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Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
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Encoding of Stimulus Probability in Macaque Inferior Temporal Cortex.

Andrew H Bell1, Christopher Summerfield2, Elyse L Morin3

  • 1Laboratory of Brain and Cognition, National Institute of Mental Health, 9000 Rockville Pike, Bethesda, MD 20892, USA; Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford OX1 3UD, UK; MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF, UK.

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|August 16, 2016
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Neural activity in the inferior temporal cortex (IT) encodes stimulus probability, supporting predictive coding theories. Neurons showed expectation suppression, but expected stimuli were decoded more accurately.

Keywords:
decision makingexpectation suppressionmonkeyneuronpredictive coding

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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Computational Neuroscience

Background:

  • Optimal perceptual decisions integrate sensory input with prior probabilities.
  • The neural basis of encoding stimulus probability in sensory cortex remains debated.
  • Previous neuronal recordings have not fully elucidated this mechanism.

Purpose of the Study:

  • To investigate how the brain encodes and utilizes prior information about stimulus probability during perception.
  • To test whether neuronal populations in the inferior temporal cortex represent long-term stimulus probabilities.
  • To examine the effects of expectation on neuronal responses and stimulus decoding.

Main Methods:

  • Recorded single-neuron activity in the inferior temporal cortex (IT) of monkeys.
  • Monkeys performed a visual discrimination task involving degraded images of faces and fruit.
  • Manipulated the long-term probability of face versus fruit stimuli unpredictably within sessions.

Main Results:

  • Population responses in IT encoded the long-term stimulus probability.
  • Face-responsive neurons exhibited reduced firing rates to expected faces (expectation suppression).
  • Expected stimuli were decoded with greater accuracy from multivariate IT population signals.

Conclusions:

  • Neuronal activity in IT actively encodes and updates predictions about the sensory environment.
  • Findings support predictive coding theories of brain function.
  • The brain integrates sensory evidence with prior probabilities for optimal decision-making.