Cognitive and Affective Theory of Mind in Healthy Aging.
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| Title: | Cognitive and Affective Theory of Mind in Healthy Aging. |
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| Authors: | Ruitenberg, Marit F. L., Santens, Patrick, Notebaert, Wim |
| Source: | Experimental Aging Research. Oct2020, Vol. 46 Issue 5, p382-395. 14p. 2 Charts, 3 Graphs. |
| Subjects: | Affect (Psychology), Age distribution, Cognitive testing, Neuropsychological tests, Reaction time, Thought & thinking, Descriptive statistics, Active aging |
| Abstract: | Previous studies on the effect of healthy aging on Theory of Mind (ToM) have produced mixed results. A possible explanation may be that different ToM components and types of inference have not systematically been considered. This study examined the effect of aging on ToM by assessing both first and second order cognitive and affective components within a single task. We compared performance of young (M = 18.3y) and older adults (M = 61.0y) on the Yoni task. This task allows for a within-subject assessment of both first and second order cognitive and affective ToM. We observed that older adults had longer reaction times than young adults across cognitive and affective first order items. For second order items, this age difference was larger for affective than cognitive items. Results showed no indications that these findings could be explained by age differences in speed/accuracy trade-offs. Our findings suggest that decision processes underlying ToM are slower in older adults on both first and second order inferences, but that age differences in these processes between cognitive and affective ToM are selective to second order inferences. We propose that the observed age differences may be associated with cortical and mental changes that occur with aging. [ABSTRACT FROM AUTHOR] |
| Copyright of Experimental Aging Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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