Dissertation Defense: Michael Slipenkyj
Candidate: Michael Slipenkyj
Major: Psychology
Advisor: Ian M. Lyons, Ph.D.
Investigating the Neural and Behavioral Processes Involved in Learning Ordered Abstract Symbols
Abstract symbols, such as numerals (e.g., 1, 2, 3…) are core components of human thinking, enabling activities such as keeping track of time and advanced mathematics. Understanding precisely how symbols are acquired and utilized is fundamental to deciphering the functional organization of the human brain. On one hand, symbols may be derived by the learning environment (i.e., the learning context). Alternatively, symbols may be influenced by the context in which they are embedded (i.e., the processing context). Across three studies, the present dissertation aims to disentangle the role of the learning context and processing context in symbolic representations. All three studies use data from an fMRI training dataset, in which participants were taught a sequence of artificial (i.e., novel) ordered symbols using either an associative (i.e., learning the order of the symbols using symbol-to-symbol relationships) or positional (i.e., learning the order of the symbols using the spatial layout) learning paradigm. In study 1, I investigated the learning trajectories and patterns of performance for associative and positional training, with results showing distinct patterns of acquisition and anchoring. In study 2, I evaluated group and task differences in the brain using fMRI data. Results showed that the neural representation of artificial symbols depends on the processing, but not the learning context. Study 3 extends these findings to show the significant role of processing context extends to overlearned numerals. Combined, the findings from this dissertation provide strong evidence for the importance of processing context for representing abstract symbols. Conversely, this research demonstrates the robustness of neural coding to different learning contexts. Overall, this research suggests the human brain is organized in a dynamic manner, able to rapidly activate neural representations specific to the processing context, regardless of symbolic format.