Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model

Tran, N.-Han; Maanen, Leendert van; Heathcote, Andrew; Matzke, Dora

doi:10.31234/osf.io/6jyf3

Cited by 5 publications

(8 citation statements)

References 65 publications

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“…Foreshadowing the results, the RL-DDM A3 improved the quality of fit compared to the RL-DDM, but required an implausibly high non-decision time variability: The across-subject mean of the median posterior estimates of the t0 and

parameters indicate a non-decision time distribution of [0.27 s, 0.64 s]. The range of 0.37 s is very high in light of the literature ( Tran et al, 2021 ), raising the question of its psychological plausibility. For this reason, as well as since the RL-DDM is used most often without

, we focus on the RL-DDM (without between-trial variabilities) in the main text.…”

Section: Methodsmentioning

confidence: 80%

A new model of decision processing in instrumental learning tasks

Miletić

Boag

Trutti

et al. 2021

eLife

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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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, we focus on the RL-DDM (without between-trial variabilities) in the main text.…”

Section: Methodsmentioning

confidence: 80%

A new model of decision processing in instrumental learning tasks

Miletić

Boag

Trutti

et al. 2021

eLife

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“…We followed general recommendations for a principled Bayesian workflow for building and validating bespoke cognitive models (Kennedy et al, 2019;Schad et al, 2019;Tran et al, 2020). Knowledge about data typical in two-choice speeded decision tasks was used to define the prior distributions on the model Here, the EAM HMM model is applied to each of the participants separately, and the model fit is assessed using posterior predictives.…”

Section: Resultsmentioning

confidence: 99%

“…P r e p r i n t The prior distributions specified above may seem extremely informative, introducing "subjective" bias to the analysis. However, we believe the prior distributions are justified by our prior predictive simulations and based on cumulative characterizations of psychological processes underlying a lexical decision and a perceptual decision task of EAMs (Tran et al, 2020). Further, prior distributions may be also regarded as constraining the parameter space to plausible values (Kennedy, Simpson, & Gelman, 2019;Tran et al, 2020;Vanpaemel, 2011), similarly as a traditional statistician would decide on ranges of parameters for a simulation study.…”

Section: Quantilementioning

confidence: 99%

“…However, we believe the prior distributions are justified by our prior predictive simulations and based on cumulative characterizations of psychological processes underlying a lexical decision and a perceptual decision task of EAMs (Tran et al, 2020). Further, prior distributions may be also regarded as constraining the parameter space to plausible values (Kennedy, Simpson, & Gelman, 2019;Tran et al, 2020;Vanpaemel, 2011), similarly as a traditional statistician would decide on ranges of parameters for a simulation study. In the current study, the prior distributions actually cover slightly more volume of the parameter space than is typical in simulation studies of similar type (e.g., Donkin et al, 2011;Visser & Poessé, 2017).…”

Section: Quantilementioning

confidence: 99%

“…Second, the responses under the guessing state are relatively rapid: responses in simple perceptual decision tasks can be faster than 1 sec on average. Third, the majority of response times does not exceed 5 sec (Tran, van Maanen, Heathcote, & Matzke, 2020).…”

Section: Latent State Distributionmentioning

confidence: 99%

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Hidden Markov Models of Evidence Accumulation in Speeded Decision Tasks

Kucharský¹,

Tran²,

Veldkamp³

et al. 2020

Preprint

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Speeded decision tasks are usually modeled within the evidence accumulation framework, enabling inferences on latent cognitive parameters, and capturing dependencies between the observed response times and accuracy. An example is the speed-accuracy trade-off, where people sacrifice speed for accuracy (or vice versa). Different views on this phenomenon lead to the idea that participants may not be able to control this trade-off on a continuum, but rather switch between distinct states (Dutilh, et al., 2010).Hidden Markov models are used to account for switching between distinct states. However, combining evidence accumulation models with a hidden Markov structure is a challenging problem, as evidence accumulation models typically come with identification and computational issues that make them challenging on their own. Thus, hidden Markov models have not used the evidence accumulation framework, giving up on the inference on the latent cognitive parameters, or capturing potential dependencies between response times and accuracy within the states.This article presents a model that uses an evidence accumulation model as part of a hidden Markov structure. This model is considered as a proof of principle that evidence accumulation models can be combined with Markov switching models. As such, the article considers a very simple case of a simplified Linear Ballistic Accumulation. An extensive simulation study was conducted to validate the model's implementation according to principles of robust Bayesian workflow. Example reanalysis of data from Dutilh, et al. (2010) demonstrates the application of the new model. The article concludes with limitations and future extensions or alternatives to the model and its application.

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Conceptually plausible Bayesian inference in interval timing

et al. 2021

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In a world that is uncertain and noisy, perception makes use of optimization procedures that rely on the statistical properties of previous experiences. A well-known example of this phenomenon is the central tendency effect observed in many psychophysical modalities. For example, in interval timing tasks, previous experiences influence the current percept, pulling behavioural responses towards the mean. In Bayesian observer models, these previous experiences are typically modelled by unimodal statistical distributions, referred to as the prior. Here, we critically assess the validity of the assumptions underlying these models and propose a model that allows for more flexible, yet conceptually more plausible, modelling of empirical distributions. By representing previous experiences as a mixture of lognormal distributions, this model can be parametrized to mimic different unimodal distributions and thus extends previous instantiations of Bayesian observer models. We fit the mixture lognormal model to published interval timing data of healthy young adults and a clinical population of aged mild cognitive impairment patients and age-matched controls, and demonstrate that this model better explains behavioural data and provides new insights into the mechanisms that underlie the behaviour of a memory-affected clinical population.

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Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model

Cited by 5 publications

References 65 publications

A new model of decision processing in instrumental learning tasks

A new model of decision processing in instrumental learning tasks

Hidden Markov Models of Evidence Accumulation in Speeded Decision Tasks

Conceptually plausible Bayesian inference in interval timing

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