Literature DB >> 18229473

Does response scaling cause the generalized context model to mimic a prototype model?

Jay I Myung1, Mark A Pitt, Daniel J Navarro.   

Abstract

Smith and Minda (1998, 2002) argued that the response scaling parameter y in the exemplar-based generalized context model (GCM) makes the model unnecessarily complex and allows it to mimic the behavior of a prototype model. We evaluated this criticism in two ways. First, we estimated the complexity of the GCM with and without the yparameter and also compared its complexity to that of a prototype model. Next, we assessed the extent to which the models mimic each other, using two experimental designs (Nosofsky & Zaki, 2002, Experiment 3; Smith & Minda, 1998, Experiment 2), chosen because these designs are thought to differ in the degree to which they can discriminate the models. The results show that y can increase the complexity of the GCM, but this complexity does not necessarily allow mimicry. Furthermore, if statistical model selection methods such as minimum description length are adopted as the measure of model performance, the models will be highly discriminable, irrespective of design.

Mesh:

Year:  2007        PMID: 18229473      PMCID: PMC2430630          DOI: 10.3758/bf03193089

Source DB:  PubMed          Journal:  Psychon Bull Rev        ISSN: 1069-9384


  12 in total

1.  GUEST EDITORS' INTRODUCTION.

Authors: 
Journal:  J Math Psychol       Date:  2000-03       Impact factor: 2.223

2.  Prototype and exemplar accounts of category learning and attentional allocation: a reassessment.

Authors:  Safa R Zaki; Robert M Nosofsky; Roger D Stanton; Andrew L Cohen
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2003-11       Impact factor: 3.051

Review 3.  Toward a method of selecting among computational models of cognition.

Authors:  Mark A Pitt; In Jae Myung; Shaobo Zhang
Journal:  Psychol Rev       Date:  2002-07       Impact factor: 8.934

4.  Exemplar and prototype models revisited: response strategies, selective attention, and stimulus generalization.

Authors:  Robert M Nosofsky; Safa R Zaki
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2002-09       Impact factor: 3.051

5.  Distinguishing prototype-based and exemplar-based processes in dot-pattern category learning.

Authors:  J David Smith; John Paul Minda
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2002-07       Impact factor: 3.051

6.  Assessing the distinguishability of models and the informativeness of data.

Authors:  Daniel J Navarro; Mark A Pitt; In Jae Myung
Journal:  Cogn Psychol       Date:  2004-08       Impact factor: 3.468

7.  Exemplars, prototypes, and the flexibility of classification models.

Authors:  Henrik Olsson; Pia Wennerholm; Urban Lyxzèn
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2004-07       Impact factor: 3.051

8.  On the Theory of Scales of Measurement.

Authors:  S S Stevens
Journal:  Science       Date:  1946-06-07       Impact factor: 47.728

9.  Comparing prototype-based and exemplar-based accounts of category learning and attentional allocation.

Authors:  John Paul Minda; J David Smith
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2002-03       Impact factor: 3.051

10.  Attention, similarity, and the identification-categorization relationship.

Authors:  R M Nosofsky
Journal:  J Exp Psychol Gen       Date:  1986-03
View more
  10 in total

Review 1.  Minimum description length model selection of multinomial processing tree models.

Authors:  Hao Wu; Jay I Myung; William H Batchelder
Journal:  Psychon Bull Rev       Date:  2010-06

2.  The diagnosticity of individual data for model selection: comparing signal-detection models of recognition memory.

Authors:  Yoonhee Jang; John T Wixted; David E Huber
Journal:  Psychon Bull Rev       Date:  2011-08

3.  Recognition memory models and binary-response ROCs: a comparison by minimum description length.

Authors:  David Kellen; Karl Christoph Klauer; Arndt Bröder
Journal:  Psychon Bull Rev       Date:  2013-08

Review 4.  Prototypes, exemplars, and the natural history of categorization.

Authors:  J David Smith
Journal:  Psychon Bull Rev       Date:  2014-04

5.  On the Minimum Description Length Complexity of Multinomial Processing Tree Models.

Authors:  Hao Wu; Jay I Myung; William H Batchelder
Journal:  J Math Psychol       Date:  2010-06       Impact factor: 2.223

6.  Optimal experimental design for model discrimination.

Authors:  Jay I Myung; Mark A Pitt
Journal:  Psychol Rev       Date:  2009-07       Impact factor: 8.934

7.  Testing signal-detection models of yes/no and two-alternative forced-choice recognition memory.

Authors:  Yoonhee Jang; John T Wixted; David E Huber
Journal:  J Exp Psychol Gen       Date:  2009-05

8.  Evaluating models of remember-know judgments: complexity, mimicry, and discriminability.

Authors:  Andrew L Cohen; Caren M Rotello; Neil A Macmillan
Journal:  Psychon Bull Rev       Date:  2008-10

9.  A Computational Model of Context-Dependent Encodings During Category Learning.

Authors:  Paulo F Carvalho; Robert L Goldstone
Journal:  Cogn Sci       Date:  2022-04

10.  Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions.

Authors:  Petko Kusev; Paul van Schaik; Krasimira Tsaneva-Atanasova; Asgeir Juliusson; Nick Chater
Journal:  Cogn Sci       Date:  2017-04-06
  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.