Strategic attention and decision control support prospective memory in a complex dual-task environment
Strategic attention and decision control support prospective memory in a complex dual-task environment
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DOI：
10.1016/j.cognition.2019.05.011
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发表时间：
2019-10-01
期刊：
影响因子：
3.4
通讯作者：
Heathcote, Andrew
中科院分区：
文献类型：
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作者：
Boag, Russell J.;Strickland, Luke;Heathcote, Andrew
人类在复杂多任务环境中的表现关键取决于认知控制和认知能力之间的相互作用。在本文中，我们提出了一个易于处理的计算模型，认知控制和能力如何影响的速度和准确性的决定，在基于事件的前瞻性记忆（PM）范式，并在这样做测试一个新的定量配方，测量两个不同的组成部分的认知能力（增益和重点），一般适用于两个或两个以上的选项之间的选择。与以前的工作相一致，个人使用主动控制（增加正在进行的任务阈值下PM负载）和被动控制（抑制正在进行的任务积累率PM项目），以支持PM性能。在时间压力和PM负荷下，个体使用认知增益来增加分配给正在进行的任务的资源量。然而，当需求超过容量限制时，资源在正在进行的任务和PM过程之间重新分配（共享）。扩展以前的工作，个人使用认知焦点来控制基于环境的特定需求和回报结构的正在进行的和PM任务的处理质量（例如，更高优先级的任务更高的关注度;在高时间压力和PM负载下较低的关注度）。我们的模型提供了第一个详细的定量理解认知增益和重点，因为它们适用于证据积累模型，其中-沿着认知控制机制-支持决策在复杂的多任务环境。
Human performance in complex multiple-task environments depends critically on the interplay between cognitive control and cognitive capacity. In this paper we propose a tractable computational model of how cognitive control and capacity influence the speed and accuracy of decisions made in the event-based prospective memory (PM) paradigm, and in doing so test a new quantitative formulation that measures two distinct components of cognitive capacity (gain and focus) that apply generally to choices among two or more options. Consistent with prior work, individuals used proactive control (increased ongoing task thresholds under PM load) and reactive control (inhibited ongoing task accumulation rates to PM items) to support PM performance. Individuals used cognitive gain to increase the amount of resources allocated to the ongoing task under time pressure and PM load. However, when demands exceeded the capacity limit, resources were reallocated (shared) between ongoing task and PM processes. Extending previous work, individuals used cognitive focus to control the quality of processing for the ongoing and PM tasks based on the particular demand and payoff structure of the environment (e.g., higher focus for higher priority tasks; lower focus under high time pressure and with PM load). Our model provides the first detailed quantitative understanding of cognitive gain and focus as they apply to evidence accumulation models, which - along with cognitive control mechanisms - support decision-making in complex multiple-task environments.