A Dynamic Model of Reasoning and Memory.
Previous models of category-based induction have neglected how the process of induction unfolds over time. We conceive of induction as a dynamic process and provide the first fine-grained examination of the distribution of response times observed in inductive reasoning. We used these data to develop...
| Publicado en: | Journal of Experimental Psychology. General Vol. 145; no. 2; pp. 155 - 181 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
Feb2016
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=112684574&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 112684574 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00963445 EPG jtl: Journal of Experimental Psychology. General issn: 00963445 maglogo: N pubinfo: dt: Feb2016 vid: 145 iid: 2 pid: 34 pub: American Psychological Association artinfo: ui: 112684574 10.1037/xge0000113 ppf: 155 ppct: 26 formats: tig: atl: A Dynamic Model of Reasoning and Memory. aug: au: Hawkins, Guy E. Hayes, Brett K. Heit, Evan affil: University of New South Wales University of Amsterdam University of California, Merced su: Induction (Logic) Generalization Sequential analysis sug: subj: Induction (Logic) Generalization Sequential analysis keyword: hierarchical Bayesian analysis inductive reasoning mathematical model recognition memory response time hierarchical Bayesian analysis inductive reasoning mathematical model recognition memory response time ab: Previous models of category-based induction have neglected how the process of induction unfolds over time. We conceive of induction as a dynamic process and provide the first fine-grained examination of the distribution of response times observed in inductive reasoning. We used these data to develop and empirically test the first major quantitative modeling scheme that simultaneously accounts for inductive decisions and their time course. The model assumes that knowledge of similarity relations among novel test probes and items stored in memory drive an accumulation-to-bound sequential sampling process: Test probes with high similarity to studied exemplars are more likely to trigger a generalization response, and more rapidly, than items with low exemplar similarity. We contrast data and model predictions for inductive decisions with a recognition memory task using a common stimulus set. Hierarchical Bayesian analyses across 2 experiments demonstrated that inductive reasoning and recognition memory primarily differ in the threshold to trigger a decision: Observers required less evidence to make a property generalization judgment (induction) than an identity statement about a previously studied item (recognition). Experiment 1 and a condition emphasizing decision speed in Experiment 2 also found evidence that inductive decisions use lower quality similarity-based information than recognition. The findings suggest that induction might represent a less cautious form of recognition. We conclude that sequential sampling models grounded in exemplar-based similarity, combined with hierarchical Bayesian analysis, provide a more fine-grained and informative analysis of the processes involved in inductive reasoning than is possible solely through examination of choice data. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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