 -.18 -2.58 .01 Ability 3 (Constant) 25.159 .37 68.25 <.001 SES .10 .43 .02 .22 .83 WM 1.859 .45 .30 4.10 <.001 SES x WM .07 .47 .01 .15 .89 4 (Constant) 25.212 .37 68.17 <.001 SES .05 .43 .01 .11 .91 IC 1.737 .46 .27 3.80 <.001 SES x IC .38 .50 .05 .77 .45 Note. DV = Dependent variable, WM = working memory, IC = inhibitory control; Model 1 F(3, 219)=4.333, p=.005; R2=.056; Model 2 F(3, 219)=3.746, p=.012; R2=.049; Model 3 F(3, 219)=7.316, p=<.001; R2=.091; Model 4 F(3, 219)=6.636, p=<.001; R2=.083 89 Table 9. Multiple Regression Results Predicting RF Parameters by Current SES and EF Factor DV Model Variable B SE β t p Orientation 1 (Constant) 25.06 .28 89.49 <.001 SES .76 .30 .14 2.50 .01 WM .33 .35 .05 .93 .35 SES x WM -.11 .37 -.02 -.29 .77 2 (Constant) 25.06 .28 89.79 <.001 SES .75 .30 .14 2.47 .01 IC .38 .35 .06 1.06 .29 SES x IC .07 .38 .01 .19 .85 Ability 3 (Constant) 25.58 .29 87.42 <.001 SES .16 .32 .03 .49 .62 WM 1.09 .37 .16 2.97 <.001 SES x WM .33 .38 .05 .86 .39 4 (Constant) 25.59 .29 87.52 <.001 SES .14 .32 .02 .45 .65 IC 1.04 .37 .15 2.81 .01 SES x IC .29 .40 .04 .74 .46 Note. DV = Dependent variable, WM = working memory, IC = inhibitory control; Model 1 F(3, 323)=2.290, p=.078; R2=.021; Model 2 F(3, 323)=2.388, p=.069; R2=.022; Model 3 F(3, 323)=3.428, p=.017; R2=.031; Model 4 F(3, 323)=2.968, p=.032; R2=.027 90 Table 10. Multiple Regression Results Predicting RF Parameters by Age and EF Factor DV Model Variable B SE β t p Orientation 1 (Constant) 25.18 .26 98.44 <.001 Age -1.33 .26 -.26 -5.20 <.001 WM .77 .30 .13 2.55 .01 Age x WM .42 .30 .07 1.43 .15 2 (Constant) 25.22 .25 99.52 <.001 Age -1.34 .25 -.26 -5.28 <.001 IC .87 .31 .15 2.85 <.001 Age x IC -.31 .30 -.05 -1.02 .31 Ability 3 (Constant) 25.41 .27 93.76 <.001 Age .78 .27 .14 2.87 <.001 WM 1.00 .32 .16 3.13 <.001 Age x WM -.03 .31 .00 -.08 .93 4 (Constant) 25.40 .27 94.27 <.001 Age .82 .27 .15 3.02 <.001 IC .97 .32 .15 2.98 <.001 Age x IC .02 .32 .00 .05 .96 Note. DV = Dependent variable, WM = working memory, IC = inhibitory control; F(3, 421)=9.303, p=<.001; R2=.062; Model 2 F(3, 421)=9.803, p=<.001; R2=.065; Model 3 F(3, 421)=9.411, p=<.001; R2=.063; Model 4 F(3, 421)=8.869, p=<.001; R2=.059. 91 Table 11. Multiple Regression Results Predicting RF Parameters by Childhood Maltreatment and EF Factor DV Model Variable B SE β t p Orientation 1 (Constant) 25.04 .26 96.15 <.001 Maltreatment .47 .29 .09 1.64 .10 WM .70 .31 .12 2.26 .02 Maltreatment x WM -.68 .26 -.15 -2.62 .01 2 (Constant) 25.09 .26 96.44 <.001 Maltreatment .56 .29 .11 1.95 .05 IC .80 .31 .13 2.55 .01 Maltreatment x IC -.61 .28 -.12 -2.15 .03 Ability 3 (Constant) 25.38 .26 96.73 <.001 Maltreatment -1.74 .29 -.32 -6.01 <.001 WM .57 .31 .09 1.84 .07 Maltreatment x WM -.11 .26 -.02 -.44 .66 4 (Constant) 25.39 .26 96.82 <.001 Maltreatment -1.77 .29 -.33 -6.12 <.001 IC .52 .32 .08 1.65 .10 Maltreatment x IC -.11 .28 -.02 -.37 .71 Note. DV = Dependent variable, WM = working memory, IC = inhibitory control; Model 1 F(3, 425)=5.135, p=.002; R2=.035; Model 2 F(3, 425)=5.050, p=.002; R2=.034; Model 3 F(3,425)=20.682, p=<.001; R2=.127; Model 4 F(3, 425)=20.572, p=<.001; R2=.126. 92 Table 12. Multiple Regression Results Predicting RF Parameters by Attachment Anxiety and EF Factor DV Model Variable B SE β t p Orientation 1 (Constant) 25.01 .25 98.51 <.001 Anxiety 1.03 .26 .20 3.99 <.001 WM .77 .30 .13 2.60 .01 Anxiety x WM -.94 .28 -.17 -3.43 <.001 2 (Constant) 25.06 .25 99.84 <.001 Anxiety 1.01 .25 .20 4.01 <.001 IC .83 .30 .14 2.77 .01 Anxiety x IC -.91 .28 -.15 -3.22 <.001 Ability 3 (Constant) 25.37 .25 101.47 <.001 Anxiety -2.12 .25 -.39 -8.40 <.001 WM .55 .29 .09 1.89 .06 Anxiety x WM -.16 .27 -.03 -.58 .56 4 (Constant) 25.37 .25 102.60 <.001 Anxiety -2.14 .25 -.39 -8.60 <.001 IC .56 .30 .09 1.89 .06 Anxiety x IC -.20 .28 -.03 -.71 .48 Note. DV = Dependent variable, WM = working memory, IC = inhibitory control; F(3, 425)=8.515, p=<.001; R2=.057; Model 2 F(3, 425)=8.628, p=<.001; R2=.057; Model 3 F(3, 425)=32.040, p=<.001; R2=.184; Model 4 F(3, 425)=32.094, p=<.001; R2=.185. 93 Table 13. Multiple Regression Results Predicting RF Parameters by Attachment Avoidance and EF Factor DV Model Variable B SE β t p Orientation 1 (Constant) 25.11 .25 98.80 <.001 Avoidance -.35 .26 -.07 -1.34 .18 WM .27 .30 .05 0.90 .37 Avoidance x WM -.83 .32 -.13 -2.58 .01 2 (Constant) 25.16 .25 99.46 <.001 Avoidance -.34 .26 -.07 -1.33 .19 IC .37 .30 .06 1.21 .23 Avoidance x IC -.69 .32 -.11 -2.13 .03 Ability 3 (Constant) 25.38 .25 103.48 <.001 Avoidance -2.10 .25 -.39 -8.34 <.001 WM .78 .29 .13 2.71 .01 Avoidance x WM -.19 .31 -.03 -.62 .53 4 (Constant) 25.42 .24 104.20 <.001 Avoidance -2.16 .25 -.40 -8.60 <.001 IC .70 .29 .11 2.38 .02 Avoidance x IC .01 .31 .00 .03 .98 Note. DV = Dependent variable, WM = working memory, IC = inhibitory control; Model 1 F(3, 425)=3.721, p=<.012; R2=.026; Model 2 F(3, 425)=3.159, p=<.025; R2=.022; Model 3 F(3, 425)=33.539, p=<.001; R2=.191; Model 4 F(3, 425)=33.009, p=<.001; R2=.189. 94 APPENDIX B: Figures Figure 1. Interaction between SES Growing-up and Working Memory Predicting Mentalization Orientation 0.3 0.2 0.1 Mentalization Orientation 0 -0.1 High SES -0.2 Low SES -0.3 -0.4 -0.5 -0.6 High Working Memory Low Working Memory 95 Figure 2. Interaction between SES Growing-up and Inhibitory Control Predicting Mentalization Orientation 0.3 0.2 0.1 Mentalization Orientation 0 -0.1 High SES Low SES -0.2 -0.3 -0.4 -0.5 High inhibitory Control Low Inhibitory Control 96 Figure 3. Interaction between Childhood Maltreatment and Working Memory Predicting Mentalization Orientation 0.3 0.2 Mentalization Orientation 0.1 0 High CTQ -0.1 Low CTQ -0.2 -0.3 -0.4 High Working Memory Low Working Memory 97 Figure 4. Interaction between Childhood Maltreatment and Inhibitory Control Predicting Mentalization Orientation 0.2 0.1 Mentalization Orientation 0 High CTQ -0.1 Low CTQ -0.2 -0.3 -0.4 High Inhibitory Control Low Inhibitory Control 98 Figure 5. Interaction between Attachment Anxiety and Working Memory Predicting Mentalization Orientation 0.3 0.2 Mentalization Orientation 0.1 0 High ANX Low ANX -0.1 -0.2 -0.3 -0.4 High Working Memory Low Working Memory 99 Figure 6. Interaction between Attachment Anxiety and Inhibitory Control Predicting Mentalization Orientation 0.2 0.1 Mentalization Orientation 0 High ANX -0.1 Low ANX -0.2 -0.3 -0.4 High Inhibitory Conrtol Low Inhibitory Control 100 Figure 7. Interaction between Attachment Avoidance and Working Memory Predicting Mentalization Orientation 0.25 0.2 Mentalization Orientation 0.15 0.1 High AVD 0.05 Low AVD 0 -0.05 -0.1 -0.15 High Working Memory Low Working Memory 101 Figure 8. Interaction between Attachment Avoidance and Inhibitory Control Predicting Mentalization Orientation 0.3 0.25 0.2 Mentalization Orientation 0.15 0.1 High AVD 0.05 Low AVD 0 -0.05 -0.1 -0.15 High Inhibitory Control Low Inhibitory Control 102 REFERENCES 103 REFERENCES Adler, N. E., Epel, E. S., Castellazzo, G., & Ickovics, J. R. (2000). Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy, White women. Health Psychology, 19, 586-592. Aldao, A., Nolen-Hoeksema, S., & Schweizer, S. (2010). Emotion-regulation strategies across psychopathology: A meta-analytic review. Clinical Psychology Review, 30, 217-237. Alderson, R. M., Kasper, L. J., Hudec, K. L., & Patros, C. H. (2013). Attention- deficit/hyperactivity disorder (ADHD) and working memory in adults: A meta-analytic review. Neuropsychology, 27, 287. Allen, J. (2008). The attachment system in adolescence. In J. Cassidy & P. R. Shaver (Eds.), Handbook of attachment: Theory, research, and clinical applications (p. 419– 435). The Guilford Press. Allen, J. (2013). Mentalizing in the Development and Treatment of Attachment Trauma. London: Karnac Books. Allen, J., Fonagy, P., & Bateman, A. W. (2008). Mentalizing in clinical practice. American Psychiatric Publishing. Anderson, V. A., Anderson, P., Northam, E., Jacobs, R., & Catroppa, C. (2001). Development of executive functions through late childhood and adolescence in an Australian sample. Developmental Neuropsychology, 20, 385-406. Baer, J. C., & Martinez, C. D. (2006). Child maltreatment and insecure attachment: A meta‐ analysis. Journal of Reproductive and Infant Psychology, 24, 187-197. Baer, R. A., Smith, G. T., & Allen, K. B. (2004). Assessment of mindfulness by self-report: The Kentucky Inventory of Mindfulness Skills. Assessment, 11, 191-206. Baddeley, A. (2003). Working memory: looking back and looking forward. Nature Reviews Neuroscience, 4, 829-839. Badoud, D., Luyten, P., Fonseca-Pedrero, E., Eliez, S., Fonagy, P., & Debbané, M. (2015). The French version of the Reflective Functioning Questionnaire: validity data for adolescents and adults and its association with non-suicidal self-injury. PLoS One, 10, e0145892. Baillargeon, R., Scott, R. M., & He, Z. (2010). False-belief understanding in infants. Trends in Cognitive Sciences, 14, 110-118. Barkley, R. A. (1997). Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychological Bulletin, 121, 65. 104 Barkley, R. A. (2012). Executive functions: What they are, how they work, and why they evolved. Guilford Press. Barkus, E. (2020). Effects of working memory training on emotion regulation: Transdiagnostic review. PsyChi Journal, 9, 258-279. Baron-Cohen, S., Wheelwright, S., Hill, J., Raste, Y., & Plumb, I. (2001). The “Reading the Mind in the Eyes” Test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism. The Journal of Child Psychology and Psychiatry and Allied Disciplines, 42, 241-251. Barrett, L. F., Tugade, M. M., & Engle, R. W. (2004). Individual differences in working memory capacity and dual-process theories of the mind. Psychological bulletin, 130(4), 553. Bartels, A., & Zeki, S. (2004). The neural correlates of maternal and romantic love. Neuroimage, 21, 1155-1166. Bateman, A., & Fonagy, P. (2001). Treatment of borderline personality disorder with psychoanalytically oriented partial hospitalization: an 18-month follow-up. American Journal of Psychiatry, 158, 36-42. Bateman, A. W., & Fonagy, P. (2004). Mentalization-based treatment of BPD. Journal of Personality Disorders, 18, 36-51. Bateman, A., & Fonagy, P. (2008). 8-year follow-up of patients treated for borderline personality disorder: mentalization-based treatment versus treatment as usual. American Journal of Psychiatry, 165, 631-638. Baumeister, R. F., & Heatherton, T. F. (1996). Self-regulation failure: An overview. Psychological Inquiry, 7, 1-15. Beeghly, M., & Cicchetti, D. (1994). Child maltreatment, attachment, and the self system: Emergence of an internal state lexicon in toddlers at high social risk. Development and Psychopathology, 6(1), 5-30. Belleville, S., Rouleau, N., & Van der Linden, M. (2006). Use of the Hayling task to measure inhibition of prepotent responses in normal aging and Alzheimer’s disease. Brain and Cognition, 62, 113-119. Benoit, D., Parker, K., & Zeanah, C. H. (1997). Mothers representations of their infants assessed prenatally: Stability and association with their infants’ attachment classifications. Journal of Child Psychology and Psychiatry, 38, 307 – 313. Berg, J. M., Latzman, R. D., Bliwise, N. G., & Lilienfeld, S. O. (2015). Parsing the heterogeneity of impulsivity: A meta-analytic review of the behavioral implications of the UPPS for psychopathology. Psychological assessment, 27, 1129. 105 Bernstein, D. P., Fink, L., Handelsman, L., & Foote, J. (1994). Childhood Trauma Questionnaire (CTQ) [Database record]. APA PsycTests. Bifulco, A., Kwon, J., Jacobs, C., Moran, P. M., Bunn, A., & Beer, N. (2006). Adult attachment style as mediator between childhood neglect/abuse and adult depression and anxiety. Social Psychiatry and Psychiatric Epidemiology, 41(10), 796-805. Birnbaum, G. E., Orr, I., Mikulincer, M., & Florian, V. (1997). When marriage breaks up-does attachment style contribute to coping and mental health?. Journal of Social and Personal Relationships, 14, 643-654. Bishop, S. R., Lau, M., Shapiro, S., Carlson, L., Anderson, N. D., Carmody, J., ... & Devins, G. (2004). Mindfulness: A proposed operational definition. Clinical Psychology: Science and Practice, 11, 230-241. Blair, R. J. (1995). A cognitive developmental approach to morality: investigating the psychopath. Cognition, 57, 1–29. Blair, C., Raver, C.C., & Finegood, E.D. (2016). Self-regulation and developmental psychopathology: Experiential canalization of brain and behavior. In D. Cicchetti. (Ed.), Developmental psychopathology (3rd edn, vol. III, pp. 484–522). New York: John Wiley & Sons. Blatt, S. J., & Blass, R. B. (1996). Relatedness and self-definition: A dialectic model of personality development. Development and vulnerabilities in close relationships, 309- 338. Block, J. H., & Block, J. (1980). The role of ego-control and ego-resiliency in the organization of behavior. In W.A. Collings (Ed.), The Minnesota symposia on child psychology (vol. 13, pp. 39–101). Hillsdale, NJ: Erlbaum. Bock, A. M., Gallaway, K. C., & Hund, A. M. (2015). Specifying links between executive functioning and theory of mind during middle childhood: Cognitive flexibility predicts social understanding. Journal of Cognition and Development, 16, 509-521. Bopp, K. L., & Verhaeghen, P. (2005). Aging and verbal memory span: A meta-analysis. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 60, P223- P233. Borella, E., Carretti, B., & De Beni, R. (2008). Working memory and inhibition across the adult life-span. Acta Psychologica, 128, 33-44. Borelli, J. L., Brugnera, A., Zarbo, C., Rabboni, M., Bondi, E., Tasca, G. A., & Compare, A. (2019). Attachment comes of age: adolescents’ narrative coherence and reflective functioning predict well-being in emerging adulthood. Attachment & Human Development, 21, 332-351. 106 Bouchard, M. A., Target, M., Lecours, S., Fonagy, P., Tremblay, L. M., Schachter, A., & Stein, H. (2008). Mentalization in adult attachment narratives: Reflective functioning, mental states, and affect elaboration compared. Psychoanalytic Psychology, 25, 47. Bowlby, J. (1969). Attachment and Loss, Vol. 1: Attachment. Attachment and Loss. New York: Basic Books. Bowlby, J. (1973). Attachment and loss. Vol. 2: Separation: anxiety and anger. New York, NY: Basic Books. Bowlby, J. (1988). A secure base: Parent-child attachment and healthy human development. New York: Basic Books. Brennan, K. A., Clark, C. L., & Shaver, P. R. (1998). Self-report measurement of adult attachment: An integrative overview. In J. A. Simpson & W. S. Rholes (Eds.), Attachment theory and close relationships (p. 46–76). The Guilford Press. Brewin, C. R., & Beaton, A. (2002). Thought suppression, intelligence, and working memory capacity. Behaviour Research and Therapy, 40, 923-930. Brewin, C. R., & Smart, L. (2005). Working memory capacity and suppression of intrusive thoughts. Journal of Behavior Therapy and Experimental Psychiatry, 36, 61-68. Burgess, P. W., & Shallice, T. (1996). Response suppression, initiation and strategy use following frontal lobe lesions. Neuropsychologia, 34, 263-272. Camras, L. A., Sachs-Alter, E., & Ribordy, S. C. (1996). Emotion understanding in maltreated children: Recognition of facial expressions and integration with other emotion cues. In M. Lewis & M. W. Sullivan (Eds.), Emotional development in atypical children (p. 203– 225). Lawrence Erlbaum Associates, Inc. Carlson, S. M. (2005). Developmentally sensitive measures of executive function in preschool children. Developmental neuropsychology, 28, 595-616. Carlson, S. M., & Beck, D. M. (2009). Symbols as tools in the development of executive function. In A. Winsler, C. Fernyhough, & I. Montero (Eds.), Private speech, executive functioning, and the development of verbal self-regulation (p. 163–175). Cambridge University Press. Carlson, S. M., Moses, L. J., & Breton, C. (2002). How specific is the relation between executive function and theory of mind? Contributions of inhibitory control and working memory. Infant and Child Development: An International Journal of Research and Practice, 11, 73-92. Carlson, S. M., Moses, L. J., & Claxton, L. J. (2004). Individual differences in executive functioning and theory of mind: An investigation of inhibitory control and planning ability. Journal of Experimental Child Psychology, 87, 299-319. 