August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
A solution to the ill-posed problem of common factors in vision
Author Affiliations & Notes
  • Dario Gordillo
    Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
  • Aline Cretenoud
    Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
  • Simona Garobbio
    Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
  • Michael H. Herzog
    Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
  • Footnotes
    Acknowledgements  This work was funded by the National Centre of Competence in Research (NCCR) Synapsy financed by the Swiss National Science Foundation under grant 51NF40-185897.
Journal of Vision August 2023, Vol.23, 5081. doi:https://doi.org/10.1167/jov.23.9.5081
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      Dario Gordillo, Aline Cretenoud, Simona Garobbio, Michael H. Herzog; A solution to the ill-posed problem of common factors in vision. Journal of Vision 2023;23(9):5081. https://doi.org/10.1167/jov.23.9.5081.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Studies investigating individual differences in vision tend to deliver mixed results. Some studies argue for a common factor underlying visual abilities, i.e., a participant performing better in one visual task, compared to another participant, is also assumed to perform better in another visual task. Other studies propose that visual abilities are better explained by several uncorrelated factors, i.e., the performance in one visual task does not necessarily predict performance in another visual task. In the above studies, the data are analyzed with principal component analysis (PCA) or factor analysis (FA). Conclusions are often made based on measures such as the proportion of variance explained by the first component/factor of a PCA/FA. Here, using computer simulations, we demonstrate that we cannot draw conclusions about common factors based on measures such as the proportion of variance explained by the first component/factor of a PCA/FA. Further, we show that the number of participants and variables strongly influence the results of PCA and FA. Finally, we propose a new tool that tests for common factors. We applied our tool to data from 13 previous studies investigating common factors in vision.

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