Learning what to see in a changing world

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 o...
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Autor*in:	Katharina eSchmack [verfasserIn] Veith eWeilnhammer [verfasserIn] Jakob eHeinzle [verfasserIn] Klaas Enno Stephan [verfasserIn] Philipp eSterzer [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Association Learning Visual Perception bistable perception sensory memory Bayesian Brain Hierarchical Gaussian Filter

Übergeordnetes Werk:	In: Frontiers in Human Neuroscience - Frontiers Media S.A., 2008, 10(2016)
Übergeordnetes Werk:	volume:10 ; year:2016

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fnhum.2016.00263

Katalog-ID:	DOAJ078173035

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allfieldsSound	10.3389/fnhum.2016.00263 doi (DE-627)DOAJ078173035 (DE-599)DOAJ6ca2fe915813408c82cdd8836026d6dd DE-627 ger DE-627 rakwb eng RC321-571 Katharina eSchmack verfasserin aut Learning what to see in a changing world 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 modelling revealed that perception was best explained by amodel 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. Association Learning Visual Perception bistable perception sensory memory Bayesian Brain Hierarchical Gaussian Filter Neurosciences. Biological psychiatry. Neuropsychiatry Veith eWeilnhammer verfasserin aut Jakob eHeinzle verfasserin aut Klaas Enno Stephan verfasserin aut Philipp eSterzer verfasserin aut In Frontiers in Human Neuroscience Frontiers Media S.A., 2008 10(2016) (DE-627)56601243X (DE-600)2425477-0 16625161 nnns volume:10 year:2016 https://doi.org/10.3389/fnhum.2016.00263 kostenfrei https://doaj.org/article/6ca2fe915813408c82cdd8836026d6dd kostenfrei http://journal.frontiersin.org/Journal/10.3389/fnhum.2016.00263/full kostenfrei https://doaj.org/toc/1662-5161 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2016
language	English
source	In Frontiers in Human Neuroscience 10(2016) volume:10 year:2016
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institution	findex.gbv.de
topic_facet	Association Learning Visual Perception bistable perception sensory memory Bayesian Brain Hierarchical Gaussian Filter Neurosciences. Biological psychiatry. Neuropsychiatry
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container_title	Frontiers in Human Neuroscience
authorswithroles_txt_mv	Katharina eSchmack @@aut@@ Veith eWeilnhammer @@aut@@ Jakob eHeinzle @@aut@@ Klaas Enno Stephan @@aut@@ Philipp eSterzer @@aut@@
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callnumber-first	R - Medicine
author	Katharina eSchmack
spellingShingle	Katharina eSchmack misc RC321-571 misc Association Learning misc Visual Perception misc bistable perception misc sensory memory misc Bayesian Brain misc Hierarchical Gaussian Filter misc Neurosciences. Biological psychiatry. Neuropsychiatry Learning what to see in a changing world
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topic_title	RC321-571 Learning what to see in a changing world Association Learning Visual Perception bistable perception sensory memory Bayesian Brain Hierarchical Gaussian Filter
topic	misc RC321-571 misc Association Learning misc Visual Perception misc bistable perception misc sensory memory misc Bayesian Brain misc Hierarchical Gaussian Filter misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc Association Learning misc Visual Perception misc bistable perception misc sensory memory misc Bayesian Brain misc Hierarchical Gaussian Filter misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title_sort	learning what to see in a changing world
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title_auth	Learning what to see in a changing world
abstract	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 modelling revealed that perception was best explained by amodel 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.
abstractGer	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 modelling revealed that perception was best explained by amodel 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.
abstract_unstemmed	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 modelling revealed that perception was best explained by amodel 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.
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title_short	Learning what to see in a changing world
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score	7.4010277

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