scholarly journals Diffusion Decision Model: Current Issues and History

2016 ◽  
Vol 20 (4) ◽  
pp. 260-281 ◽  
Author(s):  
Roger Ratcliff ◽  
Philip L. Smith ◽  
Scott D. Brown ◽  
Gail McKoon
Keyword(s):  
Decision Model ◽  
Diffusion Decision Model

scholarly journals A reinforcement learning diffusion decision model for value-based decisions

2019 ◽  
Vol 26 (4) ◽  
pp. 1099-1121 ◽  
Author(s):  
Laura Fontanesi ◽  
Sebastian Gluth ◽  
Mikhail S. Spektor ◽  
Jörg Rieskamp
Keyword(s):  
Reinforcement Learning ◽  
Decision Model ◽  
Diffusion Decision Model

scholarly journals CHaRTr: An R toolbox for modeling Choices and Response Times in decision-making tasks

2019 ◽  
Author(s):  
Chandramouli Chandrasekaran ◽  
Guy E. Hawkins
Keyword(s):  
Decision Making ◽  
Decision Model ◽  
Response Times ◽  
Research Effort ◽  
Perceptual Decision Making ◽  
Low Pass ◽  
Decision Making Models ◽  
Collapsing Boundaries ◽  
Decision Making Model ◽  
Diffusion Decision Model

AbstractDecision-making is the process of choosing and performing actions in response to sensory cues so as to achieve behavioral goals. A sophisticated research effort has led to the development of many mathematical models to describe the response time (RT) distributions and choice behavior of observers performing decision-making tasks. However, relatively few researchers use these models because it demands expertise in various numerical, statistical, and software techniques. Although some of these problems have been surmounted in existing software packages, the packages have often focused on the classical decision-making model, the diffusion decision model. Recent theoretical advances in decision-making that posit roles for “urgency”, time-varying decision thresholds, noise in various aspects of the decision-formation process or low pass filtering of sensory evidence, have proven to be challenging to incorporate in a coherent software framework that permits quantitative evaluations among these competing classes of decision-making models. Here, we present a toolbox —Choices and Response Times in R, orCHaRTr— that provides the user the ability to implement and test a wide variety of decision-making models ranging from classic through to modern versions of the diffusion decision model, to models with urgency signals, or collapsing boundaries. Earlier versions ofCHaRTrhave been instrumental in a number of recent studies of humans and monkeys performing perceptual decision-making tasks. We also provide guidance on how to extend the toolbox to incorporate future developments in decision-making models.

scholarly journals Estimating Across-Trial Variability Parameters of the Diffusion Decision Model: Expert Advice and Recommendations

2018 ◽  
Author(s):  
Udo Boehm ◽  
Jeff Annis ◽  
Michael Frank ◽  
Guy Hawkins ◽  
Andrew Heathcote ◽  
...  
Keyword(s):  
Response Time ◽  
Decision Model ◽  
Simulated Data ◽  
Expert Advice ◽  
Collaborative Project ◽  
Wide Range ◽  
Problems And Solutions ◽  
Response Time Distributions ◽  
Diffusion Decision Model ◽  
Comprehensive Reference

For many years the Diffusion Decision Model (DDM) has successfully accounted for behavioral data from a wide range of domains. Important contributors to the DDM’s success are the across-trial variability parameters, which allow the model to account for the various shapes of response time distributions encountered in practice. However, several researchers have pointed out that estimating the variability parameters can be a challenging task. Moreover, the numerous fitting methods for the DDM each come with their own associated problems and solutions. This often leaves users in a difficult position. In this collaborative project we invited researchers from the DDM community to apply their various fitting methods to simulated data and provide advice and expert guidance on estimating the DDM’s between-trial variability parameters using these methods. Our study establishes a comprehensive reference resource and describes methods that can help to overcome the challenges associated with estimating the DDM’s across-trial variability parameters.

scholarly journals Bayesian analysis of the piecewise diffusion decision model

2017 ◽  
Vol 50 (2) ◽  
pp. 730-743 ◽  
Author(s):  
William R. Holmes ◽  
Jennifer S. Trueblood
Keyword(s):  
Bayesian Analysis ◽  
Decision Model ◽  
Diffusion Decision Model

scholarly journals Estimating across-trial variability parameters of the Diffusion Decision Model: Expert advice and recommendations

2018 ◽  
Vol 87 ◽  
pp. 46-75 ◽  
Author(s):  
Udo Boehm ◽  
Jeffrey Annis ◽  
Michael J. Frank ◽  
Guy E. Hawkins ◽  
Andrew Heathcote ◽  
...  
Keyword(s):  
Decision Model ◽  
Expert Advice ◽  
Diffusion Decision Model

Sleep Deprivation and Racial Bias in the Decision to Shoot: A Diffusion Model Analysis

2020 ◽  
pp. 194855062093272
Author(s):  
David J. Johnson ◽  
Michelle E. Stepan ◽  
Joseph Cesario ◽  
Kimberly M. Fenn
Keyword(s):  
Sleep Deprivation ◽  
Diffusion Model ◽  
Decision Model ◽  
Racial Bias ◽  
Model Analysis ◽  
The Impact ◽  
Shooting Decisions ◽  
Process Level ◽  
Diffusion Decision Model ◽  
Extract Information

The current study examines the effect of sleep deprivation and caffeine use on racial bias in the decision to shoot. Participants deprived of sleep for 24 hr (vs. rested participants) made more errors in a shooting task and were more likely to shoot unarmed targets. A diffusion decision model analysis revealed sleep deprivation decreased participants’ ability to extract information from the stimuli, whereas caffeine impacted the threshold separation, reflecting decreased caution. Neither sleep deprivation nor caffeine moderated anti-Black racial bias in shooting decisions or at the process level. We discuss how our results clarify discrepancies in past work testing the impact of fatigue on racial bias in shooting decisions.

scholarly journals Effects of aging in a task-switch paradigm with the diffusion decision model.

2020 ◽  
Vol 35 (6) ◽  
pp. 850-865
Author(s):  
Nadja R. Ging-Jehli ◽  
Roger Ratcliff
Keyword(s):  
Decision Model ◽  
Task Switch ◽  
Effects Of Aging ◽  
Diffusion Decision Model

scholarly journals Predictive activation of sensory representations as a source of evidence in perceptual decision-making

2020 ◽  
Author(s):  
Daniel Feuerriegel ◽  
Tessel Blom ◽  
Hinze Hogendoorn
Keyword(s):  
Decision Making ◽  
Decision Model ◽  
Perceptual Decision Making ◽  
Specific Stimulus ◽  
Perceptual Decision ◽  
Evidence Accumulation ◽  
Neural Representations ◽  
Sensory Environment ◽  
Sensory Evidence ◽  
Diffusion Decision Model

Our brains can represent expected future states of our sensory environment. Recent work has shown that, when we expect a specific stimulus to appear at a specific time, we can predictively generate neural representations of that stimulus even before it is physically presented. These observations raise two exciting questions: Are pre-activated sensory representations used for perceptual decision-making? And, are there instances in which we transiently perceive an expected stimulus that does not actually appear? To address these questions, we propose that pre-activated neural representations provide sensory evidence that is used for perceptual decision-making. This can be understood within the framework of the Diffusion Decision Model as an early accumulation of decision evidence in favour of the expected percept. Our proposal makes novel predictions relating to expectation effects on neural markers of decision evidence accumulation, and also provides an explanation for why we do not typically perceive stimuli that are expected, but do not appear.

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