scholarly journals Speed, Accuracy, and the Optimal Timing of Choices

2018 ◽  
Vol 108 (12) ◽  
pp. 3651-3684 ◽  
Author(s):  
Drew Fudenberg ◽  
Philipp Strack ◽  
Tomasz Strzalecki
Keyword(s):  
Sequential Sampling ◽  
Decision Time ◽  
Optimal Timing ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Prior Beliefs ◽  
Constant Cost ◽  
Speed Accuracy ◽  
Decision Times ◽  
Choice Probabilities

We model the joint distribution of choice probabilities and decision times in binary decisions as the solution to a problem of optimal sequential sampling, where the agent is uncertain of the utility of each action and pays a constant cost per unit time for gathering information. We show that choices are more likely to be correct when the agent chooses to decide quickly, provided the agent’s prior beliefs are correct. This better matches the observed correlation between decision time and choice probability than does the classical drift-diffusion model (DDM), where the agent knows the utility difference between the choices. (JEL C41, D11, D12, D83)

scholarly journals Computational modeling of the N-Back task in the ABCD study: associations of drift diffusion model parameters to polygenic scores of mental disorders and cardiometabolic diseases

2021 ◽  
Author(s):  
Mads Lund Pedersen ◽  
Dag Alnæs ◽  
Dennis van der Meer ◽  
Sara Fernandez ◽  
Pierre Berthet ◽  
...  
Keyword(s):  
Decision Making ◽  
Working Memory ◽  
Mental Disorders ◽  
Computational Modeling ◽  
Decision Time ◽  
Decision Threshold ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Cardiometabolic Diseases ◽  
Back Task

Background. Cognitive dysfunction is common in mental disorders and represents a potential risk factor in childhood. The nature and extent of associations between childhood cognitive function and polygenic risk for mental disorders is unclear. We applied computational modeling to gain insight into mechanistic processes underlying decision making and working memory in childhood and their associations with PRS for mental disorders and comorbid cardiometabolic diseases. Methods. We used the drift diffusion model to infer latent computational processes underlying decision-making and working memory during the N-back task in 3707 children aged 9-10 from the ABCD Study. SNP-based heritability was estimated for cognitive phenotypes, including computational parameters, aggregated N-back task performance and neurocognitive assessments. PRS was calculated for Alzheimer’s disease (AD), bipolar disorder, coronary artery disease (CAD), major depressive disorder, obsessive-compulsive disorder, schizophrenia and type 2 diabetes. Results. Heritability estimates of cognitive phenotypes ranged from 12 to 39%. Bayesian mixed models revealed that slower accumulation of evidence was associated with higher PRS for CAD and schizophrenia. Longer non-decision time was associated with higher PRS for AD and lower PRS for CAD. Narrower decision threshold was associated with higher PRS for CAD. Load-dependent effects on non-decision time and decision threshold were associated with PRS for AD and CAD, respectively. Aggregated neurocognitive test scores were not associated with PRS for any of the mental or cardiometabolic phenotypes.Conclusions. We identified distinct associations between computational cognitive processes to genetic risk for mental illness and cardiometabolic disease, which could represent childhood cognitive risk factors.

scholarly journals The drift diffusion model as the choice rule in inter-temporal and risky choice: a case study in medial orbitofrontal cortex lesion patients and controls

2019 ◽  
Author(s):  
Jan Peters ◽  
Mark D’Esposito
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Choice Model ◽  
Sequential Sampling ◽  
Temporal Discounting ◽  
Risky Choice ◽  
Choice Rule ◽  
Model Parameters ◽  
Drift Diffusion Model ◽  
Drift Diffusion

AbstractSequential sampling models such as the drift diffusion model have a long tradition in research on perceptual decision-making, but mounting evidence suggests that these models can account for response time distributions that arise during reinforcement learning and value-based decision-making. Building on this previous work, we implemented the drift diffusion model as the choice rule in inter-temporal choice (temporal discounting) and risky choice (probability discounting) using a hierarchical Bayesian estimation scheme. We validated our approach in data from nine patients with focal lesions to the ventromedial prefrontal cortex / medial orbitofrontal cortex (vmPFC/mOFC) and nineteen age- and education-matched controls. Choice model parameters estimated via standard softmax action selection were reliably reproduced using the drift diffusion model as the choice rule, both for temporal discounting and risky choice. Model comparison revealed that, for both tasks, the data were best accounted for by a variant of the drift diffusion model including a non-linear mapping from value-differences to trial-wise drift rates. Posterior predictive checks of the winning models revealed a reasonably good fit to individual participants reaction time distributions. We then applied this modeling framework and 1) reproduced our previous results regarding temporal discounting in vmPFC/mOFC patients and 2) showed in a previously unpublished data set on risky choice that vmPFC/mOFC patients exhibit increased risk-taking relative to controls. Analyses of diffusion model parameters revealed that vmPFC/mOFC damage abolished neither value sensitivity nor asymptote of the drift rate. Rather, it substantially increased non-decision times and reduced response caution during risky choice. Our results highlight that novel insights can be gained from applying sequential sampling models in studies of inter-temporal and risky decision-making in cognitive neuroscience.

