When faced with a decision between several options, people rarely fully consider every alternative. Instead, we direct our attention to the most promising candidates, focusing our limited cognitive resources on evaluating the options that we are most likely to choose. A growing body of empirical work has shown that attention plays an important role in human decision making, but it is still unclear how people choose with option to attend to at each moment in the decision making process. In this paper, we present an analysis of how a rational decision maker should allocate her attention. We cast attention allocation in decision making as a sequential sampling problem, in which the decision maker iteratively selects from which distribution to sample in order to update her beliefs about the values of the available alternatives. By approximating the optimal solution to this problem, we derive a model in which both the selection and integration of evidence are rational. This model predicts choices and reaction times, as well as sequences of visual fixations. Applying the model to a ternary-choice dataset, we find that its predictions align well with human data.

Simple choices (e.g., eating an apple vs. an orange) are made by integrating noisy evidence that is sampled over time and influenced by visual attention; as a result, fluctuations in visual attention can affect choices. But what determines what is fixated and when? To address this question, we model the decision process for simple choice as an information sampling problem, and approximate the optimal sampling policy. We find that it is optimal to sample from options whose value estimates are both high and uncertain. Furthermore, the optimal policy provides a reasonable account of fixations and choices in binary and trinary simple choice, as well as the differences between the two cases. Overall, the results show that the fixation process during simple choice is influenced dynamically by the value estimates computed during the decision process, in a manner consistent with optimal information sampling.

No abstract

Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose a model of resource-constrained planning that allows us to derive optimal planning strategies. We find that previously proposed heuristics such as best-first search are near-optimal under some circumstances, but not others. In a mouse-tracking paradigm, we show that people adapt their planning strategies accordingly, planning in a manner that is broadly consistent with the optimal model but not with any single heuristic model. We also find systematic deviations from the optimal model that might result from additional cognitive constraints that are yet to be uncovered.

A critical aspect of human intelligence is our ability to plan, that is, to use a model of the world to simulate, evaluate, and select among hypothetical future actions. However, exhaustive planning is intractable because the number of possible action sequences increases exponentially with the number of steps that one plans ahead. Understanding how people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus critical to understanding human intelligence. Progress in answering this question has been hampered by two challenges: planning cannot be observed and we do not have a good framework for formalizing the tradeoff between performance and computational cost. In this work, we propose solutions to both challenges, based on the idea that planning can be conceptualized as information seeking. Specifically, we model planning as the selection of information-generating computations and introduce an experimental paradigm in which these computations are externalized as mouse clicks. We find that our participants' behavior is broadly consistent with the optimal information-seeking model. We also uncover systematic deviations that might result from heuristic approximations or additional cognitive constraints that have yet to be uncovered.