Literature DB >> 19756251

The optimal level of fuzz: Case studies in a methodology for psychological research.

Arthur B Markman1, Jennifer S Beer, Lisa R Grimm, Jonathan R Rein, W Todd Maddox.   

Abstract

Cognitive Science research is hard to conduct, because researchers must take phenomena from the world and turn them into laboratory tasks for which a reasonable level of experimental control can be achieved. Consequently, research necessarily makes tradeoffs between internal validity (experimental control) and external validity (the degree to which a task represents behavior outside of the lab). Researchers are thus seeking the best possible tradeoff between these constraints, which we refer to as the optimal level of fuzz. We present two principles for finding the optimal level of fuzz, in research, and then illustrate these principles using research from motivation, individual differences, and cognitive neuroscience.

Entities:  

Year:  2009        PMID: 19756251      PMCID: PMC2743110          DOI: 10.1080/09528130903065380

Source DB:  PubMed          Journal:  J Exp Theor Artif Intell        ISSN: 0952-813X            Impact factor:   2.340


  35 in total

Review 1.  Thirty categorization results in search of a model.

Authors:  J D Smith; J P Minda
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2000-01       Impact factor: 3.051

Review 2.  Culture and systems of thought: holistic versus analytic cognition.

Authors:  R E Nisbett; K Peng; I Choi; A Norenzayan
Journal:  Psychol Rev       Date:  2001-04       Impact factor: 8.934

3.  Category number impacts rule-based but not information-integration category learning: further evidence for dissociable category-learning systems.

Authors:  W Todd Maddox; J Vincent Filoteo; Kelli D Hejl; A David Ing
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2004-01       Impact factor: 3.051

4.  Exemplar theory's predicted typicality gradient can be tested and disconfirmed.

Authors:  J David Smith
Journal:  Psychol Sci       Date:  2002-09

5.  Formal learning theory dissociates brain regions with different temporal integration.

Authors:  Jan Gläscher; Christian Büchel
Journal:  Neuron       Date:  2005-07-21       Impact factor: 17.173

6.  A neurobiological theory of automaticity in perceptual categorization.

Authors:  F Gregory Ashby; John M Ennis; Brian J Spiering
Journal:  Psychol Rev       Date:  2007-07       Impact factor: 8.934

7.  Striatal contributions to category learning: quantitative modeling of simple linear and complex nonlinear rule learning in patients with Parkinson's disease.

Authors:  W T Maddox; J V Filoteo
Journal:  J Int Neuropsychol Soc       Date:  2001-09       Impact factor: 2.892

8.  Using brain imaging to extract the structure of complex events at the rational time band.

Authors:  John R Anderson; Yulin Qin
Journal:  J Cogn Neurosci       Date:  2008-09       Impact factor: 3.225

9.  A neuropsychological theory of multiple systems in category learning.

Authors:  F G Ashby; L A Alfonso-Reese; A U Turken; E M Waldron
Journal:  Psychol Rev       Date:  1998-07       Impact factor: 8.934

10.  Differential Effects of Regulatory Fit on Category Learning.

Authors:  Lisa R Grimm; Arthur B Markman; W Todd Maddox; Grant C Baldwin
Journal:  J Exp Soc Psychol       Date:  2008-05
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  2 in total

1.  Motivational Influences on Cognitive Performance in Children: Focus Over Fit.

Authors:  Darrell A Worthy; Caitlin C Brez; Arthur B Markman; W Todd Maddox
Journal:  J Cogn Dev       Date:  2011

2.  The Motivation-Cognition Interface in Learning and Decision-Making.

Authors:  W Todd Maddox; Arthur B Markman
Journal:  Curr Dir Psychol Sci       Date:  2010-04-01
  2 in total

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