A PSYCHONEUROMETRIC APPROACH TO INDEXING REWARD SENSITIVITY 

By 

Grace E. Anderson 

A THESIS 

Submitted to 
Michigan State University 
in partial fulfillment of the requirements   
for the degree of 

Psychology – Master of Arts 

2024 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ABSTRACT 

The National Institute of Mental Health has proposed the Research Domain Criteria 

(RDoC) framework as a dimensional approach to evaluating and defining components of 

psychopathology. The current RDoC matrix is comprised of several systems, including the 

positive valence system, which encompasses reward sensitivity, which can be further separated 

into temporal phases of reward anticipation and initial response to reward. The current RDoC 

matrix includes limited measures within reward sensitivity. As such, reward sensitivity is an 

ideal variable for the psychoneurometric approach, which aims to integrate multiple units of 

analysis to operationalize latent variables. An exploratory factor analysis (EFA) was used to 

integrate four electroencephalographic variables (SPN, P300, RewP amplitudes and delta 

power), a behavioral measure (reaction time), and self-report measures of personality traits and 

mood symptoms often associated with reward processing like impulsivity, anhedonia, and 

valuation. Although hypotheses included a two-factor solution of reward anticipation and reward 

response, results revealed a six-factor solution, explaining 66.031% of the variance. Factors 

seemed to reflect: the P-factor/Questionnaire Methods Variance, Trait-Level Approach 

Motivation/Task Disengagement, General Reward Sensitivity, Excitement Seeking, Impulsivity, 

and State-Dependent Response to Reward. The separation of hypomanic symptoms (motivation, 

excitement seeking, and impulsivity) from a broader reward sensitivity factor demonstrate that 

motivation, excitement seeking, and impulsivity may be candidates for separate subconstructs of 

Reward Responsiveness in the RDoC matrix. Most factors were comprised of either self-report 

or neurophysiological indices rather than integrating across units of analysis. Results highlight 

the need for future studies to include fewer variables coming from a variety of methods rather 

than oversaturating models with questionnaire data, potentially even developing new scales to 

index latent constructs or calculate composite scores before conducting factor analyses.

 
 
 
 
 
 
 
 
 
 
 
TABLE OF CONTENTS 

Introduction………………………………………………………………………………………..1 

Methods……………………………………………………………………………..……………..8 

Results……………………………………………………………………………………………14 

Discussion………………………………………………………………………………………..19 

BIBLIOGRAPHY………………………………………………………………………………..23 

APPENDIX ……………………………………………………………………………………...29

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Introduction 

Understanding mechanisms involved in different domains of psychological functioning is 

imperative for accurate diagnosis and effective intervention of psychopathology. Currently, the 

fifth iteration of the Diagnostic and Statistical Manual of Mental Disorders (DSM; American 

Psychological Association, 2013) focuses on presenting symptoms, impeding research efforts  

and leading to heterogeneous categories that may not reflect distinct diseases (Cuthbert, 2022). 

To address some of the shortcomings of the current DSM diagnostic categories, the National 

Institute of Mental Health (NIMH) has proposed the Research Domain Criteria (RDoC) project 

as a way to redefine psychopathology and mental health problems using a dimensional 

framework focused on neurobehavioral mechanisms involved in individual differences in 

psychological functioning (Cuthbert, 2022). The initiative organizes symptomatology on 

continuous process-oriented spectra (Yancey et al., 2016). Although the RDoC framework is 

designed to challenge the DSM, it has considerable limitations, including an excessive focus on 

behavioral mechanisms and insufficient psychometric validity (Lilienfeld, 2014). To address 

both limitations, the psychoneurometric approach has been proposed to integrate physiological 

and self-report measures to index dispositional (i.e., individual differences) constructs of 

psychopathology like those found in the RDoC matrix (Venables et al., 2017; Yancey et al., 

2016). 

Within the RDoC matrix are five broad systems (negative valence, positive valence, 

cognitive, social processes, arousal and regulatory, and sensorimotor) intended to reflect 

processes involved in particular contexts like, in the case of the positive valence system, 

rewarding situations (About RDoC, n.d.). The positive valence system consists of three general 

reward dimensions: reward responsiveness or sensitivity (hereon referred to as reward 

sensitivity), reward learning, and reward valuation (About RDoC, n.d.). Since dysfunction in 

reward sensitivity is characteristic of several kinds of psychopathology, particularly mood 

disorders like Major Depressive Disorder (MDD) and Bipolar Disorder (BD), the current project 

focused on reward sensitivity. The RDoC matrix defines reward sensitivity as the processes 

underlying hedonic response to upcoming or anticipated reward (reward anticipation), the initial 

response to reward, and reward satiation following repeated exposure to rewarding stimuli 

(About RDoC, n.d.). Notably, the current RDoC matrix is lacking in units of analysis to index 

reward sensitivity. Although RDoC frames reward sensitivity as three subprocesses, the current 

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project focused on addressing the two temporal phases of reward sensitivity: reward anticipation 

and the initial response to reward. 

One prominent theory of the etiology of mood disorders is the Reward Sensitivity Model, 

which proposes that mood disorders manifest predominantly as dysregulations of reward 

responses. It posits that reward hypersensitivity (i.e., excessive motivation towards rewarding 

stimuli) can lead to hypomanic/manic symptoms in BD, whereas reward hyposensitivity (i.e., 

blunted response to rewarding stimuli) can lead to depressive symptoms in both MDD and BD 

(Alloy et al., 2016). Integrating the neural, personality, and psychological processes related to 

reward sensitivity is critical to operationalizing reward sensitivity as a dispositional construct 

(i.e., through a psychoneurometric lens), which will inform both the RDoC matrix and the 

Reward Sensitivity Model. Therefore, the goal of the current project was to integrate measures of 

reward sensitivity from a psychoneurometric perspective, including several self-report measures 

of reward sensitivity as well as associated mood symptoms (e.g., anhedonia) and personality 

traits (e.g., boldness, impulsivity), a behavioral measure of reward choice reaction time, and four 

electroencephalographic (EEG) indices of reward processing derived from a randomized 

guessing task: the Stimulus-Preceding Negativity (SPN), the Reward Positivity (RewP), the P300 

(following rewarding and loss feedback), and delta power.  

EEG markers of reward processing can be indexed using event-related potentials (ERPs; 

e.g., SPN, RewP, P300) or time-frequency analysis (e.g., delta power). ERPs are time-locked 

neurophysiological markers, meaning they quantify activity following a response to a stimulus. 

Whereas ERPs provide millisecond (ms) precision, some valuable information is lost when 

averaging stimulus and trial-related information. This information is still accessible through 

time-frequency analysis, which reveals additional task-relevant dynamics and mechanisms. By 

pairing ERP and frequency data, researchers can assess highly precise temporal information 

about responses to specific stimuli and information regarding neural networks during task 

performance, respectively (Cohen, 2014). Indexing reward sensitivity by pairing ERPs and time-

frequency analysis with self-report measures will be instrumental given the temporal nature of 

reward processing that is thought to be comprised of several subprocesses. 

Reward Anticipation 

RDoC defines reward anticipation as mechanisms related to anticipating or representing 

future rewarding stimuli through language, behavior, and neural responses to future incentives  

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(About RDoC, n.d.). In the current matrix, no molecules, circuits, behavior, self-report measures, 

or paradigms are associated with reward anticipation, making it an ideal subconstruct to 

operationalize through the psychoneurometric approach. The current study integrates self-report 

measures assessing reward processing and associated mood symptoms (through loss of interest 

and low energy as well as other depressive symptoms), impulsivity (through fun-seeking, 

boldness, and externalizing symptoms), and reward hypersensitivity (through hypomania, drive, 

and reward responsiveness) with a behavioral measure (i.e., reaction time) and an EEG index of 

anticipatory and preparatory processes (the Stimulus-Preceding Negativity) to operationalize 

reward anticipation.  

Anhedonia, a key symptom of depression, is a decrease in motivation, interest, or 

pleasure related to a stimulus the individual once found rewarding (Treadway & Zald, 2011).  

