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An Empirical Investigation for Patterns of Belief Change in Human Reasoning

Empirical methods have been used to test whether human reasoning conforms to models of reasoning in logic-based artificial intelligence. Particularly, studies have shown that human reasoning is consistent with non-monotonic logic and belief change. The former refers to models of reasoning where prev...

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Main Author: Baker, Clayton Kevin
Other Authors: Meyer, Thomas A
Format: Thesis
Language:English
Published: Department of Computer Science 2023
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access_status_str Open Access
author Baker, Clayton Kevin
author2 Meyer, Thomas A
author_browse Baker, Clayton Kevin
Meyer, Thomas A
author_facet Meyer, Thomas A
Baker, Clayton Kevin
author_sort Baker, Clayton Kevin
collection Thesis
description Empirical methods have been used to test whether human reasoning conforms to models of reasoning in logic-based artificial intelligence. Particularly, studies have shown that human reasoning is consistent with non-monotonic logic and belief change. The former refers to models of reasoning where previously drawn conclusions can be retracted when adding new information. The latter refers to the operations of revising and updating a set of beliefs, respectively, when presented with new information. The operation of revision assumes the world has remained the same and that only our knowledge about the world has changed. On the other side, update assumes that the world may have changed by the time the new information is added. Both operations require the resulting belief set to be consistent. Revision allows inconsistent beliefs to be deleted, while update acknowledges that the belief set may have been wrong if it contradicts the new information. Our work surveyed postulates of belief change with human reasoners. First, we studied the role of the postulates of belief revision and belief update in the literature. Next, we decomposed the postulates of revision and update into material implication statements, each containing a premise and a conclusion. We translated the premises and conclusions into English and surveyed the postulate translations with human reasoners. The main task of the surveys was for participants to judge the translated postulate components for plausibility. For our data analysis, we used statistical methods to test the relationship between the endorsement of the premises and the endorsement of the conclusion. For our data validation, we applied possibility theory to examine whether the relationship was significant for the broader population of English-speaking human reasoners. The results show that our participants' reasoning tends to be consistent with the postulates of belief revision and belief update when judging the premises and conclusion of the postulate separately.
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language eng
last_indexed 2026-06-10T12:36:09.046Z
license_str Not specified — see source repository
provenance_str_mv Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository
publishDate 2023
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spelling oai:open.uct.ac.za:11427/37063 An Empirical Investigation for Patterns of Belief Change in Human Reasoning Baker, Clayton Kevin Meyer, Thomas A Computer Science Empirical methods have been used to test whether human reasoning conforms to models of reasoning in logic-based artificial intelligence. Particularly, studies have shown that human reasoning is consistent with non-monotonic logic and belief change. The former refers to models of reasoning where previously drawn conclusions can be retracted when adding new information. The latter refers to the operations of revising and updating a set of beliefs, respectively, when presented with new information. The operation of revision assumes the world has remained the same and that only our knowledge about the world has changed. On the other side, update assumes that the world may have changed by the time the new information is added. Both operations require the resulting belief set to be consistent. Revision allows inconsistent beliefs to be deleted, while update acknowledges that the belief set may have been wrong if it contradicts the new information. Our work surveyed postulates of belief change with human reasoners. First, we studied the role of the postulates of belief revision and belief update in the literature. Next, we decomposed the postulates of revision and update into material implication statements, each containing a premise and a conclusion. We translated the premises and conclusions into English and surveyed the postulate translations with human reasoners. The main task of the surveys was for participants to judge the translated postulate components for plausibility. For our data analysis, we used statistical methods to test the relationship between the endorsement of the premises and the endorsement of the conclusion. For our data validation, we applied possibility theory to examine whether the relationship was significant for the broader population of English-speaking human reasoners. The results show that our participants' reasoning tends to be consistent with the postulates of belief revision and belief update when judging the premises and conclusion of the postulate separately. 2023-02-23T13:44:27Z 2023-02-23T13:44:27Z 2022 2023-02-20T12:14:54Z Master Thesis Masters MSc http://hdl.handle.net/11427/37063 eng application/pdf Department of Computer Science Faculty of Science
spellingShingle Computer Science
Baker, Clayton Kevin
An Empirical Investigation for Patterns of Belief Change in Human Reasoning
thesis_degree_str Master's
title An Empirical Investigation for Patterns of Belief Change in Human Reasoning
title_full An Empirical Investigation for Patterns of Belief Change in Human Reasoning
title_fullStr An Empirical Investigation for Patterns of Belief Change in Human Reasoning
title_full_unstemmed An Empirical Investigation for Patterns of Belief Change in Human Reasoning
title_short An Empirical Investigation for Patterns of Belief Change in Human Reasoning
title_sort empirical investigation for patterns of belief change in human reasoning
topic Computer Science
url http://hdl.handle.net/11427/37063
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