August 2016
Volume 16, Issue 12
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2016
Categorical Decision Making and Category Learning in Parietal and Prefrontal Cortices
Author Affiliations
  • David Freedman
    Department of Neurobiology and Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, The University of Chicago
Journal of Vision September 2016, Vol.16, 1301. doi:10.1167/16.12.1301
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      David Freedman; Categorical Decision Making and Category Learning in Parietal and Prefrontal Cortices. Journal of Vision 2016;16(12):1301. doi: 10.1167/16.12.1301.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

We have a remarkable ability to recognize the behavioral significance, or category membership of incoming sensory stimuli. In the visual system, much is known about how simple visual features (such as color, orientation and direction of motion) are processed in early stages of the visual system. However, much less is known about how the brain learns and recognizes categorical information that gives meaning to incoming stimuli. This talk will discuss neurophysiological and behavioral experiments aimed at understanding the mechanisms underlying visual categorization and decision making, with a focus on the impact of category learning on underlying neuronal representations in the posterior parietal cortex (PPC) and prefrontal cortex (PFC). We recorded from PPC both before and after training on a visual categorization task. This revealed that categorization training influenced both visual and cognitive encoding in PPC, with a marked enhancement of memory-related delay-period encoding during the categorization task which was not observed during a motion discrimination task prior to categorization training. In contrast, the PFC exhibited strong delay-period encoding during both discrimination and categorization tasks. This reveals a dissociation between PFCs and PPCs roles in decision making and short term memory, with generalized engagement of PFC across a wider range of tasks, in contrast with more task-specific and training dependent mnemonic encoding in PPC.

Meeting abstract presented at VSS 2016

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