Anhedonia is characterized by deficits in both anticipatory (i.e., motivation to pursue) and 

outcome (i.e., interest, liking) aspects of reward, meaning anhedonia could act as an index of 

both reward anticipation and response (Treadway & Zald, 2011; Chen et al., 2018; Bowyer et al., 

2022). The lack of motivation to pursue rewarding stimuli can serve as an index of anticipatory 

reward processes. In contrast, the consummatory aspects (i.e., losing interest in previously 

pleasurable stimuli) could reflect an initial response to reward. Therefore, anhedonia could be 

related to both reward anticipation and response. Particularly, low energy (i.e., motivation to 

pursue) could be related to reward anticipation while loss of interest (i.e., consummatory aspect) 

could be related to initial response to reward. 

In contrast to depressive symptoms, impulsivity, a defining feature of mania and 

hypomania, is maladaptive over-engagement in pleasurable activities (i.e., hypersensitivity to 

reward), which are often unsafe (Giovanelli et al., 2013). Impulsivity represents a maladaptive 

increase in motivation to pursue future rewarding stimuli, acting as an approximation to reward 

hypersensitivity and, thus, reward anticipation. An individual’s level of impulsivity can be 

considered a personality trait as well as a state; however, the current study will focus on trait-

level impulsivity. Trait-level impulsivity has been demonstrated to be associated with suicidal 

behaviors, substance abuse, eating disorders, anti-social personality disorder, and borderline 

personality disorder (McHugh et al., 2019; Acton, 2003; Claes et al., 2005; Swann et al., 2009; 

Mortensen et al., 2010). These findings highlight the necessity of characterizing the role of 

impulsivity in reward sensitivity as it relates to internalizing mood disorders (e.g., MDD and 

3 

 
 
 
 
BD) and correlated externalizing coping strategies and symptoms (e.g., excitement seeking, 

boldness). As impulsivity can be considered an excessive motivation to pursue rewarding stimuli 

(in contrast to anhedonia), impulsivity will likely be a marker of exaggerated reward 

anticipation.  

Behavioral measures are an integral part of a psychoneurometric approach, as they 

provide insight into the functional outcomes associated with dispositional traits. Reaction time 

could serve as an indicator of reward anticipation by quantifying an individual’s impulsivity or 

cautiousness when responding in a monetary reward task (Li et al., 2020). If an individual 

responds faster, it may indicate impulsivity, a trait-level indicator for reward anticipation. 

However, if an individual demonstrates slower reaction times, it may indicate more cautiousness 

or less sensitivity to the prospect of reward. Thus, incorporating reaction time into the 

psychoneurometric operationalization of reward sensitivity will demonstrate how these 

personality traits and neural markers implicate consequential behavior during cognitive tasks.  

The Stimulus Preceding Negativity (SPN) serves as a direct neurophysiological measure 

of reward anticipation, particularly the substage of feedback anticipation, which will be 

imperative to incorporate into the psychoneurometric operationalization of reward anticipation. 

The SPN occurs within a 200 ms interval prior to feedback receipt and has been demonstrated to 

be related to anticipatory attention, such that it is elevated (i.e., more negative) prior to positive 

feedback (Kotani et al., 2001). The SPN is especially valuable in assessing passive anticipation 

of monetary incentives, making it an ideal anticipation index during a randomized guessing task 

resulting in monetary gains (Glazer et al., 2018; Knutson & Greer, 2008). Since the task used in 

the current study is a randomized guessing task, the SPN will serve as an indicator of the extent 

to which individuals anticipate outcomes.  

Initial Response to Reward 

RDoC describes the initial response to reward as processes caused by the initial 

presentation of rewarding stimuli, demonstrated through language, behavior, and 

neurophysiological activity (About RDoC, n.d.). Unlike reward anticipation, RDoC has laid out 

several underlying mechanisms of the initial response to reward, including molecules like 

endocannabinoids, circuits involving the nucleus accumbens, behaviors like taste reactivity, self-

report measures including the Positive and Negative Affect Schedule, and simple guessing task 

paradigms such as the one used in the current project (About RDoC, n.d.). Beyond self-report 

4 

 
 
 
 
measures of affect, measures of valuation, the process of assigning salience to rewarding stimuli, 

is a crucial indicator of the initial hedonic response to reward. Reward valuation is its own 

subconstruct in the RDoC positive valence systems; however, it is highly related to reward 

response as it indicates how individuals make meaning of rewarding stimuli upon initial receipt, 

assessed through measures of what kinds of rewards individuals attend to and find attractive. 

Including valuation in the operationalization of reward response will capture hedonic response to 

specific stimuli, whereas the current RDoC matrix conceptualizes valuation solely in terms of 

decision-making. In addition to a self-report measure of valuation (i.e., reward preference), I 

utilized three EEG components to define initial response to reward: the reward positivity (RewP) 

ERP, the P300 ERP, and delta frequency power. 

Valuation, or assigning importance based on representations of the environment before 

receiving feedback, can be assessed by evaluating what kinds of stimuli are rewarding to 

individuals (Hélie et al., 2017). When faced with multiple decisions, individuals use valuation to 

make selections based on available information regarding what they expect will be most 

rewarding (Montague & Berns, 2002). These valuation decisions are specific to individuals and 

their context. Although valuation can be used for reward learning and future decision-making, it 

is rooted in the initial response to reward by appraising hedonic response to specific rewarding 

stimuli. For instance, valuation represents whether an individual finds a stimulus rewarding and, 

if so, how rewarding. Explicitly assessing valuation will provide insight into the kinds of rewards 

eliciting more positive responses in individuals. Integrating self-report valuation measures with 

EEG reward response markers allowed me to index both the type of stimuli and the extent to 

which stimuli are rewarding to individuals. 

The Reward Positivity, or RewP, is an ERP occurring at approximately 250 to 350 ms at 

frontocentral locations following feedback and tends to be larger following rewards than losses 

(Mackin et al., 2023; Moser et al., 2018; Holroyd et al., 2008). The RewP has been demonstrated 

to be related to individual differences in reward evaluation, reward salience, reward learning and 

anhedonia such that blunted reward responses are associated with blunted RewP amplitude 

(Glazer et al., 2018; Whitton et al., 2016; Liu et al., 2014; Bress et al., 2013; Hager et al., 2022). 

Given these findings linking RewP amplitude with several facets of reward appraisal (e.g., 

salience, learning, evaluation), using the RewP as a measure of the initial response to reward 

receipt will be valuable. Regarding the task utilized in the current project, RewP amplitude 

5 

 
 
 
 
 
should be, on average, larger following reward trials than loss trials. Still, it should also track 

with individual differences in personality traits and mood symptoms. 

The P300 is an ERP occurring approximately 300 ms following the presentation of task-

relevant stimuli (Sara et al., 1994). Previously, the RewP and the P300 have been used together 

to study reward magnitude, demonstrating that P300 amplitude is positively associated with 

reward magnitude (Sato et al., 2005). The P300’s relationship with reward magnitude 

exemplifies its relevance to the initial response to reward. Further, individuals who report 

increased trait levels of anhedonia tend to demonstrate blunted P300 amplitude (Santopetro et al., 

2022). Including the RewP and the P300 will further our understanding of individual differences 

in reward sensitivity, as each component reflects somewhat different aspects of reward 

processing. The feedback-related P300 has previously been implicated in reward processes 

reflecting motivational salience and affective processes of reward (Donchin, 1981; San Martín, 

2012). Outside of reward tasks, the P300 is used to indicate attention and learning (Polich, 1986). 

Thus, the RewP seems to be clearly reflective of reward response and individual differences in 

reward sensitivity, whereas the P300 may demonstrate a more nuanced relationship to individual 

differences in reward sensitivity. The current study will enable a closer look at how these 

different neurophysiological mechanisms reflect individual differences in reward appraisal and 

sensitivity. However, given evidence that the P300 is modulated by rewarding feedback and is 

associated with individual differences in reward sensitivity, I expect it to reflect the initial 

response to reward.  

