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Contributed Talk Session: Tuesday, August 4, 10:15 – 11:15 am, Skirball Theater
Poster B in Poster Session B: Tuesday, August 4, 2:00 – 3:45 pm, Kimmel Center, Shorin & Rosenthal Rooms

Orthogonal Task Representation Enables Flexible Task Switching in Human OPM-MEG and Neural Networks

Xiaoyi Liu¹, Leigh Nystrom¹, Mark Pinsk¹, Nicholas DePinto¹, William A. Wolf¹, Nathaniel D. Daw¹, Sabine Kastner¹, Jonathan D. Cohen¹, Harrison Ritz²; ¹Princeton University, ²Queen's University

Presenter: Xiaoyi Liu

Task switching requires flexible reconfiguration of task representations over time. To characterize the underlying neural computations, we recorded brain activity with optically-pumped magnetometer-based MEG (OPM-MEG) during a task-switching paradigm, in which participants maintained instructions for an upcoming task while performing a current task. In both human and recurrent neural networks (RNNs) trained on similar paradigms, we found that the current and future task identities were encoded in orthogonal dimensions. In human but not RNNs, the current-task representations generalize across epochs, consistent with a compositional representation organized by functional relevance.

Topic Area: Methods, Tools, Theory & Neural Coding