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9 a.m. - 5 p.m.
Phone: (843) 766-6635
Wando Mount Pleasant Library
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Village Library
9 a.m. - 1 p.m.
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St. Paul's/Hollywood Library
9 a.m. - 5 p.m.
Phone: (843) 889-3300
Otranto Road Library
9 a.m. - 5 p.m.
Phone: (843) 572-4094
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Hurd/St. Andrews Library
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Folly Beach Library
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*open the 2nd and 4th Saturday
*open the 2nd and 4th Saturday
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Explaining Neural Signals in Human Visual Cortex With an Associative Learning Model.
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- Author(s): Jiang, Jiefeng1; Schmajuk, Nestor1; Egner, Tobias1
- Source:
Behavioral Neuroscience. Aug2012, Vol. 126 Issue 4, p575-581. 7p.- Subject Terms:
- Source:
- Additional Information
- 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. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Behavioral Neuroscience is the property of American Psychological Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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