107 Carpendale, J., & Lewis, C. (2006). How children develop social understanding. Blackwell Publishing. Carver, C. S., Johnson, S. L., & Timpano, K. R. (2017). Toward a functional view of the p factor in psychopathology. Clinical Psychological Science, 5, 880-889. Case, R., Kurland, D. M., & Goldberg, J. (1982). Operational efficiency and the growth of short- term memory span. Journal of Experimental Child Psychology, 33, 386-404. Casey, B.J. (2015). Beyond simple models of self-control to circuit-based accounts of adolescent behavior. Annual Review of Psychology, 66, 295–319. Caspi, A., Houts, R. M., Belsky, D. W., Goldman-Mellor, S. J., Harrington, H., Israel, S., ... & Moffitt, T. E. (2014). The p factor: one general psychopathology factor in the structure of psychiatric disorders?. Clinical Psychological Science, 2, 119-137. Castellanos-Ryan, N., Brière, F. N., O'Leary-Barrett, M., Banaschewski, T., Bokde, A., Bromberg, U., ... & Garavan, H. (2016). The structure of psychopathology in adolescence and its common personality and cognitive correlates. Journal of Abnormal Psychology, 125, 1039. Champagne, F., Diorio, J., Sharma, S., & Meaney, M. J. (2001). Naturally occurring variations in maternal behavior in the rat are associated with differences in estrogen-inducible central oxytocin receptors. Proceedings of the National Academy of Sciences, 98, 12736-12741. Charman, T., Carroll, F., & Sturge, C. (2001). Theory of mind, executive function, and social competence in boys with ADHD. Emotional and Behavioural Difficulties, 6, 31-49. Choi-Kain, L. W., & Gunderson, J. G. (2008). Mentalization: Ontogeny, assessment, and application in the treatment of borderline personality disorder. American Journal of Psychiatry, 165, 1127-1135. Cicchetti, D., Rogosch, F. A., Maughan, A., Toth, S. L., & Bruce, J. (2003). False belief understanding in maltreated children. Development and Psychopathology, 15, 1067-1091. Cicchetti, D., & Toth, S. L. (2005). Child maltreatment. Annual Review of Clinical Psychology, 1, 409-438. Cloitre, M., Scarvalone, P., & Difede, J. (1997). Posttraumatic stress disorder, self‐and interpersonal dysfunction among sexually retraumatized women. Journal of Traumatic Stress, 10, 437-452. Cohen, L. J., Ardalan, F., Tanis, T., Halmi, W., Galynker, I., Von Wyl, A., & Hengartner, M. P. (2017). Attachment anxiety and avoidance as mediators of the association between childhood maltreatment and adult personality dysfunction. Attachment & Human Development, 19, 58-75. 108 Cohen‐Gilbert, J. E., & Thomas, K. M. (2013). Inhibitory control during emotional distraction across adolescence and early adulthood. Child Development, 84, 1954-1966. Collette, F., Van der Linden, M., Delfiore, G., Degueldre, C., Luxen, A., & Salmon, E. (2001). The functional anatomy of inhibition processes investigated with the Hayling task. Neuroimage, 14, 258-267. Collishaw, S., Pickles, A., Messer, J., Rutter, M., Shearer, C., & Maughan, B. (2007). Resilience to adult psychopathology following childhood maltreatment: Evidence from a community sample. Child Abuse & Neglect, 31, 211-229. Congdon, E., Mumford, J. A., Cohen, J. R., Galvan, A., Canli, T., & Poldrack, R. A. (2012). Measurement and reliability of response inhibition. Frontiers in Psychology, 3, 37. Conway, A. R., Kane, M. J., Bunting, M. F., Hambrick, D. Z., Wilhelm, O., & Engle, R. W. (2005). Working memory span tasks: A methodological review and user’s guide. Psychonomic Bulletin & Review, 12, 769-786. Cowan, N., AuBuchon, A. M., Gilchrist, A. L., Ricker, T. J., & Saults, J. S. (2011). Age differences in visual working memory capacity: Not based on encoding limitations. Developmental science, 14, 1066-1074. Crowell, J. A., Fraley, R. C., & Shaver, P. R. (1999). Measurement of individual differences in adolescent and adult attachment. In J. Cassidy & P. R. Shaver (Eds.), Handbook of attachment: Theory, research, and clinical applications (p. 434–465). The Guilford Press. Cyr, C., Euser, E. M., Bakermans-Kranenburg, M. J., & Van Ijzendoorn, M. H. (2010). Attachment security and disorganization in maltreating and high-risk families: A series of meta-analyses. Development and Psychopathology, 22, 87-108. Daneman, M., & Carpenter, P. A. (1980). Individual differences in working memory and reading. Journal of Memory and Language, 19, 450. Damasio, A. R. (1994). Descartes' error and the future of human life. Scientific American, 271, 144-144. Damasio, A. R. (1998). Emotion in the perspective of an integrated nervous system. Brain research reviews, 26, 83-86. Davidson, M. C., Amso, D., Anderson, L. C., & Diamond, A. (2006). Development of cognitive control and executive functions from 4 to 13 years: Evidence from manipulations of memory, inhibition, and task switching. Neuropsychologia, 44, 2037-2078. De Bellis, M. D. (2005). The psychobiology of neglect. Child Maltreatment, 10, 150-172. Decety, J. E., & Ickes, W. E. (2009). The Social Neuroscience of Empathy. MIT Press. 109 Decety, J., & Jackson, P. L. (2004). The functional architecture of human empathy. Behavioral and Cognitive Neuroscience Reviews, 3, 71-100. Decety, J., & Jackson, P. L. (2006). A social-neuroscience perspective on empathy. Current Directions in Psychological Science, 15, 54-58. Diamond, A. (1995). Evidence of robust recognition memory early in life even when assessed by reaching behavior. Journal of experimental child psychology, 59, 419-456. Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135-168. Diamond, D. (2004). Attachment disorganization: The reunion of attachment theory and psychoanalysis. Psychoanalytic Psychology, 21, 276. Dimitrijević, A., Hanak, N., Altaras Dimitrijević, A., & Jolić Marjanović, Z. (2018). The Mentalization Scale (MentS): A self-report measure for the assessment of mentalizing capacity. Journal of Personality Assessment, 100, 268-280. Duckworth, A. L., Gendler, T. S., & Gross, J. J. (2016). Situational strategies for self- control. Perspectives on Psychological Science, 11, 35-55. Duncan, J. (1986). Disorganisation of behaviour after frontal lobe damage. Cognitive Neuropsychology, 3, 271-290. Eagle, M. (1995). The developmental perspectives of attachment and psychoanalytic theory. Attachment theory: Social, developmental, and clinical perspectives, 123-150. Echterhoff, G., Higgins, E. T., & Levine, J. M. (2009). Shared reality: Experiencing commonality with others’ inner states about the world. Perspectives in Psychological Science, 4, 496-521. Edwards, A., Shipman, K., & Brown, A. (2005). The socialization of emotional understanding: A comparison of neglectful and nonneglectful mothers and their children. Child Maltreatment, 10, 293-304. Ensink, K., Berthelot, N., Bernazzani, O., Normandin, L., & Fonagy, P. (2014). Another step closer to measuring the ghosts in the nursery: preliminary validation of the Trauma Reflective Functioning Scale. Frontiers in Psychology, 5, 1471. Enticott, P. G., Ogloff, J. R., & Bradshaw, J. L. (2006). Associations between laboratory measures of executive inhibitory control and self-reported impulsivity. Personality and Individual Differences, 41, 285-294. Erikson, E. H. (1968). Identity: Youth and crisis (No. 7). WW Norton & company. Falkenström, F., Solbakken, O. A., Möller, C., Lech, B., Sandell, R., & Holmqvist, R. (2014). Reflective functioning, affect consciousness, and mindfulness: Are these different functions?. Psychoanalytic psychology, 31, 26. 