How does aging impact decision making? The contribution of cognitive decline and strategic compensation revealed in a cognitive architecture.

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. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Cognitive costs of decision-making strategies: A resource demand decomposition analysis with a cognitive architecture.

Several theories of cognition distinguish between strategies that differ in the mental effort that their use requires. But how can the effort-or cognitive costs-associated with a strategy be conceptualized and measured? We propose an approach that decomposes the effort a strategy requires into the time costs associated with the demands for using specific cognitive resources. We refer to this approach as resource demand decomposition analysis (RDDA) and instantiate it in the cognitive architecture Adaptive Control of Thought-Rational (ACT-R). ACT-R provides the means to develop computer simulations of the strategies. These simulations take into account how strategies interact with quantitative implementations of cognitive resources and incorporate the possibility of parallel processing. Using this approach, we quantified, decomposed, and compared the time costs of two prominent strategies for decision making, take-the-best and tallying. Because take-the-best often ignores information and foregoes information integration, it has been considered simpler than strategies like tallying. However, in both ACT-R simulations and an empirical study we found that under increasing cognitive demands the response times (i.e., time costs) of take-the-best sometimes exceeded those of tallying. The RDDA suggested that this pattern is driven by greater requirements for working memory updates, memory retrievals, and the coordination of mental actions when using take-the-best compared to tallying. The results illustrate that assessing the relative simplicity of strategies requires consideration of the overall cognitive system in which the strategies are embedded.

Strategies for memory-based decision making: Modeling behavioral and neural signatures within a cognitive architecture.

How do people use memories to make inferences about real-world objects? We tested three strategies based on predicted patterns of response times and blood-oxygen-level-dependent (BOLD) responses: one strategy that relies solely on recognition memory, a second that retrieves additional knowledge, and a third, lexicographic (i.e., sequential) strategy, that considers knowledge conditionally on the evidence obtained from recognition memory. We implemented the strategies as computational models within the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, which allowed us to derive behavioral and neural predictions that we then compared to the results of a functional magnetic resonance imaging (fMRI) study in which participants inferred which of two cities is larger. Overall, versions of the lexicographic strategy, according to which knowledge about many but not all alternatives is searched, provided the best account of the joint patterns of response times and BOLD responses. These results provide insights into the interplay between recognition and additional knowledge in memory, hinting at an adaptive use of these two sources of information in decision making. The results highlight the usefulness of implementing models of decision making within a cognitive architecture to derive predictions on the behavioral and neural level.