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Hierarchical drift diffusion modeling uncovers multisensory benefit in numerosity discrimination tasks
Author(s) -
Edwin Chau,
Carolyn A. Murray,
Ladan Shams
Publication year - 2021
Publication title -
peerj
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.927
H-Index - 70
ISSN - 2167-8359
DOI - 10.7717/peerj.12273
Subject(s) - numerosity adaptation effect , computer science , bayesian probability , diffusion , sample (material) , estimation theory , sample size determination , hierarchical database model , estimation , artificial intelligence , biological system , algorithm , statistics , data mining , mathematics , chemistry , physics , chromatography , neuroscience , perception , biology , thermodynamics , management , economics
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.

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