The Role of Emotional vs. Cognitive Intelligence in Economic Decision-Making Amongst Older Adults

Abstract

The links between emotions, bio-regulatory processes, and economic decision-making are well-established in the context of age-related changes in fluid, real-time, decision competency. The objective of the research reported here is to assess the relative contributions, interactions, and impacts of affective and cognitive intelligence in economic, value-based decision-making amongst older adults. Additionally, we explored this decision-making competency in the context of the neurobiology of aging by examining the neuroanatomical correlates of intelligence and decision-making in an aging cohort. Thirty-nine, healthy, community dwelling older adults were administered the Iowa Gambling Task (IGT), an ecologically valid laboratory measure of complex, economic decision-making; along with standardized, performance-based measures of cognitive and emotional intelligence (EI). A smaller subset of this group underwent structural brain scans from which thicknesses of the frontal, parietal, temporal, occipital, cingulate cortices and their sub-sections, were computed. Fluid (online processing) aspects of Perceptual Reasoning cognitive intelligence predicted superior choices on the IGT. However, older adults with higher overall emotional intelligence (EI) and higher Experiential EI area/sub-scores learned faster to make better choices on the IGT, even after controlling for cognitive intelligence and its area scores. Thickness of the left rostral anterior cingulate (associated with fluid affective, processing) mediated the relationship between age and Experiential EI. Thickness of the right transverse temporal gyrus moderated the rate of learning on the IGT. In conclusion, our data suggest that fluid processing, which involves "online," bottom-up, cognitive processing, predicts value-based decision-making amongst older adults, while crystallized intelligence, which relies on "offline" previously acquired knowledge, does not. However, only emotional intelligence, especially its fluid "online" aspects of affective processing predicts the rate of learning in situations of complex choice, especially when there is a paucity of cues/information available to guide decision-making. Age-related effects on these cognitive, affective and decision mechanisms may have neuroanatomical correlates, especially in regions that form a subset of the human mirror-neuron and mentalizing systems. While superior decision-making may be stereotypically associated with "smarter people" (i.e., higher cognitive intelligence), our data indicate that emotional intelligence has a significant role to play in the economic decisions of older adults.

Keywords: aging decision competency; cognitive reserve; emotional intelligence; fluid intelligence; neuroeconomic decision-making; structural imaging biomarkers.

Figure 1

Role of emotional intelligence in rate of learning on the Iowa Gambling Task: The y axes represent advantageous decision-making on the Iowa Gambling Task (IGT), calculated as the number of picks from the bad decks subtracted from the number of picks from the good decks. The x axes represent the progression of the task across trials in 5 blocks (of 20 trials each) from the start to the end of the task. A median split separates high (red) and low (blue) scores on index score of emotional intelligence (EI), and its area sub-score Experiential Emotional Intelligence (EXP).

Figure 2

Visual representation (on the Freesurfer software template) of the average cortical thicknesses of the significant Regions of Interest (ROIs). These are predictive of emotional intelligence index score (EI), its area sub-scores Experiential Emotional Intelligence (EXP), Strategic Emotional Intelligence (REA); Full scale cognitive intelligence index score (FSIQ), its sub-score Performance Intelligence Quotient (PIQ), and the IOWA Gambling Task score. N = 27.

Figure 3

Visual representation of causal mediation of the Rostral anterior cingulate fasciculus gyrus (LRACFG). The Y axis represents the Average causal mediated effect (ACME); the Average direct effect (ADE- residual effect after mediation) of age on experiential emotional intelligence (EXP); and the total effect of the ACME and ADE. The X-axis represents the regression weights (effect estimates).

Figure 4

Path diagram of the causal mediation of the Rostral anterior cingulate fasciculus gyrus (LRACFG). Given the significant negative correlation between age and EXP (Table 1), and the negative direction of the regression estimates (Figures 3, 4) between LRACFG and EXP, it appears that those amongst the older aging adults with thinner LRACFG, tend to have better preserved EXP.

Figure 5

Neural substrate that moderates decision-making performance in aging adults. The X axis represents Iowa Gambling Task (IGT) task performance in 5 blocks (1–5) of 20 trials each, from start to finish. Y axis represents IGT score. The cortical thickness of the neural substrate, right transverse tegmental gyrus (RTTFG), is median split into thick (blue) and thin (red). Control Variable: Sex.