scholarly journals Sequential sampling models without random between-trial variability: the racing diffusion model of speeded decision making

2020 ◽  
Vol 27 (5) ◽  
pp. 911-936 ◽  
Author(s):  
Gabriel Tillman ◽  
Trish Van Zandt ◽  
Gordon D. Logan
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Sequential Sampling ◽  
Sequential Sampling Models

Modeling Simple Decisions and Applications Using a Diffusion Model

2015 ◽  
Author(s):  
Roger Ratcliff ◽  
Philip Smith
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Sequential Sampling ◽  
Choice Response ◽  
Neural Basis ◽  
Sequential Sampling Models ◽  
Choice Decision ◽  
Cognitive Foundations ◽  
Response Biases ◽  
Speed Accuracy

The diffusion model is one of the major sequential-sampling models for two-choice decision-making and choice response time in psychology. The model conceives of decision-making as a process in which noisy evidence is accumulated until one of two response criteria is reached and the associated response is made. The criteria represent the amount of evidence needed to make each decision and reflect the decision maker’s response biases and speed-accuracy trade-off settings. In this chapter we examine the application of the diffusion model in a variety of different settings. We discuss the optimality of the model and review its applications to a number of cognitive tasks, including perception, memory, and language tasks. We also consider its applications to normal and special populations, to the cognitive foundations of individual differences, to value-based decisions, and its role in understanding the neural basis of decision-making.

scholarly journals Decision making under time pressure: An independent test of sequential sampling models

1999 ◽  
Vol 27 (4) ◽  
pp. 713-725 ◽  
Author(s):  
Itiel E. Dror ◽  
Beth Basola ◽  
Jerome R. Busemeyer
Keyword(s):  
Decision Making ◽  
Time Pressure ◽  
Sequential Sampling ◽  
Sequential Sampling Models ◽  
Independent Test

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.

Modeling Moral Decision Making With Sequential Sampling Models

2012 ◽  
Author(s):  
Nicolas A. J. Berkowitsch ◽  
Joerg Rieskamp ◽  
Benjamin Scheibehenne
Keyword(s):  
Decision Making ◽  
Sequential Sampling ◽  
Moral Decision ◽  
Moral Decision Making ◽  
Sequential Sampling Models

scholarly journals An LBA account of decisions in the multiple object tracking task

2019 ◽  
Author(s):  
Reilly James Innes ◽  
Caroline Kuhne
Keyword(s):  
Decision Making ◽  
Object Tracking ◽  
Response Times ◽  
Sequential Sampling ◽  
Multiple Object Tracking ◽  
Tracking Task ◽  
Everyday Functioning ◽  
Multiple Object ◽  
Sequential Sampling Models ◽  
Linear Ballistic Accumulator

Decision making is a vital aspect of our everyday functioning, from simple perceptual demands to more complex and meaningful decisions. The strategy adopted to make such decisions is often viewed as balancing elements of speed and caution, i.e. making fast or careful decisions. Using sequential sampling models to analyse decision making data can allow us to tease apart strategic differences, such as being more or less cautious, from processing differences, which would otherwise be indistinguishable in behavioural data. Our study used a multiple object tracking task where student participants and a highly skilled military group were compared on their ability to track several items at once. Using a mathematical model of decision making (the linear ballistic accumulator), we show the underpinnings of how two groups differ in performance. Results showed a large difference between the groups on accuracy, with the RAAF group outperforming students. An interaction effect was observed between groups and level of difficulty in response times, where RAAF response times slowed at a greater rate than the student group as difficulty increased. Model results indicated that the RAAF personnel were more cautious in their decisions than students, and had faster processing in some conditions. Our study shows the strength of sequential sampling models, as well as providing a first attempt at fitting a sequential sampling model to data from a multiple object tracking task.

scholarly journals Unification of Countermanding and Perceptual Decision-making

2017 ◽  
Author(s):  
Paul G. Middlebrooks ◽  
Bram B. Zandbelt ◽  
Gordon D. Logan ◽  
Thomas J. Palmeri ◽  
Jeffrey D. Schall
Keyword(s):  
Decision Making ◽  
Response Inhibition ◽  
Sequential Sampling ◽  
Race Model ◽  
Decision Time ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Evidence Accumulation ◽  
Sequential Sampling Models ◽  
Choice Tasks

