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Theoretical Investigations in Visual Short-Term Memory : = Structured Probabilistic Representations, Model Mismatch and Neural Population Coding.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Theoretical Investigations in Visual Short-Term Memory :/
其他題名:	Structured Probabilistic Representations, Model Mismatch and Neural Population Coding.
作者:	Orhan, A. Emin.
面頁冊數:	1 online resource (292 pages)
附註:	Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.
標題:	Experimental psychology. -
電子資源:	click for full text (PQDT)
ISBN:	9781303505485

Theoretical Investigations in Visual Short-Term Memory : = Structured Probabilistic Representations, Model Mismatch and Neural Population Coding.
Orhan, A. Emin.

Theoretical Investigations in Visual Short-Term Memory :Structured Probabilistic Representations, Model Mismatch and Neural Population Coding. - 1 online resource (292 pages)

Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.

Thesis (Ph.D.)--University of Rochester, 2013.

Includes bibliographical references

Visual short-term memory (VSTM) makes a stable and coherent perception of the world possible by maintaining visual information about a scene as it was a moment ago. As VSTM is widely considered to be a severely limited form of memory, it is important to know what kinds of information about a scene can or cannot be held in VSTM. This thesis proposes a probabilistic clustering model that attempts to capture the representations that subjects encode from brief presentations of simple visual scenes typically used in VSTM studies (Chapter 2). The model explains a number of previously observed biases in VSTM and predicts dependencies between representations of different stimuli that should decrease with the difference between their feature values. We show that results from three experiments are qualitatively in agreement with this novel prediction of the model. We further show that this type of dependency can arise as a natural consequence of the simultaneous encoding of multiple stimuli in a population of neurons with correlated responses (Chapter 4). We also investigate the consequences of possible mismatches, of the type demonstrated in Chapter 2, between the internal model that subjects might use to encode simple visual displays and the model actually used by the experimenter to generate the stimuli (Chapter 3). We tentatively suggest that such mismatches can arise due to the long-term adaptation of the visual system to the stimulus statistics in the natural environment which is generally quite different from the stimulus statistics used in VSTM studies. Remarkably, we show that this type of mismatch alone, without assuming any resource or capacity limitation, can explain some of the main qualitative characteristics of performance limitations observed in VSTM studies typically attributed to capacity limitations. Finally, we take a critical look at the use of impoverished visual stimuli that lack the rich statistical structure displayed by stimuli that are more representative of the natural environment and discuss how the study of VSTM can be extended to ecologically more realistic stimuli, as well as possible implications of such an extension (Chapter 5). Taken together, this thesis proposes novel theoretical ideas at multiple levels of analysis, from computation to neural implementation, for an ecologically more realistic and behaviorally more adequate modeling of representations in VSTM.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781303505485Subjects--Topical Terms:

1180476
Experimental psychology.
Index Terms--Genre/Form:

554714
Electronic books.

Theoretical Investigations in Visual Short-Term Memory : = Structured Probabilistic Representations, Model Mismatch and Neural Population Coding.
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520 $a Visual short-term memory (VSTM) makes a stable and coherent perception of the world possible by maintaining visual information about a scene as it was a moment ago. As VSTM is widely considered to be a severely limited form of memory, it is important to know what kinds of information about a scene can or cannot be held in VSTM. This thesis proposes a probabilistic clustering model that attempts to capture the representations that subjects encode from brief presentations of simple visual scenes typically used in VSTM studies (Chapter 2). The model explains a number of previously observed biases in VSTM and predicts dependencies between representations of different stimuli that should decrease with the difference between their feature values. We show that results from three experiments are qualitatively in agreement with this novel prediction of the model. We further show that this type of dependency can arise as a natural consequence of the simultaneous encoding of multiple stimuli in a population of neurons with correlated responses (Chapter 4). We also investigate the consequences of possible mismatches, of the type demonstrated in Chapter 2, between the internal model that subjects might use to encode simple visual displays and the model actually used by the experimenter to generate the stimuli (Chapter 3). We tentatively suggest that such mismatches can arise due to the long-term adaptation of the visual system to the stimulus statistics in the natural environment which is generally quite different from the stimulus statistics used in VSTM studies. Remarkably, we show that this type of mismatch alone, without assuming any resource or capacity limitation, can explain some of the main qualitative characteristics of performance limitations observed in VSTM studies typically attributed to capacity limitations. Finally, we take a critical look at the use of impoverished visual stimuli that lack the rich statistical structure displayed by stimuli that are more representative of the natural environment and discuss how the study of VSTM can be extended to ecologically more realistic stimuli, as well as possible implications of such an extension (Chapter 5). Taken together, this thesis proposes novel theoretical ideas at multiple levels of analysis, from computation to neural implementation, for an ecologically more realistic and behaviorally more adequate modeling of representations in VSTM.
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