Hierarchical drift diffusion modeling uncovers multisensory benefit in numerosity discrimination tasks.

Edwin Chau, Carolyn A Murray, Ladan Shams

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Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 authors.

Edwin ChauDepartment of Mathematics, University of California, Los Angeles, Los Angeles, California, USA.

Carolyn A MurrayDepartment of Psychology, University of California, Los Angeles, Los Angeles, California, USA.

Ladan ShamsDepartment of Psychology, BioEngineering, and Interdepartmental Neuroscience Program, University of California, Los Angeles, Los Angeles, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Studies of accuracy and reaction time in decision making often observe a speed-accuracy tradeoff, where either accuracy or reaction time is sacrificed for the other. While this effect may mask certain multisensory benefits in performance when accuracy and reaction time are separately measured, drift diffusion models (DDMs) are able to consider both simultaneously. However, drift diffusion models are often limited by large sample size requirements for reliable parameter estimation. One solution to this restriction is the use of hierarchical Bayesian estimation for DDM parameters. Here, we utilize hierarchical drift diffusion models (HDDMs) to reveal a multisensory advantage in auditory-visual numerosity discrimination tasks. By fitting this model with a modestly sized dataset, we also demonstrate that large sample sizes are not necessary for reliable parameter estimation.

Indexed as

Auditory-visual interactionBayesian drift diffusionCross-modal interactionDrift diffusionDrift diffusion modelFlash illusionHierarchical drift diffusionMultisensory benefitMultisensory perceptionNumerosity judgement

Identifiers

PMID34760356

DOI10.7717/peerj.12273

PMCPMC8556708

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.