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Updated: Apr 16, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
Published on: February 8, 2019
Choice-correlated activity fluctuations underlie learning of neuronal category representation
Tatiana A Engel1, Warasinee Chaisangmongkon2, David J Freedman3
11] Department of Neurobiology, Yale University School of Medicine, Kavli Institute for Neuroscience, 333 Cedar Street, New Haven, Connecticut 06510, USA [2] Department of Bioengineering, Stanford University, 318 Campus Drive, Stanford, California 94305, USA.
Abstract:
The ability to categorize stimuli into discrete behaviourally relevant groups is an essential cognitive function. To elucidate the neural mechanisms underlying categorization, we constructed a cortical circuit model that is capable of learning a motion categorization task through reward-dependent plasticity. Here we show that stable category representations develop in neurons intermediate to sensory and decision layers if they exhibit choice-correlated activity fluctuations (choice probability). In the model, choice probability and task-specific interneuronal correlations emerge from plasticity of top-down projections from decision neurons. Specific model predictions are confirmed by analysis of single-neuron activity from the monkey parietal cortex, which reveals a mixture of directional and categorical tuning, and a positive correlation between category selectivity and choice probability. Beyond demonstrating a circuit mechanism for categorization, the present work suggests a key role of plastic top-down feedback in simultaneously shaping both neural tuning and correlated neural variability.
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