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Computational modeling of individual differences in short term memory search

Computational modeling of individual differences in ...

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dc.contributor.author Chuderski, Adam [SAP13015922] pl
dc.contributor.author Stettner, Zbigniew pl
dc.contributor.author Orzechowski, Jarosław [SAP11017112] pl
dc.date.accessioned 2016-10-24T06:53:09Z
dc.date.available 2016-10-24T06:53:09Z
dc.date.issued 2007 pl
dc.identifier.issn 2214-4366 pl
dc.identifier.uri http://ruj.uj.edu.pl/xmlui/handle/item/31642
dc.language eng pl
dc.rights Dodaję tylko opis bibliograficzny *
dc.rights.uri *
dc.title Computational modeling of individual differences in short term memory search pl
dc.type JournalArticle pl
dc.description.physical 161-173 pl
dc.abstract.en Modeling of individual or group differences, believed to be a powerful test for computational models, is still rare in current cognitive science. In this paper, we discuss alternative approaches to the computational modeling of both qualitative and quantitative differences among individuals as well as groups of individuals. Then, an example is presented of how accounting for individual differences in short term memory (STM) search can bring us insight into cognitive processes underlying this phenomenon, insight that otherways would be impossible. The two-phase computational model of memory search implements the idea of working memory (WM) focus of attention (FA): due to updating process a few items may be actively kept and easily accessed in ACT-R goal buffer. FA is being scanned serially first, and if the scan result is negative, a parallel chunk retrieval from active part of declarative memory outside the FA may run with certain probability. The model aptly simulates steep decrease in accuracy as well as steep increase in latency for responses to five most recent stimuli. The model also predicts the observed effect of faster negative responses than positive responses to less recent stimuli. Most important, with manipulation to only one of its parameters (i.e., the capacity of FA) our model is able to predict 94% of variance for two groups of participants that differed in latency patterns (i.e., ‘serial-like’ vs. ‘parallel-like’ groups) of the search process. pl
dc.subject.en focus of attention pl
dc.subject.en short term memory pl
dc.subject.en computational modeling pl
dc.subject.en individual differences pl
dc.description.volume 8 pl
dc.description.number 3 pl
dc.identifier.doi 10.1016/j.cogsys.2007.06.001 pl
dc.identifier.eissn 1389-0417 pl
dc.title.journal Cognitive Systems Research pl
dc.language.container eng pl
dc.affiliation Wydział Filozoficzny : Instytut Psychologii pl
dc.affiliation Wydział Filozoficzny : Instytut Filozofii pl
dc.subtype Article pl
dc.rights.original bez licencji pl


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