Manual exploratory behaviors observed during early childhood have critical functional and clinical role in the motor development of a child (Lockman & Kahrs, 2017). This dissertation is aimed to (1) address the challenges faced in the quantitative analysis of these behaviors, (2) conduct quantitative analysis of two important manual exploratory behaviors, (3) extend the current knowledge on the effects of age and object properties on these behaviors beyond infancy by assessing them in preschoolers. In Study 1, a machine learning (ML) -based automated classification system was developed as a proof-of-concept for the classification of manual exploratory behaviors that address the challenges encountered in the quantitative analysis of these behaviors. This system was developed using data from adult participants and it can currently classify three manual exploratory behaviors namely- rotation, throwing and fingering with substantially higher accuracy than chance level (average accuracy = 85.0 + 4.16%). Based on these findings, ML -based approach appears to be both- a feasible and a scalable alternative to conventional video coding for identifying the manual exploratory behaviors on time series; thereby, facilitating their quantitative assessment. In Study 2, quantitative assessment of two important manual exploratory behaviors- rotation and throwing was conducted along with the assessment of ML classifiers on data from children (3 - 5 years old). The ML classifiers showed substantial decrease in performance owing to differences in movements between children and adults as well as technical difficulties. Rotation behaviors became more variable and faster with increasing age while the characteristics of throwing behaviors were inconclusive of developmental differences across the three ages. In Study 3, the effects of age and three object properties (size, shape and texture) were assessed on the qualitative characteristics of manual exploratory behaviors in children (3 - 5 years old). Manual exploration of objects was driven at different levels by age and object properties in preschoolers. In terms of age, throwing behaviors were more common in the 3-year group while rotational behaviors in the 5-year group. In terms of the three object properties, object size and shape directed child's hand preference in reaching objects while object size and texture influenced their manual exploratory behaviors. In addition, object texture was found to mainly influence child's first interactions with the objects as the squeezing and fingering behaviors occurred more often during the first interactions with the objects. The findings suggest that the dynamic interplay between learning to perceive object properties and manually exploring them continues to develop and adapt beyond infancy. In summary, manual exploratory behaviors, similar to other motor behaviors, are influenced by different individual, task and environment factors. These effects continue beyond infancy and throughout early childhood. A thorough qualitative and quantitative assessment is required to fully understand their functional and clinical role in early childhood. For this, ML -based approach is recommended to address the challenges in their quantitative analysis and to facilitate the overall scope of investigating these behaviors.
Read