The delta frequency (<3 Hz), which modulates RewP and P300 amplitude, has been 

implicated in several aspects of reward processing in both the anticipatory and response phases 

(Bernat et al., 2007). Regarding initial response to reward, delta power has been positively 

associated with reward learning and performance evaluation as well as monetary rewards, and it 

has been negatively associated with depressive symptoms (Cavanagh, 2015; Foti et al., 2015). 

Delta has been shown to modulate P300 activity, particularly following monetary gains (Bernat 

et al., 2015; Bernat et al., 2011). Like the P300, delta power has been associated with the 

appraisal of reward magnitude following receipt, demonstrating its relevance to the initial 

response to reward (Bernat et al., 2015). Further, blunted delta response elicited by the same 

randomized guessing task used in the current project has been demonstrated to predict the onset 

of MDD (a disorder characterized by decreased reward response), and delta-modulated RewP 

6 

 
 
 
 
activity following reward (Nelson et al., 2018). Due to delta power’s established relationship 

with reward outcome and response as well as its relationship to ERPs representing reward 

response (RewP and P300), it will be critical to include delta power in a psychoneurometric 

model of the initial response to reward. 

Aims and Hypothesis  

The primary aim of the current project was to operationalize reward sensitivity as a 

psychoneurometric composite by integrating self-report, behavioral, and neurophysiological data 

using the approach described in Yancey et al. (2016). See Figure 1 for an outline of the proposed 

psychoneurometric operationalization of reward sensitivity. 

I hypothesized that once all self-report, behavioral, and neurophysiological data were 

integrated, two subfactors of reward sensitivity would emerge representing phases of reward: 

reward anticipation and the initial response to reward. Reward anticipation would be indexed by 

self-report measures of anhedonia and impulsivity, reaction time, and the SPN ERP. A valuation 

self-report measure (e.g., the reward sensitivity subscale of the Sensitivity to Punishment and 

Sensitivity to Reward Questionnaire, which assesses the types of experiences an individual finds 

rewarding), and several EEG components (the RewP ERP, the P300 ERP, and delta power) 

would index initial response to reward. Anhedonia would be cross-loaded between both 

subfactors of reward sensitivity. 

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Participants 

Methods 

Participants were recruited from Lansing, Michigan to participate in an investigation of 

familial transmission of neurobehavioral liabilities for internalizing and externalizing pathways 

to substance use problems (see Moser et al., 2018). The current study focused on the adult 

parents who participated in the more extensive study. Participants were recruited through the 

Michigan Longitudinal Study (MLS), a longitudinal study assessing the familial risk of 

neurobehavioral liabilities associated with risk for substance use disorders (SUDs), and 

Craigslist. Participants received $75 cash. A subset of participants with useable data for the 

current purposes represented the final sample (N = 110 adults, 63.64 % female, 39.09% MLS 

Sample). See Table 1 for demographic information including age and racial background.  

Guessing Task Procedure 

While undergoing electroencephalography (EEG) recording, participants completed a 

guessing task, also referred to as the Doors Task, in which they were presented with an image of 

two doors on a computer screen. Participants were asked to select which door they believed hid a 

monetary reward, using keyboard buttons “C” to choose the left door and “N” to choose the right 

door. The doors remained on the screen either until the participant selected a door or 4000 ms 

had passed. After making a choice, participants were presented with a fixation cross (+) in the 

center of the screen for 1000 ms, with feedback following for 2000 ms. Participants were told 

they would either gain $0.50 (which was indicated by a green “­”) or lose $0.25 (which was 

indicated by a red “¯”). Following feedback, participants were instructed to press the spacebar to 

begin the next trial. The likelihood of winning a trial was 50%, regardless of the actual choice 

made by the participant, and the participants were naïve to the random nature of the task. First, 

there was a practice block of 10 trials (5 gain trials, 5 loss trials) to ensure participants 

understood the task. After the practice block, participants completed 6 blocks of 10 trials each. 

At the end of each block, participants received information about the money earned in the game 

up until that point. 

Electroencephalography Recording and Data Processing 

EEG recording was collected continuously by the ActiveTwo system (BioSemi, 

Amsterdam, The Netherlands) from 64 Ag-AgCl electrodes embedded in a stretch-lycra cap 

according to the 10/20 system. Two electrodes were placed on the left and right mastoids as 

8 

 
 
 
 
 
 
reference electrodes. Electrooculogram activity (i.e., eyeblink and movements) was recorded 

with three electrodes placed inferior to the left pupil and on the left and right outer canthi and at 

FP1. The Common Mode Sense active electrode and Driven Right Leg passive electrode formed 

the ground during acquisition. All signals were digitized at 1024 Hz via BioSemi’s ActiView 

software. EEG processing and analysis were performed using BrainVision Analyzer 2 

(BrainProducts, Gilching, Germany). Scalp electrodes were re-referenced to the numeric mean of 

the mastoids and bandpass filtered with cutoffs of 0.1 and 30 Hz (12 dB/oct roll-off). Ocular 

artifacts were corrected using the method developed by Gratton et al. (1983). Physiologic 

artifacts were rejected if they met the following criteria (detected by a computer-based 

algorithm): a voltage step exceeding 50 µV between contiguous sampling points, a voltage 

difference of >200 µV within a trial, or a maximum voltage difference of <0.5 µV within a trial.  

Reaction Time 

Reaction time was recorded in milliseconds and was locked to when participants chose 

between the two doors.  

Reward Anticipation Questionnaires 

Before the guessing task, participants completed a battery of self-report measures related 

to several aspects of risk for psychopathology. Participants’ data was considered useable if they 

responded to at least 80% of items. For each measure, mean imputation was used for missing 

items. 

Global Behavior Inventory (GBI)   

The GBI is a 73-item inventory of behaviors contributing to the risk for depressive and 

bipolar disorders (Depue et al., 1981). Each item is scored on a four-point Likert scale from 1 

(Never or Hardly Ever) to 4 (Very Often or Almost Constantly). An example item is: Have there 

been periods lasting several days or more when you lost almost all interest in people close to you 

and spent long times by yourself? Per Pendergast et al. (2015), there are three mood factors 

evaluated on the measure: total depressive symptoms, total hypomanic symptoms, total biphasic 

(cross-loaded) symptoms. Depressive symptoms include feeling sad, hopelessness, loss of 

interest, low energy/anhedonia, sleep disturbance, cognitive disturbance (down), depressive 

irritable mood, guilt, somatic symptoms, atypical features, and sad appearance. Hypomanic 

symptoms include increased energy, elevated mood, high drive, rage, cognitive disturbance (up), 

and grandiosity. Biphasic symptoms include mood never in the middle and extremes of mood 

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and energy. Loss of interest, low energy/anhedonia, and high drive were included in the current 

analysis to proxy anhedonia and increased drive.  

BAS-BIS  

The BAS-BIS is a 24-item measure with two subscales assessing the Behavioral 

Activation System (BAS) and the Behavioral Inhibition System (BIS). BAS items, which will be 

used in the proposed study, have three subscales for drive, fun-seeking, and reward 

responsiveness (Carver & White, 1994). Each item is measured on a four-point Likert scale from 

1 (Very true for me) to 4 (Very false for me). An example item is: When I’m doing well at 

something I love to keep at it. Scores from each subscale – drive, fun-seeking, and reward 

responsiveness – were used in the current study to index motivation and traits associated with 

trait impulsivity like fun-seeking and reward responsiveness. 

Boldness Inventory 

The Boldness Inventory is a 19-item excerpt from the Triarchic Psychopathy Measure, 

measuring boldness in interpersonal behavior, emotional experience, and venturesomeness 

(Patrick et al., 2009). Each item is scored on a four-point Likert scale from 1 (False) to 4 (True). 

An example item is: I never worry about making a fool of myself with others. Total boldness was 

used to index boldness, a personality trait similar to fearlessness, which is associated with trait 

impulsivity (Patrick et al., 2009).  

