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A randomness perspective on intelligence processes

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  • Kang, Inhan
  • De Boeck, Paul
  • Partchev, Ivailo

Abstract

We study intelligence processes using a diffusion IRT model with random variability in cognitive model parameters: variability in drift rate (the trend of information accumulation toward a correct or incorrect response) and variability in starting point (from where the information accumulation starts). The random variation concerns randomness across person-item pairs and cannot be accounted for by individual and inter-item differences. Interestingly, the models explain the conditional dependencies between response accuracy and response time that are found in previous studies on cognitive ability tests, leading us to the formulation of a randomness perspective on intelligence processes. For an empirical test, we have analyzed verbal analogies data and matrix reasoning data using diffusion IRT models with different variability assumptions. The results indicate that 1) models with random variability fit better than models without, with implications for the conditional dependencies in both types of tasks; 2) for verbal analogies, random variation in drift rate seems to exist, which can be explained by person-by-item word knowledge differences; and 3) for both types of tasks, the starting point variation was also established, in line with the inductive nature of the tasks, requiring a sequential hypothesis testing process. Finally, the correlation of individual differences in drift rate and SAT suggests a meta-strategic choice of respondents to focus on accuracy rather than speed when they have a higher cognitive capacity and when the task is one for which investing in time pays off. This seems primarily the case for matrix reasoning and less so for verbal analogies.

Suggested Citation

  • Kang, Inhan & De Boeck, Paul & Partchev, Ivailo, 2022. "A randomness perspective on intelligence processes," Intelligence, Elsevier, vol. 91(C).
  • Handle: RePEc:eee:intell:v:91:y:2022:i:c:s0160289622000137
    DOI: 10.1016/j.intell.2022.101632
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    References listed on IDEAS

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    1. Inhan Kang & Paul Boeck & Roger Ratcliff, 2022. "Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 725-748, June.
    2. Dylan Molenaar & Paul Boeck, 2018. "Response Mixture Modeling: Accounting for Heterogeneity in Item Characteristics across Response Times," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 279-297, June.
    3. Molenaar, Dylan & Tuerlinckx, Francis & van der Maas, Han L. J., 2015. "Fitting Diffusion Item Response Theory Models for Responses and Response Times Using the R Package diffIRT," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i04).
    4. Shaw, Amy & Elizondo, Fabian & Wadlington, Patrick L., 2020. "Reasoning, fast and slow: How noncognitive factors may alter the ability-speed relationship," Intelligence, Elsevier, vol. 83(C).
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    6. Francis Tuerlinckx & Paul Boeck, 2005. "Two interpretations of the discrimination parameter," Psychometrika, Springer;The Psychometric Society, vol. 70(4), pages 629-650, December.
    7. Wim van der Linden, 2007. "A Hierarchical Framework for Modeling Speed and Accuracy on Test Items," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 287-308, September.
    8. Chen, Haiqin & De Boeck, Paul & Grady, Matthew & Yang, Chien-Lin & Waldschmidt, David, 2018. "Curvilinear dependency of response accuracy on response time in cognitive tests," Intelligence, Elsevier, vol. 69(C), pages 16-23.
    9. Maria Bolsinova & Paul Boeck & Jesper Tijmstra, 2017. "Modelling Conditional Dependence Between Response Time and Accuracy," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1126-1148, December.
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    1. Inhan Kang & Minjeong Jeon & Ivailo Partchev, 2023. "A Latent Space Diffusion Item Response Theory Model to Explore Conditional Dependence between Responses and Response Times," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 830-864, September.
    2. Inhan Kang & Dylan Molenaar & Roger Ratcliff, 2023. "A Modeling Framework to Examine Psychological Processes Underlying Ordinal Responses and Response Times of Psychometric Data," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 940-974, September.
    3. Sun-Joo Cho & Sarah Brown-Schmidt & Paul De Boeck & Matthew Naveiras & Si On Yoon & Aaron Benjamin, 2023. "Incorporating Functional Response Time Effects into a Signal Detection Theory Model," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 1056-1086, September.

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