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Moderators of learning and performance trajectories in microworld simulations: Too soon to give up on intellect!?

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  • Birney, Damian P.
  • Beckmann, Jens F.
  • Beckmann, Nadin
  • Double, Kit S.
  • Whittingham, Karen

Abstract

The burgeoning increase in the importance given to non-cognitive factors in complex decisions making, has led to calls to question intelligence as the primary explanatory model of success. Features of a business microworld simulation were experimentally manipulated to investigate the incremental value of 20 cognitive and non-cognitive predictors of learning and performance trajectories. Using a combined experimental-differential paradigm and mixed-level modelling, it was predicted that of these, facilitating personality traits (e.g., openness and extraversion), growth/motivational mindsets (e.g., learning goals, need for cognition, and beliefs of malleability), and tentatively, emotion-regulation (e.g., managing and facilitating emotions) would moderate the impact of microworld complexity and experience on performance. Results from 142 experienced business managers replicate the pervasive importance of general and domain-specific reasoning. Contrary to expectations, of the 16 non-cognitive factors investigated, only three mindset variables showed incremental value, and only performance-goal orientations moderated effects above reasoning. These findings give prima facie reason to question the purported importance of conative factors, over and above intellect. However, rather than discount non-cognitive factors entirely, our analyses suggest that with refinement, microworlds and mixed-level modelling may well-support the experimental methods needed to understand moderators of real-world problem solving.

Suggested Citation

  • Birney, Damian P. & Beckmann, Jens F. & Beckmann, Nadin & Double, Kit S. & Whittingham, Karen, 2018. "Moderators of learning and performance trajectories in microworld simulations: Too soon to give up on intellect!?," Intelligence, Elsevier, vol. 68(C), pages 128-140.
    • Handle: RePEc:eee:intell:v:68:y:2018:i:c:p:128-140
    DOI: 10.1016/j.intell.2018.03.008
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    References listed on IDEAS

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    1. Scherbaum, Charles A. & Goldstein, Harold W. & Yusko, Kenneth P. & Ryan, Rachel & Hanges, Paul J., 2012. "Intelligence 2.0: Reestablishing a Research Program on g in I–O Psychology," Industrial and Organizational Psychology, Cambridge University Press, vol. 5(2), pages 128-148, June.
    2. Manfred Grotenhuis & Ben Pelzer & Rob Eisinga & Rense Nieuwenhuis & Alexander Schmidt-Catran & Ruben Konig, 2017. "When size matters: advantages of weighted effect coding in observational studies," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 62(1), pages 163-167, January.
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    Cited by:

    1. Herrmann, W. & Beckmann, J.F. & Kretzschmar, A., 2023. "The role of learning in complex problem solving using MicroDYN," Intelligence, Elsevier, vol. 100(C).

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