Externalizing Spectrum Inventory Brief From (ESI-bf) 

The ESI-bf is a 160-item inventory of experiences characteristic of dysfunctional impulse 

control (Patrick et al., 2013). Each item is scored on a four-point Likert scale from 1 (True) to 4 

(False). An example item is: I get in trouble for not considering the consequences of my actions. 

Subscales include problematic impulsivity, irresponsibility, theft, fraud, impatient urgency, lacks 

planful control, lacks dependability, alienation, boredom proneness, blaming external factors, 

lacks honesty, rebelliousness, physical aggression, destructive aggression, relational aggression, 

lacks empathy, excitement seeking, cannabis use, cannabis problems, alcohol use, and alcohol 

problems. These subscales load onto general disinhibition, callous aggression, substance abuse, 

and total externalizing symptoms. Problematic impulsivity, impatient urgency, lacks planful 

control, excitement seeking, and boredom proneness were used in the current study to measure 

impulsivity.  

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Hypomanic Personality Scale (HPS). The HPS is a 45-item measure with three 

subscales for social vitality, mood volatility, and excitement. The measure intends to identify 

personality traits often associated with hypomanic symptoms and individuals at-risk for bipolar 

disorders (Eckblad & Chapman, 1986). All items are scored as either True or False. An example 

is: I often feel excited and happy for no apparent reason.  The excitement subscale was used in 

the current study as an index of hypomanic drive or trait-level impulsivity. 

Reward Anticipation Neurophysiology 

SPN. The stimulus-locked SPN is time-locked to the onset of feedback regardless of gain 

or loss feedback at midline occipital location Oz (Moser et al., 2009; Bowyer et al., 2022). The 

SPN has several subcomponents, but this project uses the late SPN, which indexes anticipation of 

imperative stimuli. To capture anticipation, the SPN was baseline corrected from -1200 to -1000 

ms. Then, the SPN was calculated as the average amplitude of the waveform in the 200 ms 

window preceding the feedback presentation, as defined in (Glazer et al., 2018), to reflect 

anticipation of feedback following the selection of the door.  

Initial Response to Reward Questionnaires 

Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ). The 

SPSRQ is a 39-item measure with two subscales: sensitivity to punishment, which is based on 

the BIS system, and sensitivity to reward, which is based on the BAS system (Torrubia et al., 

2001). The original version of the SPSRQ has 48 items, but this project used a short form with 

39 items. Because there is no published work on the validity of this short form, response 

averages were used for the SPSRQ score rather than totals. All items are scored as either Yes (1) 

or No (0). An example item is: Do you often do things to be praised? The sensitivity to reward 

subscale was used to measure valuation. 

Initial Response to Reward Neurophysiology 

All three neurophysiological indices of reward response (RewP amplitude, P300 

amplitude, delta power) were baseline corrected from -200 to 800 ms, locked to the onset of 

feedback. 

RewP 

As per previous literature, the stimulus-locked RewP is defined as the average amplitude 

between 215-315 ms at the midfrontal electrode FCz following the onset of the green or red 

arrow feedback stimuli (Moser et al., 2018; Mackin et al., 2023; Santopetro et al., 2021). Reward 

11 

 
 
 
 
 
 
 
and loss feedback waveforms were averaged separately, and the RewP was calculated by 

subtracting loss amplitude from reward amplitude (i.e., gain trials average waveform – loss trials 

average waveform) (Holroyd et al., 2008; Mackin et al., 2023; Santopetro et al., 2021). 

P300 

The gain-related P300 was used in this study as an indicator of the initial response to 

reward.  Thus, the P300 will be calculated following procedures for the feedback-P300 described 

in Santopetro et al. (2021). The P300 will be calculated as the average activity from 300 ms to 

500 ms following the onset of gain feedback (the green arrow) at central-parietal electrode 

location CPz.    

Delta Power 

Similar to the procedure outlined in Nelson et al. (2018), the feedback-locked ERP at FCz 

was averaged separately for trials presenting gain feedback and loss feedback were decomposed 

into a time-frequency metric using Morlet wavelets (with a Morlet Parameter equal to 5) which 

increased 20 logarithmic steps from .05 to 20 Hz to capture low-frequency delta activity. To 

estimate frequency band-specific power at each time point, the absolute value of the signal was 

squared (Nelson et al., 2018). Like the feedback-related ERPs, power was baseline corrected 

from -200 to 800 ms, locked to the onset of feedback. Then, the difference was calculated 

between gain and loss trials. Delta was captured as activity within the 1.6-4.13 Hz range from 50 

to 250 ms. This time window was selected as the maximum gain-loss delta power difference 

following the onset of feedback.  

Analyses 

All analyses were completed in IBM SPSS Statistics (Version 28; IBM Corp., 2023) and 

R (R Foundation for Statistical Computing, Vienna, Austria., 2022). Statistical analysis followed 

the approach outlined in (Yancey et al., 2016).  First, bivariate correlation analyses were 

calculated to assess relationships between all variables. Then, an exploratory factor analysis, 

using principal components analysis, with a promax rotation was used to test the proposed 

psychoneurometric reward sensitivity dimensions. Promax rotation was used to account for any 

potential correlations between factors. Specifically, I hypothesized there would be a two-factor 

solution: one factor representing reward anticipation metrics and the other representing the initial 

response to reward metrics (see Figure 1 for hypothesized organization of measures). Anhedonia 

12 

 
 
 
 
 
would cross-load between the two factors such that there is an equivalent loading score for 

anhedonia metrics on both reward anticipation and response to reward dimensions. 

13 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Descriptive Statistics  

Results 

See Figure 2 for the SPN average amplitude waveform at Oz and headmap. As previously 

discussed, the late SPN does not capture the entire negativity leading up to the onset of feedback, 

but rather, the 200 ms immediately preceding feedback to reflect the later stages of anticipation. 

This negativity was most predominant at occipital location Oz (similar to Bowyer et al., 2022). 

Average SPN amplitude was -2.810 µV (SD = 3.303). A one-sample t-test indicated SPN 

amplitude was significantly less than 0, validating the enhancement of the negative-going ERP 

just prior to feedback, t(109) = -8.921, p < .001. 

See Figure 3 for the RewP average amplitude waveform at FCz and headmap. As 

demonstrated in the figure, the difference between gain and loss trials was maximal from 215-

315 ms at the midfrontal electrode FCz. Average RewP amplitude (the difference between gain 

and loss trials) was 1.804 µV (SD = 2.921). A one-sample t-test demonstrated RewP amplitude 

was significantly greater than 0, validating larger positivity following gains than losses, t(109) = 

6.479, p < .001. 

See Figure 4 for the gain-related P300 average amplitude waveform at CPz and headmap. 

The P300 peaked around 300 to 500 ms following the onset of gain feedback with an average 

amplitude of 11.855 µV (SD = 5.231). A one-sample t-test demonstrated the P300 amplitude was 

significantly greater than 0, validating the prominent positive going wave following gain 

feedback, t(109) = 23.768, p < .001. 

See Figure 5for the time-frequency plot depicting the difference between gain and loss 

trials in the delta frequency at FCz. The average difference between the two trial types was .854 

µV2/Hz but there was a large amount of variance in this difference (SD = 16.947). A one-sample 

t-test indicated the difference between trial types was not significantly different from 0 (t(109) = 

.529, p = .598). Although these findings indicate the difference between gain and loss delta 

power may not be the optimal indicator for delta power, further analyses were run with and 

without delta power. There were no meaningful differences between the two EFAs and, as such, 

delta power remained in the model. 

See Table 2 for mean values and standard deviations for each neurophysiological, self-

report, and behavioral index.  

14 

 
 
 
 
 
 
 
 
 
Bivariate Correlations  

See Table 2 for the bivariate correlations between the neurophysiological and behavioral 

indices with self-report measures.  

