Literature DB >> 35349582

Do your eye movements reveal your performance on an IQ test? A study linking eye movements and socio-demographic information to fluid intelligence.

Enkelejda Kasneci1, Gjergji Kasneci2, Ulrich Trautwein3, Tobias Appel3, Maike Tibus3, Susanne M Jaeggi4, Peter Gerjets5.   

Abstract

Understanding the main factors contributing to individual differences in fluid intelligence is one of the main challenges of psychology. A vast body of research has evolved from the theoretical framework put forward by Cattell, who developed the Culture-Fair IQ Test (CFT 20-R) to assess fluid intelligence. In this work, we extend and complement the current state of research by analysing the differential and combined relationship between eye-movement patterns and socio-demographic information and the ability of a participant to correctly solve a CFT item. Our work shows that a participant's eye movements while solving a CFT item contain discriminative information and can be used to predict whether the participant will succeed in solving the test item. Moreover, the information related to eye movements complements the information provided by socio-demographic data when it comes to success prediction. In combination, both types of information yield a significantly higher predictive performance than each information type individually. To better understand the contributions of features related to eye movements and socio-demographic information to predict a participant's success in solving a CFT item, we employ state-of-the-art explainability techniques and show that, along with socio-demographic variables, eye-movement data. Especially the number of saccades and the mean pupil diameter, significantly increase the discriminating power. The eye-movement features are likely indicative of processing efficiency and invested mental effort. Beyond the specific contribution to research on how eye movements can serve as a means to uncover mechanisms underlying cognitive processes, the findings presented in this work pave the way for further in-depth investigations of factors predicting individual differences in fluid intelligence.

Entities:  

Mesh:

Year:  2022        PMID: 35349582      PMCID: PMC8963570          DOI: 10.1371/journal.pone.0264316

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  39 in total

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Authors:  Daniel A Hackman; Martha J Farah; Michael J Meaney
Journal:  Nat Rev Neurosci       Date:  2010-09       Impact factor: 34.870

2.  The effect of items in working memory on the deployment of attention and the eyes during visual search.

Authors:  R Houtkamp; P R Roelfsema
Journal:  J Exp Psychol Hum Percept Perform       Date:  2006-04       Impact factor: 3.332

3.  Microsaccadic responses in a bimodal oddball task.

Authors:  Matteo Valsecchi; Massimo Turatto
Journal:  Psychol Res       Date:  2008-03-05

4.  Separating cognitive capacity from knowledge: a new hypothesis.

Authors:  Graeme S Halford; Nelson Cowan; Glenda Andrews
Journal:  Trends Cogn Sci       Date:  2007-05-01       Impact factor: 20.229

5.  Critique of Cattell's "theory of fluid and crystallized intelligence: a critical experiment".

Authors:  L G Humphreys
Journal:  J Educ Psychol       Date:  1967-06

6.  Task difficulty in mental arithmetic affects microsaccadic rates and magnitudes.

Authors:  Eva Siegenthaler; Francisco M Costela; Michael B McCamy; Leandro L Di Stasi; Jorge Otero-Millan; Andreas Sonderegger; Rudolf Groner; Stephen Macknik; Susana Martinez-Conde
Journal:  Eur J Neurosci       Date:  2013-11-07       Impact factor: 3.386

7.  SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies.

Authors:  Thomas C Kübler; Colleen Rothe; Ulrich Schiefer; Wolfgang Rosenstiel; Enkelejda Kasneci
Journal:  Behav Res Methods       Date:  2017-06

8.  Pupillary responses during information processing vary with Scholastic Aptitude Test scores.

Authors:  S Ahern; J Beatty
Journal:  Science       Date:  1979-09-21       Impact factor: 47.728

9.  Executive working memory load does not compromise perceptual processing during visual search: evidence from additive factors analysis.

Authors:  Jibo He; Jason S McCarley
Journal:  Atten Percept Psychophys       Date:  2010-02       Impact factor: 2.199

10.  TüEyeQ, a rich IQ test performance data set with eye movement, educational and socio-demographic information.

Authors:  Enkelejda Kasneci; Gjergji Kasneci; Tobias Appel; Johannes Haug; Franz Wortha; Maike Tibus; Ulrich Trautwein; Peter Gerjets
Journal:  Sci Data       Date:  2021-06-16       Impact factor: 6.444

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  1 in total

1.  Evidence in Support of Analogical Reasoning Improvements with Executive Attention Intervention in Healthy Young Adults.

Authors:  Yixuan Lin; Qing Li; Mengke Zhang; Yujie Su; Xiangpeng Wang; Hong Li; Antao Chen
Journal:  Neurosci Bull       Date:  2022-08-19       Impact factor: 5.271

  1 in total

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