@article{a1c14bb580e94d7f8c6437a0c063c287,
title = "How does aging impact decision making? The contribution of cognitive decline and strategic compensation revealed in a cognitive architecture",
abstract = "Older adults often face decline in cognitive resources. How does this impact their decision making-especially under high cognitive demands from concurrent activities? Do older adults' decision processes uniformly decline with increasing mental strain relative to younger adults, or do they compensate for decline by strategically reallocating resources? Using empirical data and computational modeling, we investigated older and younger adults' execution of two decision strategies in a multiattribute judgment task, while varying the demands from a concurrent task. One strategy (take-the-best) involves searching attributes in order of importance until one attribute favors one alternative; the other strategy (tallying) requires the integration of attributes favoring each alternative. Although older adults executed both strategies quite accurately, they performed worse and more slowly than younger adults. Moreover, when the concurrent demands increased, both age groups executed the strategies less accurately and more slowly. Crucially, when take-the-best required searching an increasing number of attributes, participants' accuracy and speed initially decreased with increasing search requirements, but accuracy recovered and the slowing lessened at the highest search requirements; this pattern was particularly prominent in older adults and most pronounced under the highest concurrent demands. Simulations with models in the cognitive architecture ACT-R showed how decline in specific cognitive resources can contribute to older adults' decrements in strategy execution. However, accommodating older adults' preserved strategy execution of take-the-best under the highest demands required assuming compensatory shifts in resource allocation. Thus, cognitive decline and strategic compensation applied under highest demands provided complementary accounts for older adults' decision behavior.",
keywords = "ACT-R, Aging, Compensation, Computational modeling, Decision making",
author = "Fechner, {Hanna B.} and Thorsten Pachur and Schooler, {Lael J.}",
note = "Funding Information: This work was supported by the Max Planck International Research Network on Aging (MaxNetAging), the Center for Adaptive Behavior and Cognition (ABC), and the Center for Adaptive Rationality (ARC) at the Max Planck Institute for Human Development, the University Research Priority Program Dynamics of Healthy Aging (URPP DynAge) at the University of Z{\"u}rich, and the Department of Psychology at Syracuse University. We thank Gregor Caregnato, Julia Cleeman, Flora Klie, Marianne Ritthausen, and Swantje Wenzel for the data collection, Rebecca M{\"u}ller and J{\"u}rgen Rossbach for help with the design of the stimulus materials of the empirical study, Laura Scholaske for support in the literature research and data preprocessing, and Dan Bothell from the research group of John R. Anderson at Carnegie Mellon University, the members of the MaxNetAging Research School at the Max Planck Institute for Demographic Research, Rostock, Germany, and Klaus Oberauer and Peter Shepherdson at the University of Z{\"u}rich for helpful comments on this project and article. We thank Anita Todd for editing the article. Funding Information: This work was supported by the Max Planck International Research Network on Aging (MaxNetAging), the Center for Adaptive Behavior and Cognition (ABC), and the Center for Adaptive Rationality (ARC) at the Max Planck Institute for Human Development, the University Research Priority Program Dynamics of Healthy Aging (URPP DynAge) at the University of Z?rich, and the Department of Psychology at Syracuse University. We thank Gregor Caregnato, Julia Cleeman, Flora Klie, Marianne Ritthausen, and Swantje Wenzel for the data collection, Rebecca M?ller and J?rgen Rossbach for help with the design of the stimulus materials of the empirical study, Laura Scholaske for support in the literature research and data preprocessing, and Dan Bothell from the research group of John R. Anderson at Carnegie Mellon University, the members of the MaxNetAging Research School at the Max Planck Institute for Demographic Research, Rostock, Germany, and Klaus Oberauer and Peter Shepherdson at the University of Z?rich for helpful comments on this project and article. We thank Anita Todd for editing the article. Publisher Copyright: {\textcopyright} 2019 American Psychological Association.",
year = "2019",
month = sep,
doi = "10.1037/xlm0000661",
language = "English (US)",
volume = "45",
pages = "1634--1663",
journal = "Journal of Experimental Psychology: Learning Memory and Cognition",
issn = "0278-7393",
publisher = "American Psychological Association Inc.",
number = "9",
}