Although the neurophysiological and behavioral indices were not related to any of the 

self-report measures, there were notable correlations amongst the neurophysiological and 

behavioral variables themselves. SPN amplitude was significantly negatively correlated with 

RewP (r = -.216, p < .05) and P300 (r = -.280, p < .05) amplitudes. Since the SPN is a negatively 

deflecting ERP, and the RewP and P300 are positively deflecting ERPs, these relationships 

indicate that increased anticipation (i.e., more negative SPN amplitude) was related to increased 

reward response (i.e., more positive RewP and P300 amplitudes). SPN amplitude was also 

significantly positively correlated with reaction time (r = .260, p < .05), such that increased 

anticipation to feedback was related to slower selection between the doors.  

In addition to its relationship with SPN amplitude, RewP amplitude was significantly 

positively correlated with P300 amplitude (r = .448, p < .05), such that as RewP amplitude 

increased, P300 amplitude increased. These relationships amongst the P300, RewP, and SPN 

align with prior studies given the sensitivity of these ERPs to rewarding and arousing stimuli 

leading them to trend together (Bowyer et al., 2022; Santopetro et al., 2021). RewP amplitude 

was also significantly negatively related to reaction time (r = -.229, p < .05), such that faster 

reaction times to the doors were related to increased response to feedback. Delta power did not 

demonstrate any statistically significant relationships with any variables. 

See Table 3 for the bivariate correlations between all self-report measures. Generally, 

most of the self-report measures were positively related with each other (with the exception of 

Boldness Inventory – Boldness Total demonstrating negative relationships with other self-report 

measures). The largest correlations tended to be amongst variables from the same measures. For 

example, the correlation between ESI Problematic Impulsivity and ESI Impatient Urgency was 

.615 (p < .05). Interestingly, the items from the depression subscale of the GBI (Low 

Energy/Anhedonia and Loss of Interest) were positively related to many variables associated 

with hypomanic symptoms. For example, the relationship between GBI Loss of Interest and GBI 

High Drive was .452 (p < .05), and the relationship between GBI Low Energy/Anhedonia and 

ESI Problematic Impulsivity was .391 (p < .05). 

15 

 
 
 
 
 
 
 
Exploratory Factor Analysis (EFA) 

The EFA supported a six-factor solution such that each factor had an eigenvalue greater 

than 1. See Figure 6 for the scree plot of eigenvalues and Table 4 for the total variance explained 

by each of the six components. These six components accounted for 66.031% of the total 

variance in the data. 

Table 5 describes how each variable loaded onto the different components, and Table 7 

presents the pattern matrix, explaining how each variable uniquely contributes to the variance of 

each component. Only correlations at or above .300 in the components matrix (Table 5) were 

considered meaningful and were retained for the current analysis. Figure 7 presents a path figure 

of the six-factor solution along with bivariate correlations between factors and variables. The 

factors have a Cronbach’s alpha reliability coefficient of 0.620, suggesting acceptable reliability 

of the model. 

Component 1: P-factor/Questionnaire Methods Variance 

Given that the first component, which explained 24.008% of the variance, contains only 

questionnaire data, most of which pull for psychopathology (i.e., all questionnaires except for 

BAS – Reward Responsiveness and Boldness Inventory – Total Boldness), it appears this first 

factor can be considered to represent the p-factor. This is supported by all questionnaires within 

this factor being positively loaded, suggesting endorsing some psychopathology is related to 

endorsing other kinds of psychopathology. Further, this factor may represent methods variance 

due to the inclusion of most self-report variables.  

Component 2: Trait-Level Approach Motivation/Task Disengagement 

As the second component, which explained 12.065% of the total variance, included 

positively loaded self-report measures of drive [BAS – Drive (r = .511), BAS – Fun Seeking (r = 

.592), BAS – Reward Responsiveness (r = .535)] but negatively loaded measures of anhedonia 

[GBI – Low Energy/Anhedonia (r = -.526), GBI – Loss of Interest (r = -.591)], this factor 

appears to represent trait-level approach motivation. This is further supported by the 

neurophysiological loadings. SPN amplitude (r = .526) positively loads onto this component. 

Given the SPN is a negatively deflecting ERP, this positive relationship reflects decreased 

anticipation to feedback during the randomized guess task. Further, RewP amplitude (r = -.313) 

and P300 amplitude (r = -.393) both negatively load onto this component. Since these are both 

positively deflecting ERPs, these negative relationships indicate decreased response to feedback 

16 

 
 
 
 
 
 
during the randomized guessing task. As such, there is a discrepancy within this factor between 

trait-level approach motivation (as indexed by BAS subscales and GBI anhedonia measures) and 

state-level indices of reward sensitivity during the randomized guessing task (ERP amplitudes). 

The second factor may be differentiating individual differences between trait-level motivation 

and disengagement with the behavioral task.  

Component 3: General Reward Sensitivity 

The third component explains 9.545% of the variance and included positively loaded 

measures of boldness [Boldness Inventory – Total Boldness (r = .322)], RewP amplitude (r = 

.679), and P300 amplitude (r = .623). Since a measure of reward anticipation (Boldness 

Inventory – Total Boldness) and two neurophysiological measures of reward response, RewP 

and P300 amplitudes, positively load onto this factor, it includes variables from both the 

predicted reward anticipation and reward response factors, indicating the third component 

represents general reward sensitivity processes rather than specific temporal phases of reward 

sensitivity.  

Component 4: Excitement Seeking 

The fourth component, which explains 7.808% of the variance, includes a negatively 

loaded measure of drive [BAS – Reward Responsiveness (r = -.491)] and positively loaded 

measures of reward anticipation including boldness [Boldness Inventory – Total Boldness (r = 

.751)] and impulsivity [ESI – Excitement Seeking (r = .525)]. Since BAS – Reward 

Responsiveness is a measure of drive and both Boldness and ESI – Excitement seeking include 

items about recklessness and stimulation seeking behaviors, the factor seems to represent 

excitement seeking more broadly. 

Component 5: Impulsivity 

The fifth component explains 6.672% of the variance. This component includes 

negatively loaded measures of anhedonia [GBI – Low Energy/Anhedonia (r = -.371] and 

valuation [SPSRQ – Sensitivity to Reward (r = -.362)] and positively loaded measures of 

impulsivity [ESI – Problematic Impulsivity (r = .329) and ESI – Lacks Planful Control (r = 

.595)]. Considering the opposing relationships with anhedonia/reward response and impulsivity, 

the fifth factor indexes impulsivity. 

17 

 
 
 
 
 
 
Component 6: State-Dependent Response to Reward 

The sixth component explains 5.933% of the variance and contains positively loaded 

neurophysiological and behavioral indices [SPN amplitude (r = .343), RewP amplitude (r = 

.329), reaction time (r = .411)] and negatively loaded delta power (r = -.725). The positively 

loaded SPN amplitude and reaction time (i.e., slower reaction time) indicate decreased 

anticipation towards reward. In contrast, one of the neurophysiological indices of response to 

reward (RewP amplitude) is positively loaded onto the component. Given the t-test previously 

described regarding delta power, this may not be the most reliable calculation of delta power. 

Since there are discrepancies between the loadings of anticipation and response variables, with 

response variables positively loaded, this factor may describe individual differences in reward 

response as a related, but distinct, process from preparing for rewards. Further, since P300 

amplitude, which is often associated with attention, does not load onto this factor, the factor may 

represent something unique about hedonic response rather than attentional control, 

differentiating the roles of P300 and RewP amplitude in reward response (Polich, 1986). 

Considering this factor only contains measures from the randomized guessing task, it is 

important to note this factor may represent state differences rather than more stable personality 

traits. It likely also captures method variance for indices from the Doors Task. 

Relationships Amongst Variables and Components.  

See Table 6 for bivariate correlations amongst the components. All factors were 

positively related, besides State-Dependent Response to Reward (Component 6), which 

demonstrated significant negative relationships with all factors besides the P-factor (Component 

1), distinguishing state-depending reward response as a unique factor distinct from other 

personality trait factors. Table 7 presents the pattern coefficients which index how much each 

variable uniquely contributes to each component. Table 8 presents the communalities, how much 

variance within each variable can be explained by the components. At least 50% of each 

variable’s variance could be explained by the six factors that emerged. 

