Individual differences in information demand have a low dimensional structure predicted by some curiosity traits

Hayley K. Jach, Roshan Cools, Alex Frisvold, Michael A. Grubb, Catherine A. Hartley, Jochen Hartmann, Laura Hunter, Ruonan Jia, Floris P. de Lange, Ruby Larisch, Rosa Lavelle-Hill, Ifat Levyi, Yutong Li, Lieke L.F. van Lieshout, Kate Nussenbaum, Silvio Ravaioli, Siyu Wang, Robert Wilson, Michael Woodford, Kou MurayamaJacqueline Gottlie

Research output: Contribution to journalArticlepeer-review

Abstract

To understand human learning and progress, it is crucial to understand curiosity. But how consistent is curiosity’s conception and assessment across scientific research disciplines? We present the results of a large collaborative project assessing the correspondence between curiosity measures in personality psychology and cognitive science. A total of 820 participants completed 15 personality trait measures and 9 cognitive tasks that tested multiple aspects of information demand. We show that shared variance across the cognitive tasks was captured by a dimension reflecting directed (uncertainty-driven) versus random (stochasticity-driven) exploration and individual differences along this axis were significantly and consistently predicted by personality traits. However, the personality metrics that best predicted information demand were not the central curiosity traits of openness to experience, deprivation sensitivity, and joyous exploration, but instead included more peripheral curiosity traits (need for cognition, thrill seeking, and stress tolerance) and measures not traditionally associated with curiosity (extraversion and behavioral inhibition). The results suggest that the umbrella term “curiosity” reflects a constellation of cognitive and emotional processes, only some of which are shared between personality measures and cognitive tasks. The results reflect the distinct methods that are used in these fields, indicating a need for caution in comparing results across fields and for future interdisciplinary collaborations to strengthen our emerging understanding of curiosity.

Original languageEnglish (US)
Article numbere2415236121
JournalProceedings of the National Academy of Sciences of the United States of America
Volume121
Issue number45
DOIs
StatePublished - Nov 5 2024
Externally publishedYes

Keywords

  • curiosity
  • individual differences
  • information seeking
  • machine learning
  • personality traits

ASJC Scopus subject areas

  • General

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