scholarly journals Optimizing sequential decisions in the drift-diffusion model

2018 ◽  
Author(s):  
Khanh P. Nguyen ◽  
Krešimir Josić ◽  
Zachary P. Kilpatrick
Keyword(s):  
Decision Making ◽  
Ideal Observer ◽  
Natural Environments ◽  
Drift Diffusion Model ◽  
Sequential Decisions ◽  
Drift Diffusion ◽  
Evidence Accumulation ◽  
Temporal Correlations ◽  
Finite Sequences ◽  
Decision Times

AbstractTo make decisions organisms often accumulate information across multiple timescales. However, most experimental and modeling studies of decision-making focus on sequences of independent trials. On the other hand, natural environments are characterized by long temporal correlations, and evidence used to make a present choice is often relevant to future decisions. To understand decision-making under these conditions we analyze how a model ideal observer accumulates evidence to freely make choices across a sequence of correlated trials. We use principles of probabilistic inference to show that an ideal observer incorporates information obtained on one trial as an initial bias on the next. This bias decreases the time, but not the accuracy of the next decision. Furthermore, in finite sequences of trials the rate of reward is maximized when the observer deliberates longer for early decisions, but responds more quickly towards the end of the sequence. Our model also explains experimentally observed patterns in decision times and choices, thus providing a mathematically principled foundation for evidence-accumulation models of sequential decisions.

scholarly journals It’s not always about me: The effects of prior beliefs and stimulus prevalence on self–other prioritisation

2020 ◽  
Vol 73 (9) ◽  
pp. 1466-1480
Author(s):  
Johanna K Falbén ◽  
Marius Golubickis ◽  
Darja Wischerath ◽  
Dimitra Tsamadi ◽  
Linn M Persson ◽  
...  
Keyword(s):  
Social Cognitive ◽  
Stimulus Presentation ◽  
Drift Diffusion Model ◽  
Stimulus Uncertainty ◽  
Drift Diffusion ◽  
Prior Beliefs ◽  
Object Processing ◽  
Research Objects ◽  
Ownership Effect ◽  
Relevant Material

Although self-relevance is widely acknowledged to enhance stimulus processing, the exclusivity of this effect remains open to question. In particular, in commonly adopted experimental paradigms, the prioritisation of self-relevant (vs. other-relevant) material may reflect the operation of a task-specific strategy rather than an obligatory facet of social-cognitive functioning. By changing basic aspects of the decisional context, it may therefore be possible to generate stimulus-prioritisation effects for targets other than the self. Based on the demonstration that ownership facilitates object categorisation (i.e., self-ownership effect), here we showed that stimulus prioritisation is sensitive to prior expectations about the prevalence of forthcoming objects (owned-by-self vs. owned-by-friend) and whether these beliefs are supported during the task. Under conditions of stimulus uncertainty (i.e., no prior beliefs), replicating previous research, objects were classified more rapidly when owned-by-self compared with owned-by-friend (Experiment 1). When, however, the frequency of stimulus presentation either confirmed (Experiment 2) or disconfirmed (Experiment 3) prior expectations, stimulus prioritisation was observed for the most prevalent objects regardless of their owner. A hierarchical drift diffusion model (HDDM) analysis further revealed that decisional bias was underpinned by differences in the evidential requirements of response generation. These findings underscore the flexibility of ownership effects (i.e., stimulus prioritisation) during object processing.

scholarly journals The Leaky Integrating Threshold and its impact on evidence accumulation models of choice RT

2020 ◽  
Author(s):  
Stijn Verdonck ◽  
Tim Loossens ◽  
Marios G. Philiastides
Keyword(s):  
Diffusion Model ◽  
Motor Preparation ◽  
Difficulty Level ◽  
Choice Response ◽  
Decision Threshold ◽  
Actual Process ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Evidence Accumulation ◽  
Speed Accuracy