110 Feldman, R., Weller, A., Zagoory-Sharon, O., & Levine, A. (2007). Evidence for a neuroendocrinological foundation of human affiliation: plasma oxytocin levels across pregnancy and the postpartum period predict mother-infant bonding. Psychological Science, 18, 965-970. Fischer-Kern, M., Buchheim, A., Hörz, S., Schuster, P., Doering, S., Kapusta, N. D., ... & Fonagy, P. (2010). The relationship between personality organization, reflective functioning, and psychiatric classification in borderline personality disorder. Psychoanalytic Psychology, 27, 395. Fischer-Kern, M., Fonagy, P., Kapusta, N. D., Luyten, P., Boss, S., Naderer, A., ... & Leithner, K. (2013). Mentalizing in female inpatients with major depressive disorder. The Journal of Nervous and Mental Disease, 201, 202-207. Fischer-Kern, M., Tmej, A., Kapusta, N. D., Naderer, A., Leithner-Dziubas, K., Löffler-Stastka, H., & Springer-Kremser, M. (2008). The capacity for mentalization in depressive patients: A pilot study. Zeitschrift fur Psychosomatische Medizin und Psychotherapie, 54, 368-380. Flückiger, C., Del Re, A. C., Wampold, B. E., & Horvath, A. O. (2018). The alliance in adult psychotherapy: A meta-analytic synthesis. Psychotherapy, 55, 316. Foa, E. B., Riggs, D. S., Dancu, C. V., & Rothbaum, B. O. (1993). Reliability and validity of a brief instrument for assessing post‐traumatic stress disorder. Journal of Traumatic Stress, 6, 459-473. Foa, E. B., Steketee, G., & Rothbaum, B. O. (1989). Behavioral/cognitive conceptualizations of post-traumatic stress disorder. Behavior Therapy, 20, 155-176. Fonagy, P. (1989). On tolerating mental states: Theory of mind in borderline personality. Bulletin of the Anna Freud Centre, 12, 91-115. Fonagy, P. (1991). Thinking about thinking: Some clinical and theoretical considerations in the treatment of a borderline patient. International Journal of Psycho-Analysis, 72, 639-656. Fonagy, P., & Bateman, A. (2008). The development of borderline personality disorder—A mentalizing model. Journal of personality disorders, 22, 4-21. Fonagy, P., Gergely, G., Jurist, E., & Target, M. (2004). Affect regulation, mentalization, and the development of the self. New York: Other Press. Fonagy, P., Gergely, G., & Target, M. (2007). The parent–infant dyad and the construction of the subjective self. Journal of Child Psychology and Psychiatry, 48, 288-328. Fonagy, P., & Luyten, P. (2009). A developmental, mentalization-based approach to the understanding and treatment of borderline personality disorder. Developmental Psychopathology, 21, 1355-1381. 111 Fonagy, P., & Luyten, P. (2016). A multilevel perspective on the development of borderline personality disorder. Developmental psychopathology, 1-67. Fonagy, P., Luyten, P., & Strathearn, L. (2011). Borderline personality disorder, mentalization, and the neurobiology of attachment. Infant Mental Health Journal, 32(1), 47-69. Fonagy, P., Redfern, S., & Charman, T. (1997). The relationship between belief‐desire reasoning and a projective measure of attachment security (SAT). British Journal of Developmental Psychology, 15, 51-61. Fonagy, P., Steele, M., Steele, H., Moran, G. S., & Higgitt, A. C. (1991). The capacity for understanding mental states: The reflective self in parent and child and its significance for security of attachment. Infant mental health journal, 12, 201-218. Fonagy, P., & Target, M. (1996). Playing with reality: I. Theory of mind and the normal development of psychic reality. International Journal of Psychoanalysis, 77, 217-233. Fonagy, P., & Target, M. (2003). Psychoanalytic Theories: Perspectives from Developmental Psychopathology. New York, NY: Taylor and Francis. Fonagy, P., Target, M., Steele, H., & Steele, M. (1998). Reflective-functioning manual, version 5.0, for application to adult attachment interviews. London: University College London, 161-2. Fossati, A., Feeney, J., Maffei, C., & Borroni, S. (2014). Thinking about feelings: Affective state mentalization, attachment styles, and borderline personality disorder features among Italian nonclinical adolescents. Psychoanalytic Psychology, 31, 41. Fraley, R. C., & Shaver, P. R. (2000). Adult romantic attachment: Theoretical developments, emerging controversies, and unanswered questions. Review of General Psychology, 4, 132-154. Fraley, R. C., Waller, N. G., & Brennan, K. A. (2000). An item response theory analysis of self- report measures of adult attachment. Journal of Personality and Social Psychology, 78, 350. Friedman, N. P., & Miyake, A. (2017). Unity and diversity of executive functions: Individual differences as a window on cognitive structure. Cortex, 86, 186-204. Friedman, N. P., Miyake, A., Altamirano, L. J., Corley, R. P., Young, S. E., Rhea, S. A., & Hewitt, J. K. (2016). Stability and change in executive function abilities from late adolescence to early adulthood: A longitudinal twin study. Developmental Psychology, 52, 326. Fujita, K. (2011). On conceptualizing self-control as more than the effortful inhibition of impulses. Personality and Social Psychology Review, 15, 352-366. 112 Gan, S., Yang, J., Chen, X., Zhang, X., & Yang, Y. (2017). High working memory load impairs the effect of cognitive reappraisal on emotional response: Evidence from an event-related potential study. Neuroscience Letters, 639, 126-131. George, C., Kaplan, N., & Main, M. (1996). The Adult Attachment Interview. Unpublished protocol (3rd edition). Department of Psychology, University of California at Berkeley Gergely, G., & Watson, J.S. (1996). The social biofeedback theory of parental affect mirroring: The development of emotional self-awareness and self-control in infancy. International Journal of Psycho-Analysis, 77, 1181-1212. Gillath, O., Bunge, S. A., Shaver, P. R., Wendelken, C., & Mikulincer, M. (2005). Attachment- style differences in the ability to suppress negative thoughts: exploring the neural correlates. Neuroimage, 28, 835-847. Giordano, P. C. (2003). Relationships in adolescence. Annual Review of Sociology, 29, 257-281. Granqvist, P., Sroufe, L. A., Dozier, M., Hesse, E., Steele, M., van Ijzendoorn, M., ... & Steele, H. (2017). Disorganized attachment in infancy: a review of the phenomenon and its implications for clinicians and policy-makers. Attachment & Human Development, 19, 534-558. Greenberg, D. M., Kolasi, J., Hegsted, C. P., Berkowitz, Y., & Jurist, E. L. (2017). Mentalized affectivity: A new model and assessment of emotion regulation. PloS one, 12, e0185264. Gross, J. J. (2014). Emotion regulation: Conceptual and empirical foundations. Guarino, S., & Vismara, L. (2012). Mental state of attachment and reflective function in a group of antisocial adolescents. Psicologia Clinica dello Sviluppo, 16, 579-598. Ha, C., Sharp, C., Ensink, K., Fonagy, P., & Cirino, P. (2013). The measurement of reflective function in adolescents with and without borderline traits. Journal of Adolescence, 36, 1215-1223. Håkansson, U., Söderström, K., Watten, R., Skårderud, F., & Øie, M. G. (2018). Parental reflective functioning and executive functioning in mothers with substance use disorder. Attachment & Human Development, 20, 181-207. Haldane, M., Cunningham, G., Androutsos, C., & Frangou, S. (2008). Structural brain correlates of response inhibition in Bipolar Disorder I. Journal of Psychopharmacology, 22, 138- 143. Hamilton, C. E. (2000). Continuity and discontinuity of attachment from infancy through adolescence. Child Development, 71, 690-694. Hankin, B. L. (2005). Childhood maltreatment and psychopathology: Prospective tests of attachment, cognitive vulnerability, and stress as mediating processes. Cognitive Therapy and Research, 29, 645-671. 