18 

 
 
 
 
 
 
 
 
 
Discussion 

The aim of the current study was to use a psychoneurometric approach to index reward 

sensitivity, a facet of the positive valence system in RDoC, by integrating self-report, 

neurophysiological, and behavioral data. I hypothesized that there would be a two-factor solution 

with components representing reward anticipation and initial response to reward. Instead, the 

EFA suggested a six-factor solution, with components representing: the P-factor/Questionnaire 

Methods Variance, Trait-Level Approach Motivation/Task Disengagement, General Reward 

Sensitivity, Excitement Seeking, Impulsivity, and State-Dependent Response to Reward.  

Interestingly, the general reward sensitivity factor which emerged did not include any 

self-report measures of hypomanic/depressive symptoms (e.g., impulsivity, anhedonia) or 

valuation. Instead, the factor consisted of boldness, a reward anticipation personality trait 

associated with hypomanic symptoms, and neurophysiological indices of initial response to 

reward (i.e., P300 and RewP amplitudes). Increased RewP and P300 amplitudes following the 

onset of positive feedback suggest increased response reward (Glazer & Nusslock, 2022). Given 

the integration of self-report and EEG variables from both reward anticipation and response to 

reward, this factor broadly represents reward sensitivity. Even though these variables were not 

predicted to load onto the same factor, the contributions of each of these variables theoretically 

aligns with prior studies of reward sensitivity. This can be seen in prior literature through 

positive relationships between each neurophysiological index (P300 and RewP) and reward 

processing ( Bowyer et al., 2021; Sato et al., 2005; for a review: Glazer et al., 2018). 

Additionally, past studies have linked neurophysiological indices of reward with hypomanic 

symptoms and associated personality traits (Glazer et al., 2019).     

Although it contrasts with hypotheses, having separate components for Trait-Level 

Approach Motivation, Excitement Seeking, and Impulsivity align with other conceptualizations 

of reward sensitivity. Given the Reward Sensitivity Model of mood disorders, hypomanic 

symptoms (e.g., impulsivity, reckless behavior, increased drive) can be considered a result of 

hypersensitivity to reward (Alloy et al., 2016). High drive and impulsivity are two characteristic 

traits of hypomania (Stange et al., 2012; Benazzi, 2007). These three distinct, but theoretically 

related, constructs may represent two facets of hypomanic tendencies within the current study.  

Although the current study failed to produce a two-factor psychoneurometric index of 

reward sensitivity based on the temporal phases of reward anticipation and initial response to 

19 

 
 
 
 
 
 
 
reward, the six-factor solution identified multiple traits which, in excess, can result in hypomanic 

symptoms (Trait-Level Approach Motivation, Excitement Seeking, and Impulsivity) as separate 

constructs contributing to the latent construct. As such, future iterations of the RDoC matrix 

might consider adding more specific subconstructs – such as specific symptoms and risk factors 

for hypomania like motivation, excitement seeking, and impulsivity – to its Reward 

Responsiveness construct. Currently, the Reward Responsiveness construct (and even the 

Positive Valence System as a whole)  does not include any explicit self-report or behavioral 

measures of these traits (About RDoC, n.d.).  

In addition to the systems and their subconstructs provided in the RDoC matrix, the units 

of analysis portions of the matrix provide guidance on measures and paradigms to evaluate these 

systems and subconstructs in empirical research. Currently, a simple random guessing task is 

included in the RDoC matrix, within the reward anticipation subconstruct of reward 

responsiveness (About RDoC, n.d.). RDoC is likely referring to the Doors Task used in this 

study; however, they should specify the kinds and quantities of rewarding stimuli involved in the 

paradigm. Prior literature has demonstrated that the kind of rewarding stimuli presented in these 

paradigms impacts the resulting relationships between personality traits and states with reward 

response (Banica et al., 2022). There may be something unique about monetary rewards 

impacting state-dependent motivation, which should be considered in future studies. 

Additionally, considering indices collected during the Doors Task cross-loaded between factors 

representing trait-level approach motivation/task disengagement, general reward sensitivity, and 

state-dependent response to reward, the Doors Task could be cross-listed in the matrix within 

reward anticipation and initial response to reward.  

Limitations and Future Directions 

These findings highlight key methodological issues with the psychoneurometric 

approach. Within each of the factors, generally, variables were either self-report or 

neurophysiological (except for the Trait-Level Approach Motivation/Task Disengagement and 

Reward Sensitivity factors). Thus, it seems the type of methodology explained a significant 

portion of the variance, rather than variance being explained by theoretically linked variables. 

Even further, the self-report measures which loaded together in the first factor representing the p-

factor were all measures used to explicitly evaluate psychopathology. If the goal of the 

psychoneurometric approach, and, more broadly, dimensional models of psychopathology, is to 

20 

 
 
 
 
 
 
integrate multiple sources of data to index a continuum of latent variables from healthy to 

pathology, saturating the models with a multitude of self-report questionnaire measures may lend 

itself to identifying singular general factors (e.g., the p-factor). Although general factors do have 

merit, they do not describe causal mechanisms or how latent variables differentially contribute to 

these general factors (Watts et al., 2024).  

In the original implementation of the psychoneurometric approach, factor analyses 

contained few variables from different sources. For example, in Yancey et al. (2016), the authors 

only used one self-report measure alongside three psychophysiological variables coming from 

different sources (i.e., heart rate, electromyography, and startle blink response). In the current 

study, there were twelve self-report measures alongside one behavioral measure and four EEG 

measures. The increased number of variables and decreased variability in the type of variables 

may have contributed to the large impact of methodology on variance explained in the current 

project. As such, future studies attempting to implement a psychoneurometric approach to index 

latent variables should consider using fewer, but more specific, variables in their 

operationalization.  

Further, most self-report measures included in the current study pull for psychopathology 

(e.g., the GBI tracks mood disorder symptoms) and other negative associated personality traits 

(e.g., the ESI tracks many externalizing symptoms including callous aggression). The BAS and 

Boldness Inventory were the only two measures which measure more positive traits like drive, 

motivation, confidence, fearlessness, and self-esteem. If dimensional models aim to represent the 

spectrum of healthy to pathological levels of traits, including only negatively valanced scales can 

lead to issues like response bias, malingering, and impression management. These psychometric 

issues can contribute to general psychopathology factors emerging. As researchers develop 

dimensional models of psychopathology, they should consider constructing new scales tracking 

individual differences in latent variables rather than DSM-5 diagnostic criteria. 

A final limitation of the current study is sample size. Despite the study including 110 

participants, it may have been underpowered with the number of heterogeneous included 

variables. Within factor analysis, less than 150 participants is considered a small sample size 

(Kyriazos, 2018). Future studies implementing a psychoneurometric approach to EFA should 

aim to include at least 150 participants, and ideally 300 to 400, to ensure reliability of 

correlations amongst variables (Kyriazos, 2018).  

21 

 
 
 
 
Concluding Remarks 

Overall, the current study aimed to use a psychoneurometric approach to operationalize 

reward sensitivity comprising two factors representing temporal phases of reward processing, 

anticipation and initial response. Although the EFA revealed six factors, including three factors 

which cut across methodologies (Trait-Level Approach Motivation/Task Disengagement and 

General Reward Sensitivity), the current study highlights challenges to using multiple units of 

measurement in dimensional models. Future studies should consider developing their own self-

report scales of latent constructs rather than depending on currently available measurement tools 

which often are based on DSM-5 categorical symptoms.  

22 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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28 

 
 
 
 
 
 
 
 
 
 
 
 
 
APPENDIX 

Figures and Tables 

Reward 
Sensitivity

Anticipation

Initial Response

GBI – Low 
Energy/Anhedonia (-)

Boldness 
Inventory -
Boldness Total 
(+)

GBI – High 
Drive (+)

BAS – Drive, 
Fun Seeking, 
Reward 
Responsiveness
, (+)

ESI Impulsivity 
Subscales (+)

SPN (+)

SPSRQ –
Reward (+)

GBI - Loss of 
Interest (-)

RewP (+)

Gain-related 
P300 (+)

Delta Power (+)

Figure 1. Proposed psychoneurometric model of reward sensitivity using self-report and EEG indices.

