Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions

Abstract

How do we make decisions when confronted with several alternatives (e.g., on a supermarket shelf)? Previous work has shown that accumulator models, such as the drift-diffusion model, can provide accurate descriptions of the psychometric data for binary value-based choices, and that the choice process is guided by visual attention. However, the computational processes used to make choices in more complicated situations involving three or more options are unknown. We propose a model of trinary value-based choice that generalizes what is known about binary choice, and test it using an eye-tracking experiment. We find that the model provides a quantitatively accurate description of the relationship between choice, reaction time, and visual fixation data using the same parameters that were estimated in previous work on binary choice. Our findings suggest that the brain uses similar computational processes to make binary and trinary choices.A basic goal of decision neuroscience is to characterize the computational processes used by individuals to make different types of decisions, as well as the neurobiological substrates of such computations (1-5). A significant amount of effort has been devoted to characterizing these processes in the realm of perceptual decision making involving two-alternative forced choices (2, 6-8). However, many important decisions do not fit this framework: they involve choices among multiple alternatives (n > 2) associated with different reward values (e.g., which food to select from a buffet table). Here we investigate these types of decisions.The standard drift-diffusion model (DDM), as well as closely related versions, such as the leaky competitive accumulator (LCA) model (3, 4, 9), have been highly successful in providing quantitative explanations of the psychometrics, chronometrics, and neurometrics of binary perceptual choice (2, 10-16), and more recently in binary value-based choice (17-20). These models assume that decisions are made by accumulating stochastic information over time until the net evidence in favor of one option exceeds a prespecified threshold. The size of the threshold can be chosen to optimally balance the benefit of accumulating more information with the cost of taking more time to reach a decision (21). Consider, for example, the canonical dot-motion task that has been widely used to study perceptual decision making. Here the stimulus itself is stochastic and each instant is thought to provide noisy but informative evidence for the net direction of movement in the display. Thus, as the individual accumulates more evidence, his knowledge about the true net direction of movement increases (2).The DDM has also been shown to provide highly accurate descriptions of accuracy and response times in domains such as memory retrieval and decision-making, where the stimuli are not explicitly stochastic (17,19,20,(22)(23)(24)(25)(26)(27)(28); this suggests that these decisions might be made using a similar process of random information accumulation and integration. To see why, ...

Biasing moral decisions by exploiting the dynamics of eye gaze

“…Several frameworks and models have been proposed where attentional processes affect the speed and content of decisions (8,10,31,46,47). Of these, the aDDM model (18,19,31) makes the most detailed and strongest claim about eye gaze. It posits that decisions are driven by a diffusion process (48,49), whereby evidence is stochastically accumulated over time.…”

“…In decision tasks, before asserting their preference for faces or similarly valued snack foods people look more toward the alternative they are going to choose (17,19). For example, the attentional driftdiffusion model (aDDM) proposes a computational mechanism underlying choice whereby gaze direction biases the decision process (19,31). Similarly, studies measuring hand and eye movements show that attitudes and preferences are dynamically constructed over the course of a trial (20,29,30).…”

Evidence integration in model-based tree search

“…Such a scenario exemplifies "simple choice," a decision situation in which the objective is to select among a set of individual, immediate outcomes, each carrying a different reward. Simple choice, in this sense, has provided a convenient focus for a great deal of work in behavioral economics and decision neuroscience (1)(2)(3)(4)(5). However, it would be an obvious mistake to treat it as an exhaustive model of rewardbased decision making.…”

“…This situation contrasts sharply with what one finds in the literature on simple choice, where a number of detailed process models have been proposed. Although important differences exist, current models of simple choice converge on a common evidence-integration paradigm (1)(2)(3)(4)(5)(24)(25)(26). Here, each choice option is associated with a specific utility, but this quantity can only be accessed through a noisy sampling Significance Recent behavioral research has made rapid progress toward revealing the processes by which we make choices based on judgments of subjective value.…”

“…This procedure involves collecting a series of samples for each choice option and integrating across them until the accumulated evidence in favor of one option is significantly greater than the rest. This evidence-integration framework, often implemented in the form of a drift-diffusion process, accounts for detailed behavioral data, both at the level of reaction times and choice probabilities (2)(3)(4).…”