Evidence accumulation models with R: A practical guide to hierarchical Bayesian methods

Lin, Yi Shin; Strickland, Luke

doi:10.20982/tqmp.16.2.p133

Cited by 15 publications

(14 citation statements)

References 46 publications

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“…Hierarchical LBA modeling was performed with the “ggdmc” package (Lin & Strickland, 2020). This package computes Bayesian hierarchical modeling.…”

Section: Resultsmentioning

confidence: 99%

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Cognitive appraisal contributes to feeling generation through emotional evidence accumulation rate: Evidence from instructed fictional reappraisal.

Singer-Landau¹,

Meiran²

2021

Emotion

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How do (reportable) emotional feelings come to be? Following William James and many others, Givon et al. ( 2020) described the generation of feelings as evidence accumulation toward a boundary. In this work, we began clarifying the nature of "evidence". In two preregistered experiments, participants were presented with normed emotion-evoking negative/positive pictures that were described as reflecting either authentic or fictitious happenings ("fictional reappraisal"). In negative pictures (but contrary to our predictions, not in positive pictures), fictional reappraisal slowed feeling reports and reduced the rate of unpleasant feeling reports. An evidence accumulation model, the Hierarchical Linear Ballistic Accumulator model, was fit to the results from negative stimuli. This analysis indicated that fictional reappraisal selectively slowed the rate of evidence accumulation favoring (the normatively "correct") unpleasant feeling reports and speeded evidence accumulation favoring (the normatively "wrong") pleasant feeling reports. Fictional reappraisal did not change the response criterion, specifying the required amount of evidence for report. These results suggest that cognitive appraisals contribute to (and are a part of) emotional evidence, as operationalized in evidence accumulation models, and provide additional support for the usefulness of these models for the study of feeling reports.

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“…Hierarchical LBA modeling was performed with the “ggdmc” package (Lin & Strickland, 2020). This package computes Bayesian hierarchical modeling.…”

Section: Resultsmentioning

confidence: 99%

“…For this reason, we could not assess the influence of picture description on sd_v. 4 Hierarchical LBA modeling was performed with the "ggdmc" package (Lin & Strickland, 2020). This package computes Bayesian hierarchical modeling.…”

Section: Modelingmentioning

confidence: 99%

Cognitive appraisal contributes to feeling generation through emotional evidence accumulation rate: Evidence from instructed fictional reappraisal.

Singer-Landau¹,

Meiran²

2021

Emotion

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“…During recent years several other hierarchical Bayesian toolboxes have been developed with the specific aim of simplifying the process of analyzing data with cognitive computational models. Popular toolboxes in R include the hBayes package ( Ahn et al 2017 ) that allows users to analyze data with the DDM and several RL models and the ggdmc package that includes sequential sampling models ( Lin and Strickland 2020 ). The VBA toolbox in matlab also includes several RL models ( Daunizeau et al 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data

Pedersen

Frank

2020

Comput Brain Behav

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Cognitive models have been instrumental for generating insights into the brain processes underlying learning and decision making. In reinforcement learning it has recently been shown that not only choice proportions but also their latency distributions can be well captured when the choice function is replaced with a sequential sampling model such as the drift diffusion model. Hierarchical Bayesian parameter estimation further enhances the identifiability of distinct learning and choice parameters. One caveat is that these models can be time-consuming to build, sample from, and validate, especially when models include links between neural activations and model parameters. Here we describe a novel extension to the widely used hierarchical drift diffusion model (HDDM) toolbox, which facilitates flexible construction, estimation, and evaluation of the reinforcement learning drift diffusion model (RLDDM) using hierarchical Bayesian methods. We describe the types of experiments most applicable to the model and provide a tutorial to illustrate how to perform quantitative data analysis and model evaluation. Parameter recovery confirmed that the method can reliably estimate parameters with varying numbers of synthetic subjects and trials. We also show that the simultaneous estimation of learning and choice parameters can improve the sensitivity to detect brain-behavioral relationships, including the impact of learned values and fronto-basal ganglia activity patterns on dynamic decision parameters.

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“…Hierarchical models require fewer data per subject or experimental condition. Thus, when the number of observations per individual is limited, hierarchical models may appeal to potential users (for a tutorial to fit hierarchical Bayesian models, see Lin & Strickland, 2020 ). Research on the recommended number of trials for diffusion modeling analysis is ongoing (Lerche et al, 2017 ).…”

Section: Challenges and Recommendationsmentioning

confidence: 99%

Neurocomputational models of altruistic decision‐making and social motives: Advances, pitfalls, and future directions

Tusche

Bas

2021

WIRES Cognitive Science

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This article discusses insights from computational models and social neuroscience into motivations, precursors, and mechanisms of altruistic decisionmaking and other-regard. We introduce theoretical and methodological tools for researchers who wish to adopt a multilevel, computational approach to study behaviors that promote others' welfare. Using examples from recent studies, we outline multiple mental and neural processes relevant to altruism.To this end, we integrate evidence from neuroimaging, psychology, economics, and formalized mathematical models. We introduce basic mechanismspertinent to a broad range of value-based decisions-and social emotions and cognitions commonly recruited when our decisions involve other people.Regarding the latter, we discuss how decomposing distinct facets of social processes can advance altruistic models and the development of novel, targeted interventions. We propose that an accelerated synthesis of computational approaches and social neuroscience represents a critical step towards a more comprehensive understanding of altruistic decision-making. We discuss the utility of this approach to study lifespan differences in social preference in late adulthood, a crucial future direction in aging global populations. Finally, we review potential pitfalls and recommendations for researchers interested in applying a computational approach to their research.

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Evidence accumulation models with R: A practical guide to hierarchical Bayesian methods

Cited by 15 publications

References 46 publications

Cognitive appraisal contributes to feeling generation through emotional evidence accumulation rate: Evidence from instructed fictional reappraisal.

Cognitive appraisal contributes to feeling generation through emotional evidence accumulation rate: Evidence from instructed fictional reappraisal.

Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data

Neurocomputational models of altruistic decision‐making and social motives: Advances, pitfalls, and future directions

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