Low-dimensional encoding of decisions in parietal cortex reflects long-term training history

Kenneth W. Latimer () and David J. Freedman
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Kenneth W. Latimer: University of Chicago
David J. Freedman: University of Chicago

Nature Communications, 2023, vol. 14, issue 1, 1-24

Abstract: Abstract Neurons in parietal cortex exhibit task-related activity during decision-making tasks. However, it remains unclear how long-term training to perform different tasks over months or even years shapes neural computations and representations. We examine lateral intraparietal area (LIP) responses during a visual motion delayed-match-to-category task. We consider two pairs of male macaque monkeys with different training histories: one trained only on the categorization task, and another first trained to perform fine motion-direction discrimination (i.e., pretrained). We introduce a novel analytical approach—generalized multilinear models—to quantify low-dimensional, task-relevant components in population activity. During the categorization task, we found stronger cosine-like motion-direction tuning in the pretrained monkeys than in the category-only monkeys, and that the pretrained monkeys’ performance depended more heavily on fine discrimination between sample and test stimuli. These results suggest that sensory representations in LIP depend on the sequence of tasks that the animals have learned, underscoring the importance of considering training history in studies with complex behavioral tasks.

Date: 2023
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DOI: 10.1038/s41467-023-36554-5

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