Response Times and Decision‐Making

Donkin, Chris; Brown, Scott

doi:10.1002/9781119170174.epcn509

Cited by 49 publications

(55 citation statements)

References 142 publications

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“…In the study of error-driven learning (O’Doherty et al, 2017; Sutton and Barto, 2018), the decision process is typically simplified to soft-max, a descriptive model that offers no process-level understanding of how decisions arise from representations, and ignores choice response times (RTs). In the study of decision-making using evidence-accumulation models (EAMs; Donkin and Brown, 2018; Forstmann et al, 2016; Ratcliff et al, 2016), tasks are typically designed to minimize the influence of learning, and residual variability caused by learning is treated as noise.…”

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confidence: 99%

A new model of decision processing in instrumental learning tasks

Miletić

Boag

Trutti

et al. 2020

Preprint

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Learning and decision making are interactive processes, yet cognitive modelling of error-driven learning and decision making have largely evolved separately. Recently, evidence accumulation models (EAMs) of decision making and reinforcement learning (RL) models of error-driven learning have been combined into joint RL-EAMs that can in principle address these interactions. However, we show that the most commonly used combination, based on the diffusion decision model (DDM) for binary choice, consistently fails to capture crucial aspects of response times observed during reinforcement learning. We propose a new RL-EAM based on an advantage racing diffusion (ARD) framework for choices among two or more options that not only addresses this problem but captures stimulus difficulty, speed-accuracy trade-off, and stimulus-response-mapping reversal effects. The RL-ARD avoids fundamental limitations imposed by the DDM on addressing effects of absolute values of choices, as well as extensions beyond binary choice, and provides a computationally tractable basis for wider applications.

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confidence: 99%

A new model of decision processing in instrumental learning tasks

Miletić

Boag

Trutti

et al. 2020

Preprint

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“…Evidence accumulation models have developed over the last 50 years with architectural extensions added over time to account for more complex empirical observations (see Donkin & Brown, 2018 for a more detailed review). Early models assumed that a single accumulator indexed the difference in evidence for each choice followed a random walk process; response time variability was captured by the moment-to-moment change in evidence (Stone, 1960).…”

Section: Evidence Accumulation Modelsmentioning

confidence: 99%

An evidence accumulation model of perceptual discrimination with naturalistic stimuli

Palada¹,

Searston²,

Persson³

et al. 2018

Preprint

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Evidence accumulation models have been used to describe the cognitive processes underlying performance in tasks involving two-choice decisions about unidimensional stimuli, such as motion or orientation. Given the multidimensionality of natural stimuli, however, we might expect qualitatively different patterns of evidence accumulation in more applied perceptual tasks. One domain that relies heavily on human decisions about complex natural stimuli is fingerprint discrimination. We know little about the ability of evidence accumulation models to account for the dynamic decision process of a fingerprint examiner resolving if two different prints belong to the same finger or not. Here, we apply a dynamic decision-making model -the linear ballistic accumulator (LBA) -to fingerprint discrimination decisions in order to gain insight into the cognitive processes underlying these complex perceptual judgments. Across three experiments, we show that the LBA provides an accurate description of the fingerprint discrimination decision process with manipulations in visual noise, speedaccuracy emphasis, and training. Our results demonstrate that the LBA is a promising model for furthering our understanding of applied decision-making with naturally varying visual stimuli.

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“…Cognitive models of response times and accuracy canonically assume an accumulation process, where evidence favoring different options is summed over time until a threshold is reached that triggers an associated response. The two most prominent types of evidence-accumulation models, the diffusion decision model (DDM; Ratcliff, 1978;Ratcliff & McKoon, 2008) and the linear ballistic accumulator (LBA; Brown & Heathcote, 2008) have been widely applied across animal and human research in biology, psychology, economics, and the neurosciences to topics including vision, attention, language, memory, cognition, emotion, development, aging, and clinical disorders (for reviews, see Mulder, Van Maanen, & Forstmann, 2014;Ratcliff, Smith, Brown, & McKoon, 2016;Donkin & Brown, 2018). Evidence-accumulation models are popular because they provide a comprehensive account of the probability Quentin F. Gronau quentin.f.gronau@gmail.com 1 University of Amsterdam, Amsterdam, Netherlands 2 University of Tasmania, Hobart, Australia of choices and the associated distribution of times to make them, and because they provide parameter estimates that directly quantify important psychological quantities, such as the quality of the evidence provided by a choice stimulus and the amount of evidence required to trigger the response.…”

Section: Introductionmentioning

confidence: 99%

Computing Bayes factors for evidence-accumulation models using Warp-III bridge sampling

2019

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Over the last decade, the Bayesian estimation of evidence-accumulation models has gained popularity, largely due to the advantages afforded by the Bayesian hierarchical framework. Despite recent advances in the Bayesian estimation of evidence-accumulation models, model comparison continues to rely on suboptimal procedures, such as posterior parameter inference and model selection criteria known to favor overly complex models. In this paper, we advocate model comparison for evidence-accumulation models based on the Bayes factor obtained via Warp-III bridge sampling. We demonstrate, using the linear ballistic accumulator (LBA), that Warp-III sampling provides a powerful and flexible approach that can be applied to both nested and non-nested model comparisons, even in complex and high-dimensional hierarchical instantiations of the LBA. We provide an easy-to-use software implementation of the Warp-III sampler and outline a series of recommendations aimed at facilitating the use of Warp-III sampling in practical applications. Keywords Bayesian model comparison • Differential evolution Markov chain Monte Carlo • Dynamic models of choice • Linear ballistic accumulator • Marginal likelihood • Response time models

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Response Times and Decision‐Making

Cited by 49 publications

References 142 publications

A new model of decision processing in instrumental learning tasks

A new model of decision processing in instrumental learning tasks

An evidence accumulation model of perceptual discrimination with naturalistic stimuli

Computing Bayes factors for evidence-accumulation models using Warp-III bridge sampling

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