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    Parameters as Trait Indicators: Exploring a Complementary Neurocomputational Approach to Conceptualizing and Measuring Trait Differences in Emotional Intelligence

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    Author
    Smith, Ryan
    Alkozei, Anna cc
    Killgore, William D. S.
    Affiliation
    Univ Arizona, Dept Psychiat
    Issue Date
    2019-04-18
    Keywords
    trait emotional intelligence
    computational neuroscience
    reinforcement learning
    Bayesian brain
    computational modeling
    assessment
    
    Metadata
    Show full item record
    Publisher
    FRONTIERS MEDIA SA
    Citation
    Smith R, Alkozei A and Killgore WDS (2019) Parameters as Trait Indicators: Exploring a Complementary Neurocomputational Approach to Conceptualizing and Measuring Trait Differences in Emotional Intelligence. Front. Psychol. 10:848. doi: 10.3389/fpsyg.2019.00848
    Journal
    FRONTIERS IN PSYCHOLOGY
    Rights
    © 2019 Smith, Alkozei and Killgore. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Current assessments of trait emotional intelligence (EI) rely on self-report inventories. While this approach has seen considerable success, a complementary approach allowing objective assessment of EI-relevant traits would provide some potential advantages. Among others, one potential advantage is that it would aid in emerging efforts to assess the brain basis of trait EI, where self-reported competency levels do not always match real-world behavior. In this paper, we review recent experimental paradigms in computational cognitive neuroscience (CCN), which allow behavioral estimates of individual differences in range of parameter values within computational models of neurocognitive processes. Based on this review, we illustrate how several of these parameters appear to correspond well to EI-relevant traits (i.e., differences in mood stability, stress vulnerability, self-control, and flexibility, among others). In contrast, although estimated objectively, these parameters do not correspond well to the optimal performance abilities assessed within competing "ability models" of EI. We suggest that adapting this approach from CCN-by treating parameter value estimates as objective trait EI measures-could (1) provide novel research directions, (2) aid in characterizing the neural basis of trait EI, and (3) offer a promising complementary assessment method.
    Note
    Open access journal.
    ISSN
    1664-1078
    DOI
    10.3389/fpsyg.2019.00848
    Version
    Final published version
    ae974a485f413a2113503eed53cd6c53
    10.3389/fpsyg.2019.00848
    Scopus Count
    Collections
    UA Faculty Publications

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