29 

 
 
 
 
  
Figure 2. SPN waveform and headmap. The SPN is the average amplitude at occipital location 
Oz during the 200 ms immediately preceding the receipt of feedback (i.e., the green or red 
arrow), regardless of win or loss. 0 ms indicates the onset of feedback (i.e., the green or red 
arrow). The headmap depicts decreased activity (blue) at occipital locations during this time 
window. 

30 

 
 
 
 
  
 
 
 
 
Figure 3. RewP waveform and headmap. The RewP is the difference between gain and loss 
trials from 215 to 315 ms following the onset of feedback at frontal-central location FCz. 0 ms 
indicates the onset of feedback (i.e., the green or red arrow). The headmap depicts increased 
activation (red) at frontal central locations during this time window. 

31 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 4. P300 waveform – gain trials. The gain-related P300 is the average amplitude 300-500 
ms following the onset of feedback (i.e., the green arrow) at central-parietal location CPz. 0 ms 
represents the onset of the feedback. The headmap depicts increased activity (red) at central-
parietal locations during this time window. 

32 

 
 
 
 
 
 
 
 
 
 
 
Figure 5. Delta power difference for gains minus losses. Delta was extracted using a Morlet 
wavelet transformation with 20 logarithmic steps ranging from .05 to 20 Hz. Delta power was 
calculated as the average delta frequency occurring from 50 to 250 ms following losses 
subtracted from the average delta frequency occurring from 50 to 250 ms following gains. Delta 
frequency was more sensitive to gain trials compared to losses, as depicted in the warmer tones 
of the figure. 

33 

 
 
 
 
 
 
 
Figure 6. Scree plot. Six components with eigenvalues greater than 1 were interpreted in the 
current study. 

34 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 7. Path diagram of each component along with each variable loading and bivariate correlations between components. Only 
loadings r > .30 are depicted.

35 

 
 
 
 
 
 
Table 1. Demographic information for the sample.  

Total Sample 

MLS Sample 

Sex 

N = 110 

39.091% original 
MLS sample 

63.637% Female 

Race/Ethnicity 

Age (Mean (Standard 
Deviation) ) 
34.593 (6.374) 

White/Caucasian 
African American/Black 
Asian 
Latine 
Middle Eastern/North African 
Pacific Islander/Native Hawaiian 
Multiracial 
Did not specify 

60.910% (n = 67) 
4.545% (n = 5) 
1.818% (n = 2) 
0% (n = 0) 
0% (n = 0) 
0% (n = 0) 
2.727% (n = 3) 
30.0% (n = 33) 

36 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 2. Means and standard deviations for each variable included in the EFA.  

Measure 

Mean (Standard Deviation) 

EEG and Behavioral Measures 

RewP Amplitude 

P300 Amplitude  

SPN Amplitude 

1.804 (2.921) µV 

11.855 (5.231) µV 

-2.810 (3.303) µV 

Delta Power – Gain/Loss Difference 

.854 (16.947) µV2/Hz 

Reaction Time  

542.00 (197.252) ms 

Anhedonia Measures 

GBI – Loss of Interest 

GBI – Low Energy/Anhedonia 

6.510 (2.325) 

5.600 (2.010) 

Impulsivity and Drive Measures 

GBI – High Drive 

ESI – Problematic Impulsivity 

ESI – Impatient Urgency 

ESI – Lacks Planful Control 

ESI – Boredom Proneness 

ESI – Excitement Seeking 

BAS – Drive 

BAS – Fun Seeking 

BAS – Reward Responsiveness 

HPS – Excitement 

Boldness Inventory – Boldness Total  

37 

4.845 (1.110) 

2.472 (3.697) 

5.248 (3.919) 

2.895 (3.628) 

3.781 (3.470) 

3.098 (3.644) 

11.981 (3.744) 

9.691 (2.695) 

15.354 (3.170) 

1.277 (1.658) 

49.684 (7.733) 

 
 
 
 
Table 2 (cont’d) 

Valuation Measure 

SPSRQ – Sensitivity to Reward 

0.330 (0.201) 

38 

 
 
 
 
 
Table 3. Bivariate correlations between neurophysiological and behavioral indices and self-report measures.  

SPN Amplitude 
(µV) 

RewP Amplitude 
(µV) 

P300 Amplitude 
(µV) 

Delta Power 
(µV2/Hz) 

Reaction Time 
(ms) 

GBI – Low 
Energy/Anhedonia 

GBI – Loss of 
Interest 

GBI – High Drive 
BAS – Drive 

BAS – Fun Seeking 

BAS – Reward 
Responsiveness 

Boldness Inventory 
– Boldness Total 

ESI – Problematic 
Impulsivity 

ESI – Impatient 
Urgency 

ESI – Lacks Planful 
Control 

ESI – Boredom 
Proneness 

ESI – Excitement 
Seeking 

HPS – Excitement 

-.050 

-.058 

-.065 
.070 

.078 

.070 

-.102 

-.017 

-.072 

.020 

-.035 

-.086 

.038 

-.063 
.024 

-.117 

-.048 

.066 

.008 

.067 

.092 

.044 

-.020 

-.025 
-.127 

-.134 

-.064 

.122 

.072 

.102 

.035 

-.084 

-.051 

-.095 

-.137 

.034 
.039 

.184 

.040 

.027 

-.026 

.001 

-.030 

.021 

.065 

-.104 

-.105 

-.125 

-.087 

.006 

.043 

.056 

39 

.086 

-.044 

.184 
-.050 

.102 

.112 

-.007 

.098 

.068 

.024 

-.041 

.162 

.074 

 
 
 
 
 
Table 3 (cont’d) 

SPSRQ – Sensitivity 
to Reward 
SPN Amplitude 
RewP Amplitude 
P300 Amplitude 
Delta Power 
Reaction Time 

*p < .05 

.051 

-.216* 
-.280* 
-.002 
.260* 

.003 

-.216* 

.448* 
-.182 
-.229* 

-.116 

-.280* 
.448* 

-.068 
-.180 

.026 

-.002 
-.182 
.040 

-.027 

.105 

.260* 
-.229* 
-.180 
-.027 

40 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 4. Bivariate correlations between self-report measures.  

GBI – Low 
Energy/Anhedonia 

GBI – Loss 
of Interest 

GBI – High 
Drive 

BAS – 
Drive 

BAS – Fun 
Seeking 

BAS – Reward 
Responsiveness 

Boldness 
Inventory – 
Boldness 
Total  

.684* 

.445* 

.037 

-.021 

-.067 

-.189* 

.684* 

.452* 

.008 

-.052 

-.072 

-.287* 

GBI – Low 
Energy/Anhedonia 

GBI – Loss of 
Interest 

GBI – High Drive 

.445* 

.452* 

.246* 

.260* 

.123 

.067 

BAS – Drive 

.037 

.008 

.246* 

.645* 

.469* 

.081 

BAS – Fun 
Seeking 

BAS – Reward 
Responsiveness 

Boldness 
Inventory – 
Boldness Total 

ESI – Problematic 
Impulsivity 

ESI – Impatient 
Urgency 

-.021 

-.052 

.260* 

.645* 

.496* 

.040 

-.067 

-.072 

.123 

.469* 

.496* 

-.108 

-.189* 

-.287* 

.067 

.081 

.040 

-.108 

.391* 

.326* 

.546* 

.174 

.174 

.040 

-.111 

.252* 

.238* 

.424* 

.350* 

.302* 

.265* 

-.236* 

41 

 
 
 
 
 
 
 
 
 
 
 
 
Table 4 (cont’d) 