A common assumption in choice response time modelling is that after evidence accumulation reaches a certain decision threshold, the choice is categorically communicated to the motor system that then executes the response. However, neurophysiological findings suggest that motor preparation partly overlaps with evidence accumulation, and is not independent from stimulus difficulty level. We propose to model this entanglement by changing the nature of the decision criterion from a simple threshold to an actual process. More specifically, we propose a secondary, motor preparation related, leaky accumulation process that takes the accumulated evidence of the original decision process as a continuous input, and triggers the actual response when it reaches its own threshold. We analytically develop this Leaky Integrating Threshold (LIT), applying it to a simple constant drift diffusion model, and show how its parameters can be estimated with the D*M method. Reanalyzing three different datasets, the LIT extension is shown to outperform a standard drift diffusion model using multiple statistical approaches. Further, the LIT leak parameter is shown to be better at explaining the speed/accuracy trade-off manipulation than the commonly used boundary separation parameter. These improvements can also be verified using traditional diffusion model analyses, for which the LIT predicts the violation of several common selective parameter influence assumptions. These predictions are consistent with what is found in the data and with what is reported experimentally in the literature. Crucially, this work offers a new benchmark against which to compare neural data to offer neurobiological validation for the proposed processes.

scholarly journals Testing the drift-diffusion model

2020 ◽  
Vol 117 (52) ◽  
pp. 33141-33148
Author(s):  
Drew Fudenberg ◽  
Whitney Newey ◽  
Philipp Strack ◽  
Tomasz Strzalecki
Keyword(s):  
Decision Maker ◽  
Diffusion Model ◽  
Sequential Sampling ◽  
Statistical Test ◽  
Drift Diffusion Model ◽  
Test Statistic ◽  
Drift Diffusion ◽  
Finite Samples ◽  
Over Time

The drift-diffusion model (DDM) is a model of sequential sampling with diffusion signals, where the decision maker accumulates evidence until the process hits either an upper or lower stopping boundary and then stops and chooses the alternative that corresponds to that boundary. In perceptual tasks, the drift of the process is related to which choice is objectively correct, whereas in consumption tasks, the drift is related to the relative appeal of the alternatives. The simplest version of the DDM assumes that the stopping boundaries are constant over time. More recently, a number of papers have used nonconstant boundaries to better fit the data. This paper provides a statistical test for DDMs with general, nonconstant boundaries. As a by-product, we show that the drift and the boundary are uniquely identified. We use our condition to nonparametrically estimate the drift and the boundary and construct a test statistic based on finite samples.

scholarly journals Sensitivity of reaction time to the magnitude of rewards reveals the cost-structure of time

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kai Steverson ◽  
Hui-Kuan Chung ◽  
Jan Zimmermann ◽  
Kenway Louie ◽  
Paul Glimcher
Keyword(s):  
Decision Making ◽  
Functional Form ◽  
Reaction Times ◽  
Error Rates ◽  
Empirical Literature ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Algorithmic Framework ◽  
The Cost ◽  
Speed Accuracy

AbstractThe Drift-Diffusion Model (DDM) is the prevalent computational model of the speed-accuracy trade-off in decision making. The DDM provides an explanation of behavior by optimally balancing reaction times and error rates. However, when applied to value-based decision making, the DDM makes the stark prediction that reaction times depend only on the relative utility difference between the options and not on absolute utility magnitudes. This prediction runs counter to evidence that reaction times decrease with higher utility magnitude. Here, we ask if and how it could be optimal for reaction times to show this observed pattern. We study an algorithmic framework that balances the cost of delaying rewards against the utility of obtained rewards. We find that the functional form of the cost of delay plays a key role, with the empirically observed pattern becoming optimal under multiplicative discounting. We add to the empirical literature by testing whether utility magnitude affects reaction times using a novel methodology that does not rely on functional form assumptions for the subjects’ utilities. Our results advance the understanding of how and why reaction times are sensitive to the magnitude of rewards.

scholarly journals Flexible categorization in perceptual decision making

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Network Models ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Sensory Evidence ◽  
Winner Take All

AbstractPerceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

scholarly journals Informing cognitive abstractions through neuroimaging: The neural drift diffusion model.

2015 ◽  
Vol 122 (2) ◽  
pp. 312-336 ◽  
Author(s):  
Brandon M. Turner ◽  
Leendert van Maanen ◽  
Birte U. Forstmann
Keyword(s):  
Diffusion Model ◽  
Drift Diffusion Model ◽  
Drift Diffusion
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