113 Hasher, L., & Zacks, R. T. (1988). Working memory, comprehension, and aging: A review and a new view. The Psychology of Learning and Motivation, 22, 193-225. Hendricks, M. A., & Buchanan, T. W. (2016). Individual differences in cognitive control processes and their relationship to emotion regulation. Cognition and Emotion, 30, 912- 924. Herrenkohl, E. C., Herrenkohl, R. C., Rupert, L. J., Egolf, B. P., & Lutz, J. G. (1995). Risk factors for behavioral dysfunction: The relative impact of maltreatment, SES, physical health problems, cognitive ability, and quality of parent-child interaction. Child Abuse and Neglect, 19, 191-203. Hofmann, W., Gschwendner, T., Friese, M., Wiers, R. W., & Schmitt, M. (2008). Working memory capacity and self-regulatory behavior: Toward an individual differences perspective on behavior determination by automatic versus controlled processes. Journal of Personality and Social Psychology, 95, 962. Hofmann, W., Schmeichel, B. J., & Baddeley, A. D. (2012). Executive functions and self- regulation. Trends in Cognitive Sciences, 16, 174-180. Hou, C. L., Xiang, Y. T., Wang, Z. L., Everall, I., Tang, Y., Yang, C., ... & Jia, F. J. (2016). Cognitive functioning in individuals at ultra-high risk for psychosis, first-degree relatives of patients with psychosis and patients with first-episode schizophrenia. Schizophrenia Research, 174, 71-76. Hughes, C. (1998). Executive function in preschoolers: Links with theory of mind and verbal ability. British Journal of Developmental Psychology, 16, 233-253. Insel, T. R., & Young, L. J. (2001). The neurobiology of attachment. Nature Reviews Neuroscience, 2, 129-136. Ivanov, I., Schulz, K. P., London, E. D., & Newcorn, J. H. (2008). Inhibitory control deficits in childhood and risk for substance use disorders: a review. The American Journal of Drug and Alcohol Abuse, 34, 239-258. Jaeggi, S. M., Buschkuehl, M., Perrig, W. J., & Meier, B. (2010). The concurrent validity of the N-back task as a working memory measure. Memory, 18, 394-412. Jewell, T., Collyer, H., Gardner, T., Tchanturia, K., Simic, M., Fonagy, P., & Eisler, I. (2016). Attachment and mentalization and their association with child and adolescent eating pathology: A systematic review. International Journal of Eating Disorders, 49, 354-373. Jurist, E. L. (2005). Mentalized affectivity. Psychoanalytic Psychology, 22, 426. Kahneman, D. (2011). Thinking, fast and slow. Macmillan. 114 Kane, M. J., Brown, L. H., McVay, J. C., Silvia, P. J., Myin-Germeys, I., & Kwapil, T. R. (2007). For whom the mind wanders, and when: An experience-sampling study of working memory and executive control in daily life. Psychological Science, 18, 614-621. Kelly, K., Slade, A., & Grienenberger, J. F. (2005). Maternal reflective functioning, mother– infant affective communication, and infant attachment: Exploring the link between mental states and observed caregiving behavior in the intergenerational transmission of attachment. Attachment & Human Development, 7, 299-311. Kirchner, W. K. (1958). Age differences in short-term retention of rapidly changing information. Journal of Experimental Psychology, 55, 352. Kirkpatrick, L. A., & Hazan, C. (1994). Attachment styles and close relationships: A four‐year prospective study. Personal Relationships, 1, 123-142. Kotov, R., Krueger, R. F., Watson, D., Achenbach, T. M., Althoff, R. R., Bagby, R. M., ... & Eaton, N. R. (2017). The Hierarchical Taxonomy of Psychopathology (HiTOP): a dimensional alternative to traditional nosologies. Journal of Abnormal Psychology, 126, 454. Kovács, Á. M., Téglás, E., & Endress, A. D. (2010). The social sense: Susceptibility to others’ beliefs in human infants and adults. Science, 330, 1830-1834. Kross, E., Ayduk, O., & Mischel, W. (2005). When asking “why” does not hurt distinguishing rumination from reflective processing of negative emotions. Psychological science, 16(9), 709-715. Kross, E., Bruehlman-Senecal, E., Park, J., Burson, A., Dougherty, A., Shablack, H., ... & Ayduk, O. (2014). Self-talk as a regulatory mechanism: how you do it matters. Journal of personality and social psychology, 106, 304. Labouvie-Vief, G. (2009). Cognition and equilibrium regulation in development and aging. Restorative Neurology and Neuroscience, 27, 551-565. Labouvie-Vief, G., Grühn, D., & Studer, J. (2010). Dynamic integration of emotion and cognition: Equilibrium regulation in development and aging. In The Handbook of Life‐ Span Development. Lantrip, C., Isquith, P. K., Koven, N. S., Welsh, K., & Roth, R. M. (2016). Executive function and emotion regulation strategy use in adolescents. Applied Neuropsychology: Child, 5, 50-55. Lawrence, A. J., Luty, J., Bogdan, N. A., Sahakian, B. J., & Clark, L. (2009). Impulsivity and response inhibition in alcohol dependence and problem gambling. Psychopharmacology, 207, 163-172. Lemogne, C., Delaveau, P., Freton, M., Guionnet, S., & Fossati, P. (2012). Medial prefrontal cortex and the self in major depression. Journal of Affective Disorders, 136, e1-e11. 115 Leon-Carrion, J., García-Orza, J., & Pérez-Santamaría, F. J. (2004). Development of the inhibitory component of the executive functions in children and adolescents. International Journal of Neuroscience, 114, 1291-1311. Levens, S. M., & Gotlib, I. H. (2015). Updating emotional content in recovered depressed individuals: Evaluating deficits in emotion processing following a depressive episode. Journal of Behavior Therapy and Experimental Psychiatry, 48, 156-163. Levy, K. N., Meehan, K. B., Kelly, K. M., Reynoso, J. S., Weber, M., Clarkin, J. F., & Kernberg, O. F. (2006). Change in attachment patterns and reflective function in a randomized control trial of transference-focused psychotherapy for borderline personality disorder. Journal of Consulting and Clinical Psychology, 74, 1027. Lieberman, M. D. (2007). Social cognitive neuroscience: a review of core processes. Annual Review of Psychology, 58, 259-289. Lodi-Smith, J., & Roberts, B. W. (2007). Social investment and personality: A meta-analysis of the relationship of personality traits to investment in work, family, religion, and volunteerism. Personality and social psychology review, 11, 68-86. Logan, G. D. (1994). On the ability to inhibit thought and action: A users' guide to the stop signal paradigm. In D. Dagenbach & T. H. Carr (Eds.), Inhibitory processes in attention, memory, and language (p. 189–239). Academic Press. Logan, G. D., Schachar, R. J., & Tannock, R. (1997). Impulsivity and inhibitory control. Psychological Science, 8, 60-64. Luciana, M., Conklin, H. M., Hooper, C. J., & Yarger, R. S. (2005). The development of nonverbal working memory and executive control processes in adolescents. Child Development, 76, 697-712. Luna, B., Garver, K. E., Urban, T. A., Lazar, N. A., & Sweeney, J. A. (2004). Maturation of cognitive processes from late childhood to adulthood. Child Development, 75, 1357- 1372. Luria, A.R. (1966). Higher cortical functions in man. New York: Basic Books Luyten, P., & Fonagy, P. (2014). Mentalising in attachment contexts. In The Routledge handbook of attachment: Theory (pp. 121-140). Routledge. Luyten, P., Mayes, L. C., Sadler, L., Fonagy, P., Nicholls, S., Crowley, M., & Slade, A. (2009). The parental reflective functioning questionnaire-1 (PRFQ-1). Leuven: University of Leuven. Luyten, P., Mayes, L. C., Nijssens, L., & Fonagy, P. (2017). The parental reflective functioning questionnaire: Development and preliminary validation. PloS one, 12, e0176218. 