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Journal Articles Scientific Reports Year : 2023

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

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.
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Dates and versions

hal-04290041 , version 1 (04-12-2023)

Identifiers

Cite

Isabelle Hoxha, Sylvain Chevallier, Matteo Ciarchi, Stefan Glasauer, Arnaud Delorme, et al.. Accounting for endogenous effects in decision-making with a non-linear diffusion decision model. Scientific Reports, 2023, 13 (1), pp.6323. ⟨10.1038/s41598-023-32841-9⟩. ⟨hal-04290041⟩
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