Learning what to see in a changing world

Verfasser / Beitragende:
[Katharina Schmack, Veith Weilnhammer, Jakob Heinzle, Klaas E Stephan, Philipp Sterzer]
Ort, Verlag, Jahr:
2016
Enthalten in:
Frontiers in Human Neuroscience:10:263.
Format:
Artikel (online)
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024 7 0 |a 10.5167/uzh-126408  |2 doi 
024 7 0 |a 10.3389/fnhum.2016.00263  |2 doi 
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084 |a 330  |2 ddc 
245 0 0 |a Learning what to see in a changing world  |h [Elektronische Daten]  |c [Katharina Schmack, Veith Weilnhammer, Jakob Heinzle, Klaas E Stephan, Philipp Sterzer] 
506 |a openAccess  |2 eu-repo 
520 3 |a Visual perception is strongly shaped by expectations, but it is poorly understood how such perceptual expectations are learned in our dynamic sensory environment. Here, we applied a Bayesian framework to investigate whether perceptual expectations are continuously updated from different aspects of ongoing experience. In two experiments, human observers performed an associative learning task in which rapidly changing expectations about the appearance of ambiguous stimuli were induced. We found that perception of ambiguous stimuli was biased by both learned associations and previous perceptual outcomes. Computational modeling revealed that perception was best explained by a model that continuously updated priors from associative learning and perceptual history and combined these priors with the current sensory information in a probabilistic manner. Our findings suggest that the construction of visual perception is a highly dynamic process that incorporates rapidly changing expectations from different sources in a manner consistent with Bayesian learning and inference. 
540 |a Creative Commons Attribution 4.0 International  |u http://creativecommons.org/licenses/by/4.0  |2 ethresearch 
690 7 |a Department of Economics  |2 zora 
690 7 |a Visual perception  |2 ethresearch 
690 7 |a Bayesian brain  |2 ethresearch 
690 7 |a Bistable perception  |2 ethresearch 
690 7 |a Associative learning  |2 ethresearch 
690 7 |a Sensory memory  |2 ethresearch 
690 7 |a Hierarchical Gaussian filter  |2 ethresearch 
700 1 |a Schmack  |D Katharina  |e joint author 
700 1 |a Weilnhammer  |D Veith  |e joint author 
700 1 |a Heinzle  |D Jakob  |e joint author 
700 1 |a Stephan  |D Klaas E.  |e joint author 
700 1 |a Sterzer  |D Philipp  |e joint author 
773 0 |t Frontiers in Human Neuroscience:10:263. 
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950 |B ZORA  |P 700  |E 1-  |a Weilnhammer  |D Veith  |e joint author 
950 |B ZORA  |P 700  |E 1-  |a Heinzle  |D Jakob  |e joint author 
950 |B ZORA  |P 700  |E 1-  |a Stephan  |D Klaas E.  |e joint author 
950 |B ZORA  |P 700  |E 1-  |a Sterzer  |D Philipp  |e joint author 
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950 |B ETHRESEARCH  |P 700  |E 1-  |a Schmack  |D Katharina  |e joint author 
950 |B ETHRESEARCH  |P 700  |E 1-  |a Weilnhammer  |D Veith  |e joint author 
950 |B ETHRESEARCH  |P 700  |E 1-  |a Heinzle  |D Jakob; id_orcid 0000-0001-5228-041X  |e joint author 
950 |B ETHRESEARCH  |P 700  |E 1-  |a Stephan  |D Klaas E.  |e joint author 
950 |B ETHRESEARCH  |P 700  |E 1-  |a Sterzer  |D Philipp  |e joint author 
950 |B ETHRESEARCH  |P 773  |E 0-  |t Frontiers in Human Neuroscience  |d Lausanne : Frontiers Research Foundation  |g 10, p. 263 
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949 |B ETHRESEARCH  |F ETHRESEARCH  |b ETHRESEARCH  |j Journal Article  |c Open access