An Adaptive Approach to Human Decision Making: Learning Theory, Decision Theory, and Human Performance
This article describes a general model of decision rule learning, the rule competition model, composed of 2 parts: an adaptive network model that describes how individuals learn to predict the payoffs produced by applying each decision rule for any given situation and a hill-climbing model that desc...
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| Veröffentlicht in: | Journal of experimental psychology. General 1992-06, Vol.121 (2), p.177-194 |
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| container_title | Journal of experimental psychology. General |
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| creator | Busemeyer, Jerome R Myung, In Jae |
| description | This article describes a general model of decision rule learning, the rule competition model, composed of 2 parts: an adaptive network model that describes how individuals learn to predict the payoffs produced by applying each decision rule for any given situation and a hill-climbing model that describes how individuals learn to fine tune each rule by adjusting its parameters. The model was tested and compared with other models in 3 experiments on probabilistic categorization. The first experiment was designed to test the adaptive network model using a probability learning task, the second was designed to test the parameter search process using a criterion learning task, and the third was designed to test both parts of the model simultaneously by using a task that required learning both category rules and cutoff criteria. |
| doi_str_mv | 10.1037/0096-3445.121.2.177 |
| format | Article |
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The model was tested and compared with other models in 3 experiments on probabilistic categorization. The first experiment was designed to test the adaptive network model using a probability learning task, the second was designed to test the parameter search process using a criterion learning task, and the third was designed to test both parts of the model simultaneously by using a task that required learning both category rules and cutoff criteria.</description><identifier>ISSN: 0096-3445</identifier><identifier>EISSN: 1939-2222</identifier><identifier>DOI: 10.1037/0096-3445.121.2.177</identifier><identifier>CODEN: JPGEDD</identifier><language>eng</language><publisher>Washington, DC: American Psychological Association</publisher><subject>Adjustment ; Biological and medical sciences ; Cognition & reasoning ; Cognition. Intelligence ; Cognitive Processes ; Decision Making ; Fundamental and applied biological sciences. Psychology ; Human ; Learning Theory ; Psychology ; Psychology. 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The first experiment was designed to test the adaptive network model using a probability learning task, the second was designed to test the parameter search process using a criterion learning task, and the third was designed to test both parts of the model simultaneously by using a task that required learning both category rules and cutoff criteria.</description><subject>Adjustment</subject><subject>Biological and medical sciences</subject><subject>Cognition & reasoning</subject><subject>Cognition. Intelligence</subject><subject>Cognitive Processes</subject><subject>Decision Making</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Human</subject><subject>Learning Theory</subject><subject>Psychology</subject><subject>Psychology. Psychoanalysis. Psychiatry</subject><subject>Psychology. Psychophysiology</subject><subject>Reasoning. 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Intelligence</topic><topic>Cognitive Processes</topic><topic>Decision Making</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Human</topic><topic>Learning Theory</topic><topic>Psychology</topic><topic>Psychology. Psychoanalysis. Psychiatry</topic><topic>Psychology. Psychophysiology</topic><topic>Reasoning. Problem solving</topic><topic>Social research</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Busemeyer, Jerome R</creatorcontrib><creatorcontrib>Myung, In Jae</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Periodicals Index Online Segment 06</collection><collection>Periodicals Index Online Segment 30</collection><collection>Periodicals Index Online</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - West</collection><collection>Primary Sources Access (Plan D) - International</collection><collection>Primary Sources Access & Build (Plan A) - MEA</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Midwest</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Northeast</collection><collection>Primary Sources Access (Plan D) - Southeast</collection><collection>Primary Sources Access (Plan D) - North Central</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Southeast</collection><collection>Primary Sources Access (Plan D) - South Central</collection><collection>Primary Sources Access & Build (Plan A) - UK / I</collection><collection>Primary Sources Access (Plan D) - Canada</collection><collection>Primary Sources Access (Plan D) - EMEALA</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - North Central</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - South Central</collection><collection>Primary Sources Access & Build (Plan A) - International</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - International</collection><collection>Primary Sources Access (Plan D) - West</collection><collection>Periodicals Index Online Segments 1-50</collection><collection>Primary Sources Access (Plan D) - APAC</collection><collection>Primary Sources Access (Plan D) - Midwest</collection><collection>Primary Sources Access (Plan D) - MEA</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Canada</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - UK / I</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - EMEALA</collection><collection>Primary Sources Access & Build (Plan A) - APAC</collection><collection>Primary Sources Access & Build (Plan A) - Canada</collection><collection>Primary Sources Access & Build (Plan A) - West</collection><collection>Primary Sources Access & Build (Plan A) - EMEALA</collection><collection>Primary Sources Access (Plan D) - Northeast</collection><collection>Primary Sources Access & Build (Plan A) - Midwest</collection><collection>Primary Sources Access & Build (Plan A) - North Central</collection><collection>Primary Sources Access & Build (Plan A) - Northeast</collection><collection>Primary Sources Access & Build (Plan A) - South Central</collection><collection>Primary Sources Access & Build (Plan A) - Southeast</collection><collection>Primary Sources Access (Plan D) - UK / I</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - APAC</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - MEA</collection><collection>PsycArticles (via ProQuest)</collection><collection>ProQuest One Psychology</collection><jtitle>Journal of experimental psychology. General</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Busemeyer, Jerome R</au><au>Myung, In Jae</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Adaptive Approach to Human Decision Making: Learning Theory, Decision Theory, and Human Performance</atitle><jtitle>Journal of experimental psychology. General</jtitle><date>1992-06-01</date><risdate>1992</risdate><volume>121</volume><issue>2</issue><spage>177</spage><epage>194</epage><pages>177-194</pages><issn>0096-3445</issn><eissn>1939-2222</eissn><coden>JPGEDD</coden><abstract>This article describes a general model of decision rule learning, the rule competition model, composed of 2 parts: an adaptive network model that describes how individuals learn to predict the payoffs produced by applying each decision rule for any given situation and a hill-climbing model that describes how individuals learn to fine tune each rule by adjusting its parameters. The model was tested and compared with other models in 3 experiments on probabilistic categorization. The first experiment was designed to test the adaptive network model using a probability learning task, the second was designed to test the parameter search process using a criterion learning task, and the third was designed to test both parts of the model simultaneously by using a task that required learning both category rules and cutoff criteria.</abstract><cop>Washington, DC</cop><pub>American Psychological Association</pub><doi>10.1037/0096-3445.121.2.177</doi><tpages>18</tpages></addata></record> |
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| ispartof | Journal of experimental psychology. General, 1992-06, Vol.121 (2), p.177-194 |
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| language | eng |
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| source | EBSCOhost APA PsycARTICLES; Periodicals Index Online |
| subjects | Adjustment Biological and medical sciences Cognition & reasoning Cognition. Intelligence Cognitive Processes Decision Making Fundamental and applied biological sciences. Psychology Human Learning Theory Psychology Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Reasoning. Problem solving Social research |
| title | An Adaptive Approach to Human Decision Making: Learning Theory, Decision Theory, and Human Performance |
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