Keynotes | K&Ts | GACs | Talks | Posters | Search

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

Flexible brain network flow rerouting from stimulus to response during rapid instructed task learning

Arun Aryal¹, Ravi D. Mill¹, Inbar Amir², Moti Salti², Alexandros Tzalvras², Nachshon Meiran², Michael W. Cole¹; ¹Rutgers University, ²Ben-Gurion University of the Negev

Presenter: Arun Aryal

Rapid instructed task learning (RITL) is a form of cognitive control that allows humans to learn and execute novel tasks based solely on instructions. Based on the cognitive control literature, RITL involves top-down control from cognitive control networks (CCNs), including the fronto-parietal (FPN), cingulo-opercular (CON), and dorsal attention networks (DAN), acting via a flexible hub mechanism. Despite extensive research, a generative framework explaining how distributed brain networks combine to produce stimulus-response (S-R) mapping during RITL has not been developed. We hypothesized two potential mechanisms: CCNs could dynamically mediate S-R mappings during task execution (reactive routing), or CCNs could pre-configure direct sensory-motor pathways during instruction encoding, establishing "prepared reflexes" for immediate execution (proactive routing). To address this gap, we employed behavioral testing, functional MRI, and empirical neural network modeling using an instructed task learning paradigm involving a primary S-R mapping phase and a secondary distractor phase. Direct functional connectivity (FC) was estimated through graphical lasso regression using mean-task-activity regressed BOLD time series. Activity flow mapping modeled the propagation of task-evoked activations from source regions through FC to predict non-circular activations in target somatomotor network (SMN). Results revealed that sensory network activations (VIS1+VIS2) flowing through FC were sufficient to generate accurate motor patterns. In-silico FC lesion analyses showed that both reactive and proactive routes carry sensorimotor transformations. Decomposing the model’s stepwise flows revealed the intermediate role of DAN in routing flows from VIS2 to SMN. These findings support a flexible mechanism where CCN-mediated proactive and reactive rerouting enable rapid task execution.

Topic Area: Decision-Making, Cognitive Control & Event Cognition