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Computational level accounts of beli...

McDonnell, John V.

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Computational level accounts of belief formation and revision in humans.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational level accounts of belief formation and revision in humans./
Author:	McDonnell, John V.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2014,
Description:	181 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
Contained By:	Dissertation Abstracts International75-07B(E).
Subject:	Experimental psychology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3614889
ISBN:	9781303805837

Computational level accounts of belief formation and revision in humans.
McDonnell, John V.

Computational level accounts of belief formation and revision in humans. - Ann Arbor : ProQuest Dissertations & Theses, 2014 - 181 p.

Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.

Thesis (Ph.D.)--New York University, 2014.

This item is not available from ProQuest Dissertations & Theses.

The work presented here reflects an effort to understand how humans form and revise beliefs, using probabilistic models as a tool for building theories of belief revision. Cognitive models were developed in two domains: semi-supervised category learning and causal reasoning with inconsistent information. These models provide a computational-level account of human learning and reasoning. At their core, the models assert that human reasoning and learning processes may be represented as parameterized probability distributions that are updated in light of experience.

ISBN: 9781303805837Subjects--Topical Terms:

2144733
Experimental psychology.

Computational level accounts of belief formation and revision in humans.
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100 1 $a McDonnell, John V. $3 3347078
245 1 0 $a Computational level accounts of belief formation and revision in humans.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2014
300 $a 181 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
500 $a Adviser: Todd M. Gureckis.
502 $a Thesis (Ph.D.)--New York University, 2014.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a The work presented here reflects an effort to understand how humans form and revise beliefs, using probabilistic models as a tool for building theories of belief revision. Cognitive models were developed in two domains: semi-supervised category learning and causal reasoning with inconsistent information. These models provide a computational-level account of human learning and reasoning. At their core, the models assert that human reasoning and learning processes may be represented as parameterized probability distributions that are updated in light of experience.
520 $a In the case of semi-supervised learning, many existing category learning models imply predictions about how the presence of unlabeled items will affect the generalization of category labels. Existing work has led to an unclear picture of how people integrate across these two types of learning. Here, I present a modified version of Anderson's (1991) rational model of categorization that is able to capture these effects. The resulting model predicted effects on participants' generalization strategies in three experiments. Empirical evidence was found in favor of these predictions in each experiment.
520 $a In the case of causal reasoning, a model was developed with a focus on coherence in causal reasoning. When learning about a domain, participants may learn different facts about the world which have incoherent implications. However, if we assume that participants' beliefs are encoded as probability distributions, their judgments may reflect a selection process in which incoherent outcomes are excluded. Applying this insight to the example of causal reasoning, a paradigm was developed in which participants were provided with incoherent facts. As predicted, their judgments appeared to reflect adjustments in the direction of coherent beliefs.
520 $a The data for these projects was collected on participants recruited on the Internet using Amazon's Mechanical Turk service and administered via their own web browsers. To validate this online methodology as a tool for studying human cognition, work was also done to replicate classic studies from the categorization literature.
520 $a Taken together, these studies offer new insight into our understanding of how people adapt their behavior in light of experience.
590 $a School code: 0146.
650 4 $a Experimental psychology. $3 2144733
650 4 $a Cognitive psychology. $3 523881
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690 $a 0633
710 2 $a New York University. $b Psychology. $3 1025800
773 0 $t Dissertation Abstracts International $g 75-07B(E).
790 $a 0146
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3614889