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Explaining neural signals in human visual cortex with an associative learning model.

Publication ,  Journal Article
Jiang, J; Schmajuk, N; Egner, T
Published in: Behavioral neuroscience
August 2012

"Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

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Published In

Behavioral neuroscience

DOI

EISSN

1939-0084

ISSN

0735-7044

Publication Date

August 2012

Volume

126

Issue

4

Start / End Page

575 / 581

Related Subject Headings

  • Visual Pathways
  • Visual Cortex
  • Signal Detection, Psychological
  • Predictive Value of Tests
  • Oxygen
  • Models, Neurological
  • Male
  • Magnetic Resonance Imaging
  • Image Processing, Computer-Assisted
  • Humans
 

Citation

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Jiang, J., Schmajuk, N., & Egner, T. (2012). Explaining neural signals in human visual cortex with an associative learning model. Behavioral Neuroscience, 126(4), 575–581. https://doi.org/10.1037/a0029029
Jiang, Jiefeng, Nestor Schmajuk, and Tobias Egner. “Explaining neural signals in human visual cortex with an associative learning model.Behavioral Neuroscience 126, no. 4 (August 2012): 575–81. https://doi.org/10.1037/a0029029.
Jiang J, Schmajuk N, Egner T. Explaining neural signals in human visual cortex with an associative learning model. Behavioral neuroscience. 2012 Aug;126(4):575–81.
Jiang, Jiefeng, et al. “Explaining neural signals in human visual cortex with an associative learning model.Behavioral Neuroscience, vol. 126, no. 4, Aug. 2012, pp. 575–81. Epmc, doi:10.1037/a0029029.
Jiang J, Schmajuk N, Egner T. Explaining neural signals in human visual cortex with an associative learning model. Behavioral neuroscience. 2012 Aug;126(4):575–581.

Published In

Behavioral neuroscience

DOI

EISSN

1939-0084

ISSN

0735-7044

Publication Date

August 2012

Volume

126

Issue

4

Start / End Page

575 / 581

Related Subject Headings

  • Visual Pathways
  • Visual Cortex
  • Signal Detection, Psychological
  • Predictive Value of Tests
  • Oxygen
  • Models, Neurological
  • Male
  • Magnetic Resonance Imaging
  • Image Processing, Computer-Assisted
  • Humans