Preregistration

Cognitive Abilities in the Wild: Predicting Fluid Intelligence from Digital Footprints of Everyday Smartphone Usage

Author(s) / Creator(s)

Bergmann, Maximilian
Schoedel, Ramona
Stachl, Clemens

Abstract / Description

Individual differences in cognitive abilities are known to predict various important life outcomes, making their study a critical area of interest for practitioners and researchers alike. While most research studied cognitive abilities within laboratory or achievement contexts, different lines of research investigated their role in everyday life, repeatedly linking them to our everyday behavior. However, as prior work mainly relied on reported behavior or simulated tasks, the relationship between cognitive abilities and objective behavior in everyday life remains unclear. The recent adaption of smartphone sensing and computational methods in psychology has demonstrated the potential of studying individual differences in real- world settings. In this fashion, the present study leverages digital footprints from everyday smartphone usage to investigate how fluid intelligence, one of the most central cognitive abilities within the Cattell-Horn-Carroll Theory (CHC; McGrew, 2009), is related to objective behavior in everyday life. More specifically, by means of a machine learning approach, we investigate (1) to what extent behavioral patterns in everyday smartphone usage predict fluid intelligence and (2) which behavioral patterns are most important for these predictions. For this purpose, we drew on existing literature to derive a comprehensive overview of behavioral correlates of fluid intelligence in everyday life capturable via logs of everyday smartphone usage. Translating these findings into features of multimodal smartphone usage data (e.g., phone usage duration, app installations, music consumption, typing patterns), we created a list of sensing features that correspond to the theory-based behavioral correlates and are described in this preregistration protocol. Using cross-validation, we will train linear and non-linear machine learning models (e.g., Elastic Net, Random Forest) based on these features and determine their predictiveness for participants’ composite scores of a fluid intelligence test. By means of interpretable machine learning techniques, we will examine which single features and feature groups contribute most to the predictive performance of these models.

Keyword(s)

Intelligence Smartphone Usage Machine Learning

Persistent Identifier

PsychArchives acquisition timestamp

2023-11-11 13:00:09 UTC

Publisher

PsychArchives

Citation

  • Author(s) / Creator(s)
    Bergmann, Maximilian
  • Author(s) / Creator(s)
    Schoedel, Ramona
  • Author(s) / Creator(s)
    Stachl, Clemens
  • PsychArchives acquisition timestamp
    2023-11-11T13:00:09Z
  • Made available on
    2023-11-11T13:00:09Z
  • Date of first publication
    2023-11-11
  • Abstract / Description
    Individual differences in cognitive abilities are known to predict various important life outcomes, making their study a critical area of interest for practitioners and researchers alike. While most research studied cognitive abilities within laboratory or achievement contexts, different lines of research investigated their role in everyday life, repeatedly linking them to our everyday behavior. However, as prior work mainly relied on reported behavior or simulated tasks, the relationship between cognitive abilities and objective behavior in everyday life remains unclear. The recent adaption of smartphone sensing and computational methods in psychology has demonstrated the potential of studying individual differences in real- world settings. In this fashion, the present study leverages digital footprints from everyday smartphone usage to investigate how fluid intelligence, one of the most central cognitive abilities within the Cattell-Horn-Carroll Theory (CHC; McGrew, 2009), is related to objective behavior in everyday life. More specifically, by means of a machine learning approach, we investigate (1) to what extent behavioral patterns in everyday smartphone usage predict fluid intelligence and (2) which behavioral patterns are most important for these predictions. For this purpose, we drew on existing literature to derive a comprehensive overview of behavioral correlates of fluid intelligence in everyday life capturable via logs of everyday smartphone usage. Translating these findings into features of multimodal smartphone usage data (e.g., phone usage duration, app installations, music consumption, typing patterns), we created a list of sensing features that correspond to the theory-based behavioral correlates and are described in this preregistration protocol. Using cross-validation, we will train linear and non-linear machine learning models (e.g., Elastic Net, Random Forest) based on these features and determine their predictiveness for participants’ composite scores of a fluid intelligence test. By means of interpretable machine learning techniques, we will examine which single features and feature groups contribute most to the predictive performance of these models.
    en
  • Publication status
    other
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  • Review status
    unknown
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  • Persistent Identifier
    https://hdl.handle.net/20.500.12034/9052
  • Persistent Identifier
    https://doi.org/10.23668/psycharchives.13571
  • Language of content
    eng
    en
  • Publisher
    PsychArchives
    en
  • Is related to
    https://doi.org/10.23668/psycharchives.2901
  • Keyword(s)
    Intelligence
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  • Keyword(s)
    Smartphone Usage
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  • Keyword(s)
    Machine Learning
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  • Dewey Decimal Classification number(s)
    150
  • Title
    Cognitive Abilities in the Wild: Predicting Fluid Intelligence from Digital Footprints of Everyday Smartphone Usage
    en
  • DRO type
    preregistration
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  • Visible tag(s)
    Smartphone Sensing Panel Study
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