An Empirical Investigation for Patterns of Belief Change in Human Reasoning
type: CreativeWork
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 …
abstract: 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 …
author: Baker, Clayton Kevin
keywords: Computer Science
date published: 2022
date modified: 2026-07-22T22:23:30Z
provider: University of Cape Town
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Department of Computer Science

PropertyValue
property ID: Handle
value: http://hdl.handle.net/11427/37063