116 Luyten, P., Nijssens, L., Fonagy, P., & Mayes, L. C. (2017). Parental reflective functioning: Theory, research, and clinical applications. The Psychoanalytic Study of the Child, 70(1), 174-199. MacBeth, A., Gumley, A., Schwannauer, M., & Fisher, R. (2011). Attachment states of mind, mentalization, and their correlates in a first‐episode psychosis sample. Psychology and Psychotherapy: Theory, Research and Practice, 84, 42-57. MacLeod, C. M. (1991). Half a century of research on the Stroop effect: an integrative review. Psychological Bulletin, 109, 163. Main, M. (1991). Metacognitive knowledge, metacognitive monitoring, and singular (coherent) vs. multiple (incoherent) models of attachment. Attachment across the life cycle, 127, 159. Main, M., Goldwyn, R., & Hesse, E. (2002). Adult attachment scoring and classification systems, Version 7.1. Unpublished manuscript, University of California at Berkeley. Main, M., Kaplan, N., & Cassidy, J. (1985). Security in infancy, childhood, and adulthood: A move to the level of representation. Monographs of the Society for Research in Child Development, 66-104. Malle, B. F., & Knobe, J. (1997). The folk concept of intentionality. Journal of Experimental Social Psychology, 33, 101-121. Malle, B. F., Knobe, J. M., & Nelson, S. E. (2007). Actor-observer asymmetries in explanations of behavior: New answers to an old question. Journal of Personality and Social Psychology, 93, 491. Malle, B. F., Moses, L. J., & Baldwin, D. A. (2001). Introduction: The significance of intentionality. Intentions and Intentionality: Foundations of Social Cognition, 1-26. Mathersul, D., Palmer, D. M., Gur, R. C., Gur, R. E., Cooper, N., Gordon, E., & Williams, L. M. (2009). Explicit identification and implicit recognition of facial emotions: II. Core domains and relationships with general cognition. Journal of Clinical and Experimental Neuropsychology, 31, 278-291. McAdams, D. P., & Olson, B. D. (2010). Personality development: Continuity and change over the life course. Annual Review of Psychology, 61, 517-542. McConnell, M., & Moss, E. (2011). Attachment across the Life Span: Factors that Contribute to Stability and Change. Australian Journal of Educational & Developmental Psychology, 11, 60-77. McGivern, R. F., Andersen, J., Byrd, D., Mutter, K. L., & Reilly, J. (2002). Cognitive efficiency on a match to sample task decreases at the onset of puberty in children. Brain and Cognition, 50, 73-89. 117 McRae, K., Gross, J. J., Weber, J., Robertson, E. R., Sokol-Hessner, P., Ray, R. D., ... & Ochsner, K. N. (2012). The development of emotion regulation: an fMRI study of cognitive reappraisal in children, adolescents, and young adults. Social Cognitive and Affective Neuroscience, 7, 11-22. Metcalfe, J., & Mischel, W. (1999). A hot/cool-system analysis of delay of gratification: dynamics of willpower. Psychological Review, 106, 3. Meins, E., Fernyhough, C., Wainwright, R., Das Gupta, M., Fradley, E., & Tuckey, M. (2002). Maternal mind–mindedness and attachment security as predictors of theory of mind understanding. Child Development, 73, 1715-1726. Mennin, D. S., & Fresco, D. M. (2013). What, me worry and ruminate about DSM‐5 and RDoC? The importance of targeting negative self‐referential processing. Clinical Psychology: Science and Practice, 20, 258-267. Mikulincer, M., & Florian, V. (1998). The relationship between adult attachment styles and emotional and cognitive reactions to stressful events. Mikulincer, M., & Shaver, P. R. (2007). Attachment in adulthood: Structure, dynamics, and change. Guilford Press. Mikulincer, M., Shaver, P. R., & Pereg, D. (2003). Attachment theory and affect regulation: The dynamics, development, and cognitive consequences of attachment-related strategies. Motivation and Emotion, 27, 77-102. Miyake, A., & Friedman, N. P. (2012). The nature and organization of individual differences in executive functions: Four general conclusions. Current directions in psychological science, 21(1), 8-14. Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41, 49-100. Moran, T. P. (2016). Anxiety and working memory capacity: A meta-analysis and narrative review. Psychological Bulletin, 142, 831. Morel, K., & Papouchis, N. (2015). The role of attachment and reflective functioning in emotion regulation. Journal of the American Psychoanalytic Association, 63, NP15-NP20. Moss, E., Parent, S., & Gosselin, C. (1995, March). Attachment and theory of mind: Cognitive and metacognitive correlates of attachment during the preschool period. In biennial meeting of the Society for Research in Child Development, Indianapolis, IN, March- April. Müller, C., Kaufhold, J., Overbeck, G., & Grabhorn, R. (2006). The importance of reflective functioning to the diagnosis of psychic structure. Psychology and Psychotherapy: Theory, Research and Practice, 79, 485-494. 118 Muller, R. T., Thornback, K., & Bedi, R. (2012). Attachment as a mediator between childhood maltreatment and adult symptomatology. Journal of Family Violence, 27, 243-255. Nelson, J.M., Sheffield, T.D., Chevalier, N., Clark, C.A.C., & Espy, K.A. (2012). Psychobiology of executive function in early development. In Executive Function in Preschool Age Children: Integrating Measurement, Neurodevelopment and Translational Research,(ed. P McCardle, L Freund, JA Griffin). Washington DC: American Psychological Association. Neumann, I. D. (2008). Brain oxytocin: a key regulator of emotional and social behaviours in both females and males. Journal of Neuroendocrinology, 20, 858-865. Nigg, J. T. (2000). On inhibition/disinhibition in developmental psychopathology: views from cognitive and personality psychology and a working inhibition taxonomy. Psychological bulletin, 126, 220. Obeso, I., Wilkinson, L., Casabona, E., Bringas, M. L., Alvarez, M., Alvarez, L., ... & Jahanshahi, M. (2011). Deficits in inhibitory control and conflict resolution on cognitive and motor tasks in Parkinson’s disease. Experimental Brain Research, 212, 371-384. O’Brien, E., Konrath, S. H., Grühn, D., & Hagen, A. L. (2013). Empathic concern and perspective taking: Linear and quadratic effects of age across the adult life span. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 68, 168-175. Ochsner, K. N., & Gross, J. J. (2005). The cognitive control of emotion. Trends in Cognitive Sciences, 9, 242-249. Ordaz, S. J., Foran, W., Velanova, K., & Luna, B. (2013). Longitudinal growth curves of brain function underlying inhibitory control through adolescence. Journal of Neuroscience, 33, 18109-18124. Orvell, A., Kross, E., & Gelman, S. A. (2017). How “you” makes meaning. Science, 355, 1299- 1302. Otto, A. R., Gershman, S. J., Markman, A. B., & Daw, N. D. (2013). The curse of planning: dissecting multiple reinforcement-learning systems by taxing the central executive. Psychological Science, 24, 751-761. Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological bulletin, 124, 54. Park, C. L., Mills, M. A., & Edmondson, D. (2012). PTSD as meaning violation: Testing a cognitive worldview perspective. Psychological Trauma: Theory, Research, Practice, and Policy, 4, 66. 