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Accounting for endogenous effects in decision‐making with a non‐linear diffusion decision model

  • The Drift‐Diffusion Model (DDM) is widely accepted for two‐alternative forced‐choice decision paradigms thanks to its simple formalism and close fit to behavioral and neurophysiological data. However, this formalism presents strong limitations in capturing inter‐trial dynamics at the single‐ trial level and endogenous influences. We propose a novel model, the non‐linear Drift‐Diffusion Model (nl‐DDM), that addresses these issues by allowing the existence of several trajectories to the decision boundary. We show that the non‐linear model performs better than the drift‐diffusion model for an equivalent complexity. To give better intuition on the meaning of nl‐DDM parameters, we compare the DDM and the nl‐DDM through correlation analysis. This paper provides evidence of the functioning of our model as an extension of the DDM. Moreover, we show that the nl‐DDM captures time effects better than the DDM. Our model paves the way toward more accurately analyzing across‐trial variability for perceptual decisions and accounts for peri‐stimulusThe Drift‐Diffusion Model (DDM) is widely accepted for two‐alternative forced‐choice decision paradigms thanks to its simple formalism and close fit to behavioral and neurophysiological data. However, this formalism presents strong limitations in capturing inter‐trial dynamics at the single‐ trial level and endogenous influences. We propose a novel model, the non‐linear Drift‐Diffusion Model (nl‐DDM), that addresses these issues by allowing the existence of several trajectories to the decision boundary. We show that the non‐linear model performs better than the drift‐diffusion model for an equivalent complexity. To give better intuition on the meaning of nl‐DDM parameters, we compare the DDM and the nl‐DDM through correlation analysis. This paper provides evidence of the functioning of our model as an extension of the DDM. Moreover, we show that the nl‐DDM captures time effects better than the DDM. Our model paves the way toward more accurately analyzing across‐trial variability for perceptual decisions and accounts for peri‐stimulus influences.show moreshow less

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Metadaten
Author: Isabelle HoxhaORCiD, Sylvain ChevallierORCiD, Matteo CiarchiORCiD, Stefan GlasauerORCiD, Arnaud DelormeORCiD, Michel‐Ange AmorimORCiD
DOI:https://doi.org/10.1038/s41598-023-32841-9
ISSN:2045-2322
Title of the source (English):Scientific Reports
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2023
Volume/Year:13
Article number:6323
Way of publication:Open Access
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Computational Neuroscience
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