Age differences in intuitive moral decision-making: Associations with inter-network neural connectivity.
Positions of power involving moral decision-making are often held by older adults (OAs). However, little is known about age differences in moral decision-making and the intrinsic organization of the aging brain. In this study, younger adults (YAs; n = 117, Mage = 22.11) and OAs (n = 82, Mage = 67.54) made decisions in hypothetical moral dilemmas and completed resting-state multi-echo functional magnetic resonance imaging (fMRI) scans. Relative to YAs, OAs were more likely to endorse deontological decisions (i.e., decisions based on adherence to a moral principle or duty), but only when the choice was immediately compelling or intuitive. By contrast, there was no difference between YAs and OAs in utilitarian decisions (i.e., decisions aimed at maximizing collective well-being) when the utilitarian choice was intuitive. Enhanced connections between the posterior medial core of the default network (pmDN) and the dorsal attention network, and overall reduced segregation of pmDN from the rest of the brain, were associated with this increased deontological-intuitive moral decision-making style in OAs. The present study contributes to our understanding of age differences in decision-making styles by taking into account the intuitiveness of the moral choice, and it offers further insights as to how age differences in intrinsic brain connectivity relate to these distinct moral decision-making styles in YAs and OAs. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Related Subject Headings
- Morals
- Magnetic Resonance Imaging
- Judgment
- Humans
- Experimental Psychology
- Decision Making
- Brain
- Aging
- Aged
- 5204 Cognitive and computational psychology
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Morals
- Magnetic Resonance Imaging
- Judgment
- Humans
- Experimental Psychology
- Decision Making
- Brain
- Aging
- Aged
- 5204 Cognitive and computational psychology