Explaining Neural Signals in Human Visual Cortex With an Associative Learning Model.
"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 vi...
| Publicado en: | Behavioral Neuroscience Vol. 126; no. 4; pp. 575 - 582 |
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| Autores principales: | , , |
| Formato: | Artículo |
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American Psychological Association
Aug2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=78276420&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 78276420 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07357044 BEN jtl: Behavioral Neuroscience issn: 07357044 maglogo: N pubinfo: dt: Aug2012 vid: 126 iid: 4 pid: 34 pub: American Psychological Association artinfo: ui: 78276420 10.1037/a0029029 ppf: 575 ppct: 7 formats: tig: atl: Explaining Neural Signals in Human Visual Cortex With an Associative Learning Model. aug: au: Jiang, Jiefeng Schmajuk, Nestor Egner, Tobias affil: Department of Psychology & Neuroscience and Center for Cognitive Neuroscience, Duke University, LSRC Box 90999, Durham, NC 27708. su: Cognition Attention Visual cortex Visual learning Stochastic learning models Paired associate learning Brain function localization Magnetic resonance imaging of the brain sug: subj: Cognition Attention Visual cortex Visual learning Stochastic learning models Paired associate learning Brain function localization Magnetic resonance imaging of the brain keyword: associative learning attention fMRI predictive coding visual cortex associative learning attention fMRI predictive coding visual cortex ab: "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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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