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.
| Original language | English (US) |
|---|---|
| Article number | 848 |
| Journal | Frontiers in Psychology |
| Volume | 10 |
| Issue number | APR |
| DOIs | |
| State | Published - Jan 1 2019 |
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Keywords
- Assessment
- Bayesian brain
- Computational modeling
- Computational neuroscience
- Reinforcement learning
- Trait emotional intelligence
ASJC Scopus subject areas
- Psychology(all)
Cite this
Parameters as trait indicators : Exploring a complementary neurocomputational approach to conceptualizing and measuring trait differences in emotional intelligence. / Smith, Ryan; Alkozei, Anna; Killgore, William.
In: Frontiers in Psychology, Vol. 10, No. APR, 848, 01.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Parameters as trait indicators
T2 - Exploring a complementary neurocomputational approach to conceptualizing and measuring trait differences in emotional intelligence
AU - Smith, Ryan
AU - Alkozei, Anna
AU - Killgore, William
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Assessment
KW - Bayesian brain
KW - Computational modeling
KW - Computational neuroscience
KW - Reinforcement learning
KW - Trait emotional intelligence
UR - http://www.scopus.com/inward/record.url?scp=85065140439&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065140439&partnerID=8YFLogxK
U2 - 10.3389/fpsyg.2019.00848
DO - 10.3389/fpsyg.2019.00848
M3 - Article
AN - SCOPUS:85065140439
VL - 10
JO - Frontiers in Psychology
JF - Frontiers in Psychology
SN - 1664-1078
IS - APR
M1 - 848
ER -