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OBJECTIVE: The objective was to better understand how people adapt multitasking behavior when circumstances in driving change and how safe versus unsafe behaviors emerge. BACKGROUND: Multitasking strategies in driving adapt to changes in the task environment, but the cognitive mechanisms of this adaptation are not well known. Missing is a unifying account to explain the joint contribution of task constraints, goals, cognitive capabilities, and beliefs about the driving environment. METHOD: We model the driver's decision to deploy visual attention as a stochastic sequential decision-making problem and propose hierarchical reinforcement learning as a computationally tractable solution to it. The supervisory level deploys attention based on per-task value estimates, which incorporate beliefs about risk. Model simulations are compared against human data collected in a driving simulator. RESULTS: Human data show adaptation to the attentional demands of ongoing tasks, as measured in lane deviation and in-car gaze deployment. The predictions of our model fit the human data on these metrics. CONCLUSION: Multitasking strategies can be understood as optimal adaptation under uncertainty, wherein the driver adapts to cognitive constraints and the task environment's uncertainties, aiming to maximize the expected long-term utility. Safe and unsafe behaviors emerge as the driver has to arbitrate between conflicting goals and manage uncertainty about them. APPLICATION: Simulations can inform studies of conditions that are likely to give rise to unsafe driving behavior.

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We test people's ability to optimize performance across two concurrent tasks. Participants performed a number entry task while controlling a randomly moving cursor with a joystick. Participants received explicit feedback on their performance on these tasks in the form of a single combined score. This payoff function was varied between conditions to change the value of one task relative to the other. We found that participants adapted their strategy for interleaving the two tasks, by varying how long they spent on one task before switching to the other, in order to achieve the near maximum payoff available in each condition. In a second experiment, we show that this behavior is learned quickly (within 2-3 min over several discrete trials) and remained stable for as long as the payoff function did not change. The results of this work show that people are adaptive and flexible in how they prioritize and allocate attention in a dual-task setting. However, it also demonstrates some of the limits regarding people's ability to optimize payoff functions.

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This paper addresses research questions that are central to the area of visualization interfaces for decision support: (RQ1) whether individual user differences in working memory should be considered when choosing how to present visualizations; (RQ2) how to present the visualization to support effective decision making and processing; and (RQ3) how to evaluate the effectiveness of presentational choices. These questions are addressed in the context of presenting plans, or sequences of actions, to users. The experiments are conducted in several domains, and the findings are relevant to applications such as semi-autonomous systems in logistics. That is, scenarios that require the attention of humans who are likely to be interrupted, and require good performance but are not time critical. Following a literature review of different types of individual differences in users that have been found to affect the effectiveness of presentational choices, we consider specifically the influence of individuals' working memory (RQ1). The review also considers metrics used to evaluate presentational choices, and types of presentational choices considered. As for presentational choices (RQ2), we consider a number of variants including interactivity, aggregation, layout, and emphasis. Finally, to evaluate the effectiveness of plan presentational choices (RQ3) we adopt a layered-evaluation approach and measure performance in a dual task paradigm, involving both task interleaving and evaluation of situational awareness. This novel methodology for evaluating visualizations is employed in a series of experiments investigating presentational choices for a plan. A key finding is that emphasizing steps (by highlighting borders) can improve effectiveness on a primary task, but only when controlling for individual variation in working memory.