Literature DB >> 25637690

Modeling individual differences in response time and accuracy in numeracy.

Roger Ratcliff1, Clarissa A Thompson2, Gail McKoon3.   

Abstract

In the study of numeracy, some hypotheses have been based on response time (RT) as a dependent variable and some on accuracy, and considerable controversy has arisen about the presence or absence of correlations between RT and accuracy, between RT or accuracy and individual differences like IQ and math ability, and between various numeracy tasks. In this article, we show that an integration of the two dependent variables is required, which we accomplish with a theory-based model of decision making. We report data from four tasks: numerosity discrimination, number discrimination, memory for two-digit numbers, and memory for three-digit numbers. Accuracy correlated across tasks, as did RTs. However, the negative correlations that might be expected between RT and accuracy were not obtained; if a subject was accurate, it did not mean that they were fast (and vice versa). When the diffusion decision-making model was applied to the data (Ratcliff, 1978), we found significant correlations across the tasks between the quality of the numeracy information (drift rate) driving the decision process and between the speed/accuracy criterion settings, suggesting that similar numeracy skills and similar speed-accuracy settings are involved in the four tasks. In the model, accuracy is related to drift rate and RT is related to speed-accuracy criteria, but drift rate and criteria are not related to each other across subjects. This provides a theoretical basis for understanding why negative correlations were not obtained between accuracy and RT. We also manipulated criteria by instructing subjects to maximize either speed or accuracy, but still found correlations between the criteria settings between and within tasks, suggesting that the settings may represent an individual trait that can be modulated but not equated across subjects. Our results demonstrate that a decision-making model may provide a way to reconcile inconsistent and sometimes contradictory results in numeracy research.
Copyright © 2014 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Diffusion model; Individual differences; Number ability; Numeracy; Response time and accuracy

Mesh:

Year:  2015        PMID: 25637690      PMCID: PMC4353499          DOI: 10.1016/j.cognition.2014.12.004

Source DB:  PubMed          Journal:  Cognition        ISSN: 0010-0277


  46 in total

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Authors:  Roger Ratcliff; Gail McKoon
Journal:  Neural Comput       Date:  2008-04       Impact factor: 2.026

2.  Fast-dm: a free program for efficient diffusion model analysis.

Authors:  Andreas Voss; Jochen Voss
Journal:  Behav Res Methods       Date:  2007-11

Review 3.  Diffusion models in experimental psychology: a practical introduction.

Authors:  Andreas Voss; Markus Nagler; Veronika Lerche
Journal:  Exp Psychol       Date:  2013

4.  Is numerical comparison digital? Analogical and symbolic effects in two-digit number comparison.

Authors:  S Dehaene; E Dupoux; J Mehler
Journal:  J Exp Psychol Hum Percept Perform       Date:  1990-08       Impact factor: 3.332

5.  Using diffusion models to understand clinical disorders.

Authors:  Corey N White; Roger Ratcliff; Michael W Vasey; Gail McKoon
Journal:  J Math Psychol       Date:  2010-02-01       Impact factor: 2.223

6.  Association between individual differences in non-symbolic number acuity and math performance: a meta-analysis.

Authors:  Qixuan Chen; Jingguang Li
Journal:  Acta Psychol (Amst)       Date:  2014-02-26

7.  Parameter variability and distributional assumptions in the diffusion model.

Authors:  Roger Ratcliff
Journal:  Psychol Rev       Date:  2012-11-12       Impact factor: 8.934

8.  Children's mapping between symbolic and nonsymbolic representations of number.

Authors:  Eleanor Mundy; Camilla K Gilmore
Journal:  J Exp Child Psychol       Date:  2009-03-26

9.  Mapping numerical magnitudes onto symbols: the numerical distance effect and individual differences in children's mathematics achievement.

Authors:  Ian D Holloway; Daniel Ansari
Journal:  J Exp Child Psychol       Date:  2008-05-29

10.  Individual differences in non-verbal number acuity correlate with maths achievement.

Authors:  Justin Halberda; Michèle M M Mazzocco; Lisa Feigenson
Journal:  Nature       Date:  2008-09-07       Impact factor: 49.962

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  30 in total

1.  A Systematic Investigation of Accuracy and Response Time Based Measures Used to Index ANS Acuity.

Authors:  Julia Felicitas Dietrich; Stefan Huber; Elise Klein; Klaus Willmes; Silvia Pixner; Korbinian Moeller
Journal:  PLoS One       Date:  2016-09-16       Impact factor: 3.240

2.  Modeling Individual Differences in the Go/No-go Task with a Diffusion Model.

Authors:  Roger Ratcliff; Cynthia Huang-Pollock; Gail McKoon
Journal:  Decision (Wash D C )       Date:  2016-08-15

3.  More than simple facts: cross-linguistic differences in place-value processing in arithmetic fact retrieval.

Authors:  Julia Bahnmueller; Silke M Göbel; Silvia Pixner; Verena Dresen; Korbinian Moeller
Journal:  Psychol Res       Date:  2018-08-31

4.  Decision making on spatially continuous scales.

Authors:  Roger Ratcliff
Journal:  Psychol Rev       Date:  2018-11       Impact factor: 8.934

5.  Modeling numerosity representation with an integrated diffusion model.

Authors:  Roger Ratcliff; Gail McKoon
Journal:  Psychol Rev       Date:  2017-11-16       Impact factor: 8.934

6.  Retest reliability of the parameters of the Ratcliff diffusion model.

Authors:  Veronika Lerche; Andreas Voss
Journal:  Psychol Res       Date:  2016-04-23

7.  Examining aging and numerosity using an integrated diffusion model.

Authors:  Roger Ratcliff; Gail McKoon
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2020-07-30       Impact factor: 3.051

8.  Adults with Poor Reading Skills, Older Adults, and College Students: the Meanings They Understand During Reading Using a Diffusion Model Analysis.

Authors:  Gail McKoon; Roger Ratcliff
Journal:  J Mem Lang       Date:  2018-06-07       Impact factor: 3.059

9.  The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance.

Authors:  James M Shine; Patrick G Bissett; Peter T Bell; Oluwasanmi Koyejo; Joshua H Balsters; Krzysztof J Gorgolewski; Craig A Moodie; Russell A Poldrack
Journal:  Neuron       Date:  2016-09-29       Impact factor: 17.173

Review 10.  Diffusion Decision Model: Current Issues and History.

Authors:  Roger Ratcliff; Philip L Smith; Scott D Brown; Gail McKoon
Journal:  Trends Cogn Sci       Date:  2016-03-05       Impact factor: 20.229

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