119 Pawliczek, C. M., Derntl, B., Kellermann, T., Kohn, N., Gur, R. C., & Habel, U. (2013). Inhibitory control and trait aggression: neural and behavioral insights using the emotional stop signal task. Neuroimage, 79, 264-274. Pe, M. L., Raes, F., & Kuppens, P. (2013). The cognitive building blocks of emotion regulation: Ability to update working memory moderates the efficacy of rumination and reappraisal on emotion. PloS one, 8, e69071. Pears, K., & Fisher, P. A. (2005). Developmental, cognitive, and neuropsychological functioning in preschool-aged foster children: Associations with prior maltreatment and placement history. Journal of Developmental & Behavioral Pediatrics, 26, 112-122. Peirce, J. W., Gray, J. R., Simpson, S., MacAskill, M. R., Höchenberger, R., Sogo, H., Kastman, E., Lindeløv, J. (2019). PsychoPy2: experiments in behavior made easy. Behavior Research Methods. 10.3758/s13428-018-01193-y. Perner, J., Kain, W., & Barchfeld, P. (2002). Executive control and higher‐order theory of mind in children at risk of ADHD. Infant and Child Development: An International Journal of Research and Practice, 11, 141-158. Perner, J., Lang, B., & Kloo, D. (2002). Theory of mind and self‐control: More than a common problem of inhibition. Child Development, 73, 752-767. Pflueger, M. O., Calabrese, P., Studerus, E., Zimmermann, R., Gschwandtner, U., Borgwardt, S., ... & Riecher-Rössler, A. (2018). The neuropsychology of emerging psychosis and the role of working memory in episodic memory encoding. Psychology Research and Behavior Management, 11, 157. Pievsky, M. A., & McGrath, R. E. (2018). The neurocognitive profile of attention- deficit/hyperactivity disorder: A review of meta-analyses. Archives of Clinical Neuropsychology, 33, 143-157. Pollak, S. D., Cicchetti, D., Hornung, K., & Reed, A. (2000). Recognizing emotion in faces: developmental effects of child abuse and neglect. Developmental Psychology, 36, 679. Premack, D., & Woodruff, G. (1978). Does the chimpanzee have a theory of mind?. Behavioral and Brain Sciences, 1, 515-526. Raikes, H. A., & Thompson, R. A. (2006). Family emotional climate, attachment security and young children's emotion knowledge in a high risk sample. British Journal of Developmental Psychology, 24, 89-104. Reeder, G. D. (1993). Trait-behavior relations and dispositional inference. Personality and Social Psychology Bulletin, 19(5), 586-593. Reville, M. C., O’Connor, L., & Frampton, I. (2016). Literature review of cognitive neuroscience and anorexia nervosa. Current psychiatry reports, 18, 18. 120 Riggs, S. A., Sahl, G., Greenwald, E., Atkison, H., Paulson, A., & Ross, C. A. (2007). Family environment and adult attachment as predictors of psychopathology and personality dysfunction among inpatient abuse survivors. Violence and Victims, 22, 577-600. Roberts, B. W., & Wood, D. (2006). Personality Development in the Context of the Neo- Socioanalytic Model of Personality. In D Mroczek, T Little (eds.) Handbook of Personality Development. Hillsdale, NJ: Erlbaum. Roca, M., Vives, M., López-Navarro, E., García-Campayo, J., & Gili, M. (2015). Cognitive impairments and depression: a critical review. Actas Esp Psiquiatr, 43, 187-93. Rogosch, F. A., Cicchetti, D., & Aber, J. L. (1995). The role of child maltreatment in early deviations in cognitive and affective processing abilities and later peer relationship problems. Development and Psychopathology, 7, 591-609. Rosenblum, K. L., Mcdonough, S. C., Sameroff, A. J., & Muzik, M. (2008). Reflection in thought and action: Maternal parenting reflectivity predicts mind-minded comments and interactive behavior. Infant Mental Health Journal, 29, 362–376. Rosnay, M. D., Harris, P. L., & Pons, F. (2008). Emotional understanding and developmental psychopathology in young children. In C. Sharp, P. Fonagy, & I. Goodyer (Eds.), Social cognition and developmental psychopathology (p. 343–385). Oxford University Press. Rothbart, M. K., & Posner, M. I. (1985). Temperament and the development of self-regulation. In The neuropsychology of individual differences (pp. 93-123). Springer, Boston, MA. Rothschild-Yakar, L., Waniel, A., & Stein, D. (2013). Mentalizing in self vs. parent representations and working models of parents as risk and protective factors from distress and eating disorders. The Journal of Nervous and Mental Disease, 201, 510-518. Rudden, M., Milrod, B., Target, M., Ackerman, S., & Graf, E. (2006). Reflective functioning in panic disorder patients: A pilot study. Journal of the American Psychoanalytic Association, 54, 1339-1343. Rutherford, H. J., Booth, C. R., Crowley, M. J., & Mayes, L. C. (2016). Investigating the relationship between working memory and emotion regulation in mothers. Journal of Cognitive Psychology, 28, 52-59. Rutherford, H. J., Byrne, S. P., Crowley, M. J., Bornstein, J., Bridgett, D. J., & Mayes, L. C. (2018). Executive functioning predicts reflective functioning in mothers. Journal of Child and Family Studies, 27, 944-952. Rutherford, H. J., Goldberg, B., Luyten, P., Bridgett, D. J., & Mayes, L. C. (2013). Parental reflective functioning is associated with tolerance of infant distress but not general distress: Evidence for a specific relationship using a simulated baby paradigm. Infant Behavior and Development, 36, 635-641. 121 Rutherford, H. J., Maupin, A. N., Landi, N., Potenza, M. N., & Mayes, L. C. (2017). Parental reflective functioning and the neural correlates of processing infant affective cues. Social Neuroscience, 12, 519-529. Rutherford, H., Williams, S., Moy, S., Mayes, L., & Johns, J. (2011). Disruption of maternal parenting circuitry by addictive process: rewiring of reward and stress systems. Frontiers in Psychiatry, 2, 37. Sabbagh, M. A., Xu, F., Carlson, S. M., Moses, L. J., & Lee, K. (2006). The development of executive functioning and theory of mind: A comparison of Chinese and US preschoolers. Psychological Science, 17, 74-81. Satpute, A. B., & Lieberman, M. D. (2006). Integrating automatic and controlled processes into neurocognitive models of social cognition. Brain Research, 1079, 86-97. Schmeichel, B. J., & Demaree, H. A. (2010). Working memory capacity and spontaneous emotion regulation: High capacity predicts self-enhancement in response to negative feedback. Emotion, 10, 739. Schmeichel, B. J., Volokhov, R. N., & Demaree, H. A. (2008). Working memory capacity and the self-regulation of emotional expression and experience. Journal of Personality and Social Psychology, 95, 1526–1540. Schore, A.N. (1994). Affect regulation and the origin of the self: The neurobiology of emotional development. Mahwah, NJ: Erlbaum. Schweizer, S., Satpute, A. B., Atzil, S., Field, A. P., Hitchcock, C., Black, M., & Dalgleish, T. (2018). The behavioral and neural effects of affective information on working memory performance: A pair of meta-analytic reviews. Psychological Bulletin. Shallice, T. I. M., & Burgess, P. W. (1991). Deficits in strategy application following frontal lobe damage in man. Brain, 114, 727-741. Sharp, C., & Fonagy, P. (2008). The parent's capacity to treat the child as a psychological agent: Constructs, measures, and implications for developmental psychopathology. Social Development, 17, 737-754. Shaver, P. R., & Mikulincer, M. (2002). Attachment-related psychodynamics. Attachment & Human Development, 4, 133-161. Shipman, K. L., & Zeman, J. (1999). Emotional understanding: A comparison of physically maltreating and nonmaltreating mother-child dyads. Journal of Clinical Child Psychology, 28, 407-417. Shipman, K. L., & Zeman, J. (2001). Socialization of children's emotion regulation in mother– child dyads: A developmental psychopathology perspective. Development and Psychopathology, 13, 317-336. 