Literature DB >> 24420728

Is state-trace analysis an appropriate tool for assessing the number of cognitive systems?

F Gregory Ashby1.   

Abstract

There is now much evidence that humans have multiple memory systems, and evidence is also building that other cognitive processes are mediated by multiple systems. Even so, several recent articles have questioned the existence of multiple cognitive systems, and a number of these have based their arguments on results from state-trace analysis. State-trace analysis was not developed for this purpose but, rather, to identify data sets that are consistent with variation in a single parameter. All previous applications have assumed that state-trace plots in which the data fall on separate curves rule out any model in which only a single parameter varies across the two tasks under study. Unfortunately, this assumption is incorrect. Models in which only one parameter varies can generate any type of state-trace plot, as can models in which two or more parameters vary. In addition, it is straightforward to show that both single-system and multiple-systems models can generate state-trace plots that are considered in the literature to be consistent with either one or multiple cognitive systems. Thus, without additional information, there is no empirical state-trace plot that supports any inferences about the number of underlying parameters or systems.

Entities:  

Mesh:

Year:  2014        PMID: 24420728      PMCID: PMC4097983          DOI: 10.3758/s13423-013-0578-x

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


  22 in total

1.  Stimulus and response generalization: tests of a model relating generalization to distance in psychological space.

Authors:  R N SHEPARD
Journal:  J Exp Psychol       Date:  1958-06

2.  The design and analysis of state-trace experiments.

Authors:  Melissa Prince; Scott Brown; Andrew Heathcote
Journal:  Psychol Methods       Date:  2011-10-31

3.  The dimensionality of perceptual category learning: a state-trace analysis.

Authors:  Ben R Newell; John C Dunn; Michael Kalish
Journal:  Mem Cognit       Date:  2010-07

4.  Delayed feedback disrupts the procedural-learning system but not the hypothesis-testing system in perceptual category learning.

Authors:  W Todd Maddox; A David Ing
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2005-01       Impact factor: 3.051

5.  Dual-task interference in perceptual category learning.

Authors:  Dagmar Zeithamova; W Todd Maddox
Journal:  Mem Cognit       Date:  2006-03

6.  Feedback interference and dissociations of classification: evidence against the multiple-learning-systems hypothesis.

Authors:  Roger D Stanton; Robert M Nosofsky
Journal:  Mem Cognit       Date:  2007-10

7.  Dimensions in data: testing psychological models using state-trace analysis.

Authors:  Ben R Newell; John C Dunn
Journal:  Trends Cogn Sci       Date:  2008-07-05       Impact factor: 20.229

8.  When more is less: feedback effects in perceptual category learning.

Authors:  W Todd Maddox; Bradley C Love; Brian D Glass; J Vincent Filoteo
Journal:  Cognition       Date:  2008-05-01

Review 9.  The dimensionality of the remember-know task: a state-trace analysis.

Authors:  John C Dunn
Journal:  Psychol Rev       Date:  2008-04       Impact factor: 8.934

10.  Interactions between declarative and procedural-learning categorization systems.

Authors:  F Gregory Ashby; Matthew J Crossley
Journal:  Neurobiol Learn Mem       Date:  2010-03-19       Impact factor: 2.877

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  8 in total

1.  The time course of explicit and implicit categorization.

Authors:  J David Smith; Alexandria C Zakrzewski; Eric R Herberger; Joseph Boomer; Jessica L Roeder; F Gregory Ashby; Barbara A Church
Journal:  Atten Percept Psychophys       Date:  2015-10       Impact factor: 2.199

2.  Declarative strategies persist under increased cognitive load.

Authors:  Matthew J Crossley; Erick J Paul; Jessica L Roeder; F Gregory Ashby
Journal:  Psychon Bull Rev       Date:  2016-02

3.  REFRESH: A new approach to modeling dimensional biases in perceptual similarity and categorization.

Authors:  Adam N Sanborn; Katherine Heller; Joseph L Austerweil; Nick Chater
Journal:  Psychol Rev       Date:  2021-09-13       Impact factor: 8.934

4.  State-trace analysis can be an appropriate tool for assessing the number of cognitive systems: a reply to Ashby (2014).

Authors:  John C Dunn; Michael L Kalish; Ben R Newell
Journal:  Psychon Bull Rev       Date:  2014-08

5.  Toward a dual-learning systems model of speech category learning.

Authors:  Bharath Chandrasekaran; Seth R Koslov; W T Maddox
Journal:  Front Psychol       Date:  2014-07-31

6.  In Search of the Factors Behind Naive Sentence Judgments: A State Trace Analysis of Grammaticality and Acceptability Ratings.

Authors:  Steven Langsford; Rachel G Stephens; John C Dunn; Richard L Lewis
Journal:  Front Psychol       Date:  2019-12-20

7.  Multiple latent variables but functionally dependent output mappings underlying the recognition of own- and other-race faces for Chinese individuals: Evidence from state-trace analysis.

Authors:  Wei Liu; Yuxue Jia
Journal:  Front Psychol       Date:  2022-07-28

8.  One Giant Leap for Categorizers: One Small Step for Categorization Theory.

Authors:  J David Smith; Shawn W Ell
Journal:  PLoS One       Date:  2015-09-02       Impact factor: 3.240

  8 in total

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