Journal article
Explaining Neural Signals in Human Visual Cortex With an Associative Learning Model
Behavioral neuroscience, Vol.126(4), pp.575-581
08/2012
DOI: 10.1037/a0029029
PMID: 22845706
Abstract
"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.
Details
- Title: Subtitle
- Explaining Neural Signals in Human Visual Cortex With an Associative Learning Model
- Creators
- Jiefeng Jiang - Department of Psychology & Neuroscience and Center for Cognitive Neuroscience, Duke UniversityNestor Schmajuk - Department of Psychology & Neuroscience, Duke UniversityTobias Egner - Department of Psychology & Neuroscience and Center for Cognitive Neuroscience, Duke University
- Resource Type
- Journal article
- Publication Details
- Behavioral neuroscience, Vol.126(4), pp.575-581
- Publisher
- American Psychological Association
- DOI
- 10.1037/a0029029
- PMID
- 22845706
- ISSN
- 0735-7044
- eISSN
- 1939-0084
- Language
- English
- Date published
- 08/2012
- Academic Unit
- Psychological and Brain Sciences; Iowa Neuroscience Institute
- Record Identifier
- 9984065821502771
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