Performing deferred actions in the future relies upon Prospective Memory (PM). Often, PM demands arise in complex dynamic tasks. Not only can PM be challenging in such environments, the processes required for PM may affect the performance of other tasks. To adapt to PM demands in such environments, humans may use a range of strategies, including flexible allocation of cognitive resources and cognitive control mechanisms. We sought to understand such mechanisms by using the Prospective Memory Decision Control (Strickland et al., 2018) model to provide a comprehensive, quantitative account of dual task performance in a complex dynamic environment (a simulated air traffic control conflict detection task). We found that PM demands encouraged proactive control over ongoing task decisions, but that this control was reduced at high time pressure to facilitate fast responding. We found reactive inhibitory control over ongoing task processes when PM targets were encountered, and that time pressure and PM demand both affect the attentional system, increasing the amount of cognitive resources available. However, as demands exceeded the capacity limit of the cognitive system, resources were reallocated (shared) between the ongoing and PM tasks. As the ongoing task used more resources to compensate for additional time pressure demands, it drained resources that would have otherwise been available for PM task processing. This study provides the first detailed quantitative understanding of how attentional resources and cognitive control mechanisms support PM and ongoing task performance in complex dynamic environments.

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.

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Learning and decision making are interactive processes, yet cognitive modelling of error-driven learning and decision making have largely evolved separately. Recently, evidence accumulation models (EAMs) of decision making and reinforcement learning (RL) models of error-driven learning have been combined into joint RL-EAMs that can in principle address these interactions. However, we show that the most commonly used combination, based on the diffusion decision model (DDM) for binary choice, consistently fails to capture crucial aspects of response times observed during reinforcement learning. We propose a new RL-EAM based on an advantage racing diffusion (ARD) framework for choices among two or more options that not only addresses this problem but captures stimulus difficulty, speed-accuracy trade-off, and stimulus-response-mapping reversal effects. The RL-ARD avoids fundamental limitations imposed by the DDM on addressing effects of absolute values of choices, as well as extensions beyond binary choice, and provides a computationally tractable basis for wider applications.

As people learn a new skill, performance changes along two fundamental dimensions: Responses become progressively faster and more accurate. In cognitive psychology, these facets of improvement have typically been addressed by separate classes of theories. Reductions in response time (RT) have usually been addressed by theories of skill acquisition, whereas increases in accuracy have been explained by associative learning theories. To date, relatively little work has examined how changes in RT relate to changes in response accuracy, and whether these changes can be accounted for quantitatively within a single theoretical framework. The current work examines joint changes in accuracy and RT in a probabilistic category learning task. We report a model-based analysis of changes in the shapes of RT distributions for different category responses at the level of individual stimuli over the course of learning. We show that changes in performance are determined solely by changes in the quality of information entering the decision process. We then develop a new model that combines an associative learning front end with a sequential sampling model of the decision process, showing that the model provides a good account of all aspects of the learning data. We conclude by discussing potential extensions of the model and future directions for theoretical development that are opened up by our findings.