Perceptual decision-making, studied using two-alternative forced-choice tasks, is explained by sequential sampling models of evidence accumulation, which correspond to the dynamics of neurons in sensorimotor structures of the brain1 2. Response inhibition, studied using stop-signal (countermanding) tasks, is explained by a race model of the initiation or canceling of a response, which correspond to the dynamics of neurons in sensorimotor structures3 4. Neither standard model accounts for performance of the other task. Sequential sampling models incorporate response initiation as an uninterrupted non-decision time parameter independent of task-related variables. The countermanding race model does not account for the choice process. Here we show with new behavioral, neural and computational results that perceptual decision making of varying difficulty can be countermanded with invariant efficiency, that single prefrontal neurons instantiate both evidence accumulation and response inhibition, and that an interactive race between two GO and one STOP stochastic accumulator fits countermanding choice behavior. Thus, perceptual decision-making and response control, previously regarded as distinct mechanisms, are actually aspects of more flexible behavior supported by a common neural and computational mechanism. The identification of this aspect of decision-making with response production clarifies the component processes of decision-making.

scholarly journals Timing of readiness potentials reflect a decision-making process in the human brain

2018 ◽  
Author(s):  
Kitty K. Lui ◽  
Michael D. Nunez ◽  
Jessica M. Cassidy ◽  
Joachim Vandekerckhove ◽  
Steven C. Cramer ◽  
...  
Keyword(s):  
Decision Making ◽  
Response Time ◽  
Response Selection ◽  
Sequential Sampling ◽  
Perceptual Categorization ◽  
Accumulation Process ◽  
Time Data ◽  
Evidence Accumulation ◽  
Sequential Sampling Models ◽  
Readiness Potentials

AbstractDecision-making in two-alternative forced choice tasks has several underlying components including stimulus encoding, perceptual categorization, response selection, and response execution. Sequential sampling models of decision-making are based on an evidence accumulation process to a decision boundary. Animal and human studies have focused on perceptual categorization and provide evidence linking brain signals in parietal cortex to the evidence accumulation process. In this exploratory study, we use a task where the dominant contribution to response time is response selection and model the response time data with the drift-diffusion model. EEG measurement during the task show that the Readiness Potential (RP) recorded over motor areas has timing consistent with the evidence accumulation process. The duration of the RP predicts decision-making time, the duration of evidence accumulation, suggesting that the RP partly reflects an evidence accumulation process for response selection in the motor system. Thus, evidence accumulation may be a neural implementation of decision-making processes in both perceptual and motor systems. The contributions of perceptual categorization and response selection to evidence accumulation processes in decision-making tasks can be potentially evaluated by examining the timing of perceptual and motor EEG signals.

scholarly journals From single decisions to long-term choice patterns: Extending the dynamics of decision-making

2019 ◽  
Author(s):  
Stefan Scherbaum ◽  
Steven Lade ◽  
Thilo Gross ◽  
Stefan Siegmund ◽  
Thomas Goschke ◽  
...  
Keyword(s):  
Decision Making ◽  
Sequential Sampling ◽  
Real Life ◽  
Decision Processes ◽  
Neural Systems ◽  
Short Term ◽  
Sequential Sampling Models ◽  
Attractor Model ◽  
Trial Basis

Decision-making is usually studied on a trial by trial basis and each decision is assumed to represent an isolated choice process. These assumptions are also reflected in sequential sampling models which conceive of the decision-process as an accumulation of information about the attractiveness of the options at hand. Real-life decisions however are usually embedded in a rich context of previous choices at different time scales. A fundamental yet neglected question is therefore how the dynamics of choice processes unfold on a long-term time scale across several decisions. Here, we present a neural-inspired attractor model that integrates the short-term mechanism of accumulation models with the long-term dynamics of coupled neural systems. The model represents a class of models that incorporate inherent long-term dynamics. We use the model to predict long-term patterns, namely oscillatory switching, perseveration and dependence of perseveration on the delay between decisions. Furthermore, we predict RT effects for specific trials. We validate the predictions in two new studies and a reanalysis of existing data from a novel decision game in which participants have to perform delay discounting decisions. Applying the validated reasoning to a well-established choice questionnaire, we illustrate and discuss that taking long-term choice patterns into account may be necessary to accurately analyse and model decision processes.

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