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

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.

SUBMITTER: Chau E

PROVIDER: S-EPMC8556708 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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

Chau Edwin E Murray Carolyn A CA Shams Ladan L

PeerJ 20211027

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 t ...[more]

PMID: 34760356

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