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Poster F in Poster Session F: Thursday, August 6, 1:45 – 3:30 pm, Kimmel Center, Shorin & Rosenthal Rooms

Behavioral Automaticity Triggered by Stimulus-Driven Reactivation of Functional Connections

Ruben Sanchez-Romero¹, Richard Chen¹, Nicole Lalta¹, Emily Winfield¹, Ravi D. Mill¹, Michael W. Cole¹; ¹Rutgers University

Presenter: Ruben Sanchez-Romero

Whether learning to walk, talk or type, cognitive psychology research spanning decades has established automaticity as the basis of robust and accurate task performance following practice. Despite the centrality of automaticity little is known about its neural mechanisms. Here, we introduce a rapid associative task paradigm to identify distributed brain network mechanisms supporting rapid learning to automaticity. Participants first learned a set of visual stimulus-motor response mappings (Primary Task) that were then reversed for a subset of mappings (Secondary Task) to measure interference effects from automaticity. Behavioral results showed less accuracy and increased response times in the reversed conditions, confirming automaticity of the practiced mappings. We hypothesized that automatic mappings are encoded as context-dependent task-state FC changes, with stimulus contexts similar to the initial learning triggering previously-learned mappings. Moreover, we hypothesized that task-state FC changes are sufficient to reproduce the full stimulus-to-response automatized behavior, and tested this using activity flow modeling (actflow)—empirical FC-specified task-performing network models. Consistent with our hypotheses, we observed that Primary Task task-state FC patterns automatically reactivated during the Secondary Task period (with greater strength early in the task), despite participants being instructed to follow different instructions. Next, to better understand how task-state functional networks support automaticity, we used actflow modeling to provide a mechanistic description of how neural activations flow through directed functional connections to generate automatic motor responses. Actflow models parameterized by empirical task-state FC successfully generated interference effects, predicting correct responses for trials with Primary Task mappings and incorrect responses for trials with reversed (Secondary Task) mappings. Finally, we found that task-state FC patterns encode specific Primary Task mapping identity, determining the appropriate motor response predictions. Together, our results suggest learning to automaticity is supported by distributed networks linking perception, control, and behavior, that are context-dependent and can operate unintentionally.

Topic Area: Methods, Tools, Theory & Neural Coding