ESI – Lacks 
Planful Control 

ESI – Boredom 
Proneness 

ESI – Excitement 
Seeking 

.087 

.154 

.228* 

.186 

.233* 

-.028 

-.080 

.294* 

.283* 

.439* 

.265* 

.360* 

.098 

-.203* 

.179 

.136 

.387* 

.123 

.313* 

-.068 

.202* 

HPS – Excitement 

.299* 

.079 

.357* 

.347* 

.338* 

.123 

-.019 

SPSRQ – 
Sensitivity to 
Reward 

GBI – Low 
Energy/Anhedo
nia 
GBI – Loss of 
Interest 
GBI – High 
Drive 
BAS – Drive 
BAS – Fun 
Seeking 
BAS – Reward 
Responsiveness 

.110 

.117 

.444* 

.351* 

.274* 

.209* 

.319* 

ESI – 
Problematic 
Impulsivity 

ESI – 
Impatient 
Urgency 

ESI – Lacks 
Planful 
Control 

ESI – 
Boredom 
Proneness 

ESI – 
Excitement 
Seeking 

HPS – 
Excitement 

SPSRQ – 
Sensitivity to 
Reward 

.391* 

.252* 

.087 

.294* 

.179 

.299* 

.110 

.326* 

.546* 

.174 

.174 

.040 

.238* 

.424* 

.350* 

.302* 

.265* 

.154 

.228* 

.186 

.233* 

-.028 

.283* 

.439* 

.265* 

.360* 

.098 

.136 

.387* 

.123 

.313* 

-.068 

.079 

.357* 

.347* 

.338* 

.123 

.117 

.444* 

.351* 

.274* 

.209* 

42 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 4 (cont’d) 

Boldness 
Inventory  – 
Boldness Total 
ESI – 
Problematic 
Impulsivity 
ESI – 
Impatient 
Urgency 
ESI – Lacks 
Planful 
Control 
ESI – Boredom 
Proneness 
ESI – 
Excitement 
Seeking 
HPS – 
Excitement 
SPSRQ – 
Sensitivity to 
Reward 

*p < .05

-.111 

-.236* 

-.080 

-.203* 

.202* 

-.019 

.319* 

.615* 

.547* 

.485* 

.558* 

.314* 

.262* 

.615* 

.306* 

.600* 

.389* 

.318* 

.426* 

.547* 

.306* 

.240* 

.298* 

.219* 

.056 

.485* 

.600* 

.240* 

.478* 

.374* 

.399* 

.558* 

.389* 

.298* 

.478* 

.118 

.338* 

.314* 

.318* 

.219* 

.374* 

.118 

.412* 

.262* 

.426* 

.056 

.399* 

.338* 

.412* 

43 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5. Total variance explained by each component of the EFA.  

Component 

Extraction Sums of Squared Loadings 

Total 

% of Variance 

Cumulative % 

1 

2 

3 

4 

5 

6 

4.562 

2.292 

1.814 

1.483 

1.268 

1.127 

24.008 

12.065 

9.545 

7.808 

6.672 

5.933 

24.008 

36.073 

45.618 

53.426 

60.098 

66.031 

44 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 6. Components matrix of how each variable correlates to each component. Only 
correlations above .300 are presented.  

Component 

1 

2 

3 

4 

5 

6 

-.371 

.329 

.595 

.546 

.592 

.535 

-.491 

.322 

.751 

GBI – Low 
Energy/Anhedonia 

.483 

-.526 

GBI – Loss of 
Interest 

.428 

-.591 

GBI – High Drive 

.730 

BAS – Drive 

.515 

.511 

BAS – Fun 
Seeking 

BAS – Reward 
Responsiveness 

Boldness 
Inventory – 
Boldness Total 

ESI – Problematic 
Impulsivity 

ESI – Impatient 
Urgency 

ESI – Lacks 
Planful Control 

ESI – Boredom 
Proneness 

ESI – Excitement 
Seeking 

.753 

.753 

.462 

.738 

.600 

HPS – Excitement 

.570 

.525 

45 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 6 (cont’d) 

SPSRQ – 
Sensitivity to 
Reward 

.588 

.321 

-.362 

SPN Amplitude 

.312 

RewP Amplitude 

-.313 

.679 

P300 Amplitude 

-.393 

.624 

Delta Power 

Reaction Time 

.343 

.329 

-.725 

.411 

46 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 7. Bivariate correlations between factor scores. 

Component 
1 

Component 
2 

Component 
3 

Component 
4 

Component 
5 

Component 
6 

.395* 

.497* 

-.040 

.312* 

-.159 

.395* 

.321* 

.497* 

.321* 

.027 

.041 

.261* 

-.263* 

.239* 

-.223* 

-.040 

.027 

.041 

.042 

-.241* 

.312* 

.261* 

.239* 

.042 

-.223* 

-.159 

-.263* 

-.223* 

-.241* 

-.223* 

Component 
1 
Component 
2 
Component 
3 
Component 
4 
Component 
5 
Component 
6 
*p < .05 

47 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 8. Pattern coefficients, which index the unique contribution of each variable to each 
component. 

GBI – Low 
Energy/ 
Anhedonia 
GBI – Loss of 
Interest 
GBI – High 
Drive 
BAS – Drive 
BAS – Fun 
Seeking 
BAS – Reward 
Responsiveness 
Boldness 
Inventory – 
Boldness Total 
ESI – 
Problematic 
Impulsivity 
ESI – Impatient 
Urgency 
ESI – Lacks 
Planful Control 
ESI – Boredom 
Proneness 
ESI – 
Excitement 
Seeking 
HPS – 
Excitement 
SPSRQ – 
Sensitivity to 
Reward 
SPN Amplitude 
RewP 
Amplitude 
P300 Amplitude 
Delta Power 
Reaction Time 

Comp
onent 
1 

Component 
2 

Component 
3 

Component 
4 

Component 
5 

Component 
6 

-.088 

-.159 

.965 

-.007 

-.104 

-.075 

-.032 

-.155 

.171 

-.021 

.183 

.063 

.861 

.751 

-.225 

.932 

.928 

.565 

-.096 

-.255 

-.078 

-.026 

.037 

-.019 

.069 

.124 

-.221 

.281 

.070 

.015 

-.144 

.163 

.035 

.054 

-.175 

.168 

-.153 

-.139 

-.308 

-.068 

.961 

.068 

.838 

-.098 

.187 

.027 

-.057 

.057 

.554 

.336 

.172 

.913 

-.034 

-.250 

.467 

.165 

.266 

-.092 

.023 

-.099 

-.130 

-.210 

-.089 

.079 

.065 

-.211 

.755 

-.227 

-.096 

.193 

.327 

-.111 

-.021 

.335 

.319 

-.170 

.147 

-.201 

.054 

.753 

-.442 

-.571 
-.245 
.710 

.631 

-.135 

.101 

.072 
-.057 
.154 

.079 

.153 

.601 

.335 
-.838 
.230 

-.038 

.319 

.145 

.140 

.145 
.043 
.084 

.104 

.091 

.074 
-.038 
.008 

.205 

-.173 

-.133 

-.048 
-.256 
.059 

48 

 
 
 
 
 
 
 
 
Table 9. Communalities for each variable. Communalities represent the proportion of the 
variance in each variable which can be explained by the six factors. 

Extraction 

.755 

.733 
.662 
.697 
.710 

.693 

.845 

.783 

.657 
.602 
.603 
.719 
.457 

.690 

.564 
.689 
.555 
.622 
.510 

GBI – Low 
Energy/Anhedonia 
GBI – Loss of Interest 
GBI – High Drive 
BAS – Drive 
BAS – Fun Seeking 
BAS – Reward 
Responsiveness 
Boldness Inventory – 
Boldness Total 
ESI – Problematic 
Impulsivity 
ESI – Impatient Urgency 
ESI – Lacks Planful Control 
ESI – Boredom Proneness 
ESI – Excitement Seeking 
HPS – Excitement 
SPSRQ – Sensitivity to 
Reward 
SPN Amplitude 
RewP Amplitude 
P300 Amplitude 
Delta Power 
Reaction Time 

Initial 

1.000 

1.000 
1.000 
1.000 
1.000 

1.000 

1.000 

1.000 

1.000 
1.000 
1.000 
1.000 
1.000 

1.000 

1.000 
1.000 
1.000 
1.000 
1.000 

49