The organization of neural representations for control
Brown University
Recording
Abstract
Cognitive control allows us to think and behave flexibly based on our context and goals. Most theories of cognitive control propose a control representation that enables the same input to produce different outputs contingent on contextual factors. In this talk, I will focus on an important property of the control representation's neural code: its representational dimensionality. Dimensionality of a neural representation balances a basic separability/generalizability trade-off in neural computation. This tradeoff has important implications for cognitive control. In this talk, I will present initial evidence from fMRI and EEG showing that task representations in the human brain leverage both ends of this tradeoff during flexible behavior.
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