The Drift Diffusion Model Can Account for the Accuracy and Reaction Time of Value-Based Choices Under High and Low Time Pressure

Mormann, Milica; Malmaud, Jonathan; Huth, Alexander G.; Koch, Christof

doi:10.2139/ssrn.1901533

Cited by 199 publications

(303 citation statements)

References 49 publications

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“…Even in the binary choice case, there are many variations to the DDM that have been proposed in the literature (9,17,27,42). This gives rise to a large number of possible model permutations, and thus to a difficult problem of model comparison (5,27).…”

Section: Discussionmentioning

confidence: 99%

“…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)(11)(12)(13)(14)(15)(16), and more recently in binary value-based choice (17)(18)(19)(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.…”

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confidence: 99%

“…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, consider the case of binary value-based choice.…”

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

Krajbich¹,

Rangel

2011

Proc. Natl. Acad. Sci. U.S.A.

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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, ...

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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

Krajbich¹,

Rangel

2011

Proc. Natl. Acad. Sci. U.S.A.

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610

774

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“…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.…”

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confidence: 99%

“…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.…”

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Evidence integration in model-based tree search

Solway

Botvinick

2015

Proc. Natl. Acad. Sci. U.S.A.

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Research on the dynamics of reward-based, goal-directed decision making has largely focused on simple choice, where participants decide among a set of unitary, mutually exclusive options. Recent work suggests that the deliberation process underlying simple choice can be understood in terms of evidence integration: Noisy evidence in favor of each option accrues over time, until the evidence in favor of one option is significantly greater than the rest. However, real-life decisions often involve not one, but several steps of action, requiring a consideration of cumulative rewards and a sensitivity to recursive decision structure. We present results from two experiments that leveraged techniques previously applied to simple choice to shed light on the deliberation process underlying multistep choice. We interpret the results from these experiments in terms of a new computational model, which extends the evidence accumulation perspective to multiple steps of action.reward-based decision making | drift-diffusion model | reinforcement learning I magine a customer standing at the counter in an ice cream shop, deliberating among the available flavors. 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 (1-5). However, it would be an obvious mistake to treat it as an exhaustive model of rewardbased decision making. The decisions that arise in everyday life are of course often more complicated. One important difference, among others, is that everyday decisions tend to involve sequences of actions and outcomes.As an illustration, let us return to the ice cream customer, picturing him at a point slightly earlier in the day, exiting his home in quest of something sweet. Upon reaching the sidewalk, he faces a decision between heading left toward the ice cream shop, or heading right toward a frozen yogurt shop. If he wishes to fully evaluate the relative appeal of these two options, he must answer a second set of questions: Which flavor would he choose in each shop? Furthermore, it may be relevant for him to consider more immediate consequences of the left-right decision. For example, the leftward path might pass by a bank, allowing him to deposit a check along his way, whereas the rightward path might lead by the post office, giving him the opportunity to mail a package.Rather than selecting among individual and immediate outcomes, the decision maker in this scenario finds himself at the root of a decision tree (Fig. 1A), with nodes corresponding to value-laden outcomes or states, and edges corresponding to choiceinduced state transitions. Deciding among immediate actions, even at the first branch point, requires a consideration of all of the paths that unfold below. Decision making thus assumes the form of reward-based tree search (6-10).Note that decision makin...

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Eyes on the Prize? Evidence of Diminishing Attention to Experienced and Foregone Outcomes in Repeated Experiential Choice

Ashby

Rakow

2015

Behavioral Decision Making

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Recently there has been increased interest in decisions-from-experience (where decision makers learn from observing the outcomes of previous choices), which provide valuable insights into the learning and preference construction processes underlying many daily decisions. Several process models have been developed to capture these processes, and while such models often fit the data well, many assume that the decision maker is a vigilant observer, processing each outcome. In two studies, we provide a critical test of this assumption using eye tracking to record directed visual attention when participants choose repeatedly among two options, each time being shown the outcome for their chosen option and for the foregone option. Consistently, we find that the vigilance assumption is not supported, with decision makers often not attending to outcome information. Moreover, (in)attention to outcomes is predictable, with vigilance decreasing as more choices are made, and being greater for obtained than for foregone outcomes, and when options deliver only gains as opposed to losses, or a mixture of gains and losses. Furthermore, we find that this variation in attentional allocation plays a central role in the apparent indecisiveness (inconsistency) in choice, with increased attention to foregone outcomes predicting switches to that option on the next choice. Together, these findings highlight the value of eye tracking in investigations of decisions-from-experience, providing novel insight into cognitive processes underlying them.

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The Drift Diffusion Model Can Account for the Accuracy and Reaction Time of Value-Based Choices Under High and Low Time Pressure

Cited by 199 publications

References 49 publications

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

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

Evidence integration in model-based tree search

Eyes on the Prize? Evidence of Diminishing Attention to Experienced and Foregone Outcomes in Repeated Experiential Choice

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