122 Shipman, K., Zeman, J., Penza, S., & Champion, K. (2000). Emotion management skills in sexually maltreated and nonmaltreated girls: A developmental psychopathology perspective. Development and Psychopathology, 12, 47-62. Simpson, J. A., Rholes, W. S., Campbell, L., Tran, S., & Wilson, C. L. (2003). Adult attachment, the transition to parenthood, and depressive symptoms. Journal of Personality and Social Psychology, 84, 1172. Skowron, E. A., & Dendy, A. K. (2004). Differentiation of self and attachment in adulthood: Relational correlates of effortful control. Contemporary Family Therapy, 26, 337-357. Slade, A. (1999). Attachment theory and research: Implications for the theory and practice of individual psychotherapy with adults. Slade, A. (2005). Parental reflective functioning: An introduction. Attachment & Human Development, 7, 269-281. Slade, A., Aber, J. L., Bresgi, I., Berger, B., & Kaplan (2004). The Parent Development Interview – Revised. Unpublished protocol. The City University of New York. Slade, A., Grienenberger, J., Bernbach, E., Levy, D., & Locker, A. (2005). Maternal reflective functioning, attachment, and the transmission gap: A preliminary study. Attachment & Human Devlopment, 7, 283-298. Smith, E. R., & DeCoster, J. (2000). Dual-process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4, 108-131. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220-247. Strathearn, L., Fonagy, P., Amico, J., & Montague, P. R. (2009). Adult attachment predicts maternal brain and oxytocin response to infant cues. Neuropsychopharmacology, 34(13), 2655-2666. Streamer, L., Seery, M. D., Kondrak, C. L., Lamarche, V. M., & Saltsman, T. L. (2017). Not I, but she: The beneficial effects of self-distancing on challenge/threat cardiovascular responses. Journal of Experimental Social Psychology, 70, 235-241. Struthers, C. W., Eaton, J., Santelli, A. G., Uchiyama, M., & Shirvani, N. (2008). The effects of attributions of intent and apology on forgiveness: When saying sorry may not help the story. Journal of Experimental Social Psychology, 44, 983-992. Target, M., & Fonagy, P. (1996). Playing with reality: II. The development of psychic reality from a theoretical perspective. International Journal of Psycho-Analysis, 77, 459-479. Tasca, G. A., Ritchie, K., Zachariades, F., Proulx, G., Trinneer, A., Balfour, L., ... & Bissada, H. (2013). Attachment insecurity mediates the relationship between childhood trauma and 123 eating disorder psychopathology in a clinical sample: a structural equation model. Child Abuse & Neglect, 37, 926-933. Taubner, S., White, L. O., Zimmermann, J., Fonagy, P., & Nolte, T. (2013). Attachment-related mentalization moderates the relationship between psychopathic traits and proactive aggression in adolescence. Journal of Abnormal Child Psychology, 41, 929-938. Taylor, E. L., Target, M., & Charman, T. (2008). Attachment in adults with high-functioning autism. Attachment & Human Development, 10(2), 143-163. Travis, L. A., Bliwise, N. G., Binder, J. L., & Horne-Moyer, H. L. (2001). Changes in clients' attachment styles over the course of time-limited dynamic psychotherapy. Psychotherapy: Theory, Research, Practice, Training, 38, 149. Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychological Review, 117, 440. Uddin, L. Q., Iacoboni, M., Lange, C., & Keenan, J. P. (2007). The self and social cognition: the role of cortical midline structures and mirror neurons. Trends in Cognitive Sciences, 11, 153-157. Ursache, A., Noble, K. G., & Blair, C. (2015). Socioeconomic status, subjective social status, and perceived stress: Associations with stress physiology and executive functioning. Behavioral Medicine, 41, 145-154. Van Overwalle, F., & Baetens, K. (2009). Understanding others' actions and goals by mirror and mentalizing systems: a meta-analysis. Neuroimage, 48, 564-584. Vanderhasselt, M. A., Baeken, C., Van Schuerbeek, P., Luypaert, R., & De Raedt, R. (2013). Inter-individual differences in the habitual use of cognitive reappraisal and expressive suppression are associated with variations in prefrontal cognitive control for emotional information: an event related fMRI study. Biological Psychology, 92, 433-439. Verbruggen, F., Aron, A. R., Band, G. P., Beste, C., Bissett, P. G., Brockett, A. T., ... & Colzato, L. S. (2019). A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. Elife, 8, e46323. Verbruggen, F., McLaren, I. P., & Chambers, C. D. (2014). Banishing the control homunculi in studies of action control and behavior change. Perspectives on Psychological Science, 9, 497-524. Verdejo-Garcia, A. (2016). Cognitive training for substance use disorders: Neuroscientific mechanisms. Neuroscience & Biobehavioral Reviews, 68, 270-281. Verhaeghen, P., & Salthouse, T. A. (1997). Meta-analyses of age–cognition relations in adulthood: Estimates of linear and nonlinear age effects and structural models. Psychological Bulletin, 122, 231. 124 Verhaeghen, P., & Zhang, Y. (2013). What is still working in working memory in old age: Dual tasking and resistance to interference do not explain age-related item loss after a focus switch. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 68(5), 762-770. Waechter, S., Moscovitch, D. A., Vidovic, V., Bielak, T., Rowa, K., & McCabe, R. E. (2018). Working memory capacity in social anxiety disorder: Revisiting prior conclusions. Journal of Abnormal Psychology, 127, 276. Wagner, G., Schachtzabel, C., Peikert, G., & Bär, K. J. (2015). The neural basis of the abnormal self‐referential processing and its impact on cognitive control in depressed patients. Human Brain Mapping, 36, 2781-2794. Walsh, M. M., & Anderson, J. R. (2014). Navigating complex decision spaces: Problems and paradigms in sequential choice. Psychological Bulletin, 140, 466. Ward, A., Ramsay, R., Turnbull, S., Steele, M., Steele, H., & Treasure, J. (2001). Attachment in anorexia nervosa: A transgenerational perspective. British Journal of Medical Psychology, 74, 497-505. Waters, E., Merrick, S., Treboux, D., Crowell, J., & Albersheim, L. (2000). Attachment security in infancy and early adulthood: A twenty‐year longitudinal study. Child Development, 71, 684-689. Widom, C. S. (1999). Posttraumatic stress disorder in abused and neglected children grown up. American Journal of Psychiatry, 156, 1223-1229. Wimmer, H., & Perner, J. (1983). Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children's understanding of deception. Cognition, 13, 103-128. Wright, L., Lipszyc, J., Dupuis, A., Thayapararajah, S. W., & Schachar, R. (2014). Response inhibition and psychopathology: A meta-analysis of go/no-go task performance. Journal of Abnormal Psychology, 123, 429. Yang, Y., Cao, S., Shields, G. S., Teng, Z., & Liu, Y. (2017). The relationships between rumination and core executive functions: A meta‐analysis. Depression and Anxiety, 34, 37-50. Yüksel, D., Dietsche, B., Konrad, C., Dannlowski, U., Kircher, T., & Krug, A. (2018). Neural correlates of working memory in first episode and recurrent depression: an fMRI study. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 84, 39-49. Zhang, F., & Labouvie-Vief, G. (2004). Stability and fluctuation in adult attachment style over a 6-year period. Attachment & Human Development, 6, 419-437. Zelazo, P. D., Craik, F. I., & Booth, L. (2004). Executive function across the life span. Acta psychologica, 115(2-3), 167-183. 125 Zelazo, P.D., & Cunningham, W. (2007). Executive function: Mechanisms underlying emotion regulation. In J.J. Gross (Ed.), Handbook of emotion regulation (pp. 135–158). New York: Guilford. 126