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Influence of Contextual Change on Time Perception, Probability Learning, and Decision-Making Dynamics.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Influence of Contextual Change on Time Perception, Probability Learning, and Decision-Making Dynamics./
作者:	Lositsky, Olga.
面頁冊數:	1 online resource (301 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
標題:	Neurosciences. -
電子資源:	click for full text (PQDT)
ISBN:	9780355323283

Influence of Contextual Change on Time Perception, Probability Learning, and Decision-Making Dynamics.
Lositsky, Olga.

Influence of Contextual Change on Time Perception, Probability Learning, and Decision-Making Dynamics. - 1 online resource (301 pages)

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)--Princeton University, 2017.

Includes bibliographical references

The principles that govern how memories are organized into categories, and how these categories then inform predictions for decision-making, constitute active areas of research. In this work, we investigated how people learn associations with different contexts (external cues that do not require responses but guide future behavior), how these associations are used to prepare decisions and how they influence time perception. The first study tested the hypothesis that people would interpret contextual changes in a story (including changes in spatial, social or temporal factors) as 'event boundaries', causing them to segment the story into distinct episodes. We predicted that more segmented intervals would appear longer in retrospect. Indeed, people's retrospective duration estimates were highly correlated with the number of event boundaries in an interval. Using fMRI, we found that activity patterns in brain regions important for binding spatial and temporal elements into episodes changed more during intervals with overestimated durations. The second study investigated how people learn when contextual cues signal a change in the situation. We hypothesized that people merge predictions across contexts into a single cluster when they are similar enough, and only form separate predictions for each context when truly necessary, as predicted by Bayesian inference models. We manipulated the degree to which cues predicted similar response or stimulus probabilities and built a new drift diffusion model to measure how people's expectations changed by cue. We found that people combined response and stimulus probabilities across cues when the probabilities were similar, despite maintaining entirely separate behavioral rules (stimulus-response associations) for each cue. Moreover, given the distortions in people's probability memories (caused by merging), model fits showed that contextual predictions were used almost optimally in decisions. The third study tested whether contextual associations were always used to prepare the decision proactively, or whether they could inform the decision reactively. Simulations from the diffusion model reveal that across-trial probability learning, as well as working memory failures, must be quantified in order to isolate reactive strategies in behavior, and that response times provide a cleaner index of strategy than error rates.

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

Mode of access: World Wide Web

ISBN: 9780355323283Subjects--Topical Terms:

593561
Neurosciences.
Index Terms--Genre/Form:

554714
Electronic books.

Influence of Contextual Change on Time Perception, Probability Learning, and Decision-Making Dynamics.
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