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Pré-Publication, Document De Travail Année : 2022

nl-DDM: a non-linear drift-diffusion model accounting for the dynamics of single-trial perceptual decisions

Résumé

ABSTRACT 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-stimulus influences.

Dates et versions

hal-04297123 , version 1 (21-11-2023)

Identifiants

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Isabelle Hoxha, Sylvain Chevallier, Matteo Ciarchi, Stefan Glasauer, Arnaud Delorme, et al.. nl-DDM: a non-linear drift-diffusion model accounting for the dynamics of single-trial perceptual decisions. 2022. ⟨hal-04297123⟩
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