Predicting true patterns of cognitive performance from noisy data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2004-12

AUTHORS

W. Todd Maddox, W. K. Estes

ABSTRACT

Starting from the premise that the purpose of cognitive modeling is to gain information about the cognitive processes of individuals, we develop a general theoretical framework for assessment of models on the basis of tests of the models' ability to yield information about the true performance patterns of individual subjects and the processes underlying them. To address the central problem that observed performance is a composite of true performance and error, we present formal derivations concerning inference from noisy data to true performance. Analyses of model fits to simulated data illustrate the usefulness of our approach for coping with difficult issues of model identifiability and testability. More... »

PAGES

1129-1135

References to SciGraph publications

  • 2003-03. Flexibility versus generalizability in model selection in BULLETIN OF THE PSYCHONOMIC SOCIETY
  • 2005-06. Risks of drawing inferences about cognitive processes from model fits to individual versus average performance in BULLETIN OF THE PSYCHONOMIC SOCIETY
  • 2002-03. Traps in the route to models of memory and decision in BULLETIN OF THE PSYCHONOMIC SOCIETY
  • 1997-06. A model for recognition memory: REM—retrieving effectively from memory in BULLETIN OF THE PSYCHONOMIC SOCIETY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.3758/bf03196748

    DOI

    http://dx.doi.org/10.3758/bf03196748

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1000595896

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/15875987


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