Max Planck Institute for Human Development, Center for Adaptive Behavior and Cognition, Lentzeallee 94, 14195 Berlin, Germany; University of Zürich, Department of Psychology, Binzmühlestrasse 14/22, 8050 Zürich, Switzerland.
Syracuse University, Department of Psychology, 509 Huntington Hall, Syracuse, NY 13244, United States; Max Planck Institute for Human Development, Center for Adaptive Behavior and Cognition, Lentzeallee 94, 14195 Berlin, Germany.
Cognition. 2018 Jan;170:102-122. doi: 10.1016/j.cognition.2017.09.003. Epub 2017 Oct 5.
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.
几种认知理论区分了不同的策略,这些策略在使用时所需的心理努力程度不同。但是,如何概念化和衡量与策略相关的努力或认知成本呢?我们提出了一种方法,将策略所需的努力分解为使用特定认知资源的需求所带来的时间成本。我们将这种方法称为资源需求分解分析(RDDA),并将其实例化到自适应思维控制认知架构(ACT-R)中。ACT-R 为策略的计算机模拟提供了手段。这些模拟考虑了策略如何与认知资源的定量实现相互作用,并纳入了并行处理的可能性。使用这种方法,我们对两种著名的决策策略——最优选择和计数,进行了量化、分解和比较。因为最优选择经常忽略信息并放弃信息整合,所以它被认为比计数等策略更简单。然而,在 ACT-R 模拟和一项实证研究中,我们发现,随着认知需求的增加,最优选择的反应时间(即时间成本)有时超过计数的反应时间。RDDA 表明,与计数相比,使用最优选择时,工作记忆更新、记忆检索和心理动作协调的需求更大,这导致了这种模式。结果表明,评估策略的相对简单性需要考虑策略所嵌入的整体认知系统。