ArticleJournal of mathematical neuroscience2019
Drift-diffusion models for multiple-alternative forced-choice decision making.
Article in Journal of mathematical neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Spiking neural models for decision-making tasks with learning.Journal of mathematical biology · 2026Article
- Speed and quality of complex strategic decisions.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Integrated Ising model with global inhibition for decision-making.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Noise correlations and neuronal diversity may limit the utility of winner-take-all readout in a pop out visual search task.PLoS computational biology · 2025Article
- Integrated Ising Model with global inhibition for decision making.bioRxiv : the preprint server for biology · 2024Article
- Informational Entropy Threshold as a Physical Mechanism for Explaining Tree-like Decision Making in Humans.Entropy (Basel, Switzerland) · 2022Article
- Multi-sensory feedback improves spatially compatible sensori-motor responses.Scientific reports · 2022Article
- Evolutionary shaping of human brain dynamics.eLife · 2022Article
- Signal neutrality, scalar property, and collapsing boundaries as consequences of a learned multi-timescale strategy.PLoS computational biology · 2022Article
- Temporal order judgment of multisensory stimuli in rat and human.Frontiers in behavioral neuroscience · 2022Article
- Two sources of uncertainty independently modulate temporal expectancy.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Flexible categorization in perceptual decision making.Nature communications · 2021Article
- Robust parallel decision-making in neural circuits with nonlinear inhibition.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
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Abstract
The canonical computational model for the cognitive process underlying two-alternative forced-choice decision making is the so-called drift-diffusion model (DDM). In this model, a decision variable keeps track of the integrated difference in sensory evidence for two competing alternatives. Here I extend the notion of a drift-diffusion process to multiple alternatives. The competition between n alternatives takes place in a linear subspace of [Formula: see text] dimensions; that is, there are [Formula: see text] decision variables, which are coupled through correlated noise sources. I derive the multiple-alternative DDM starting from a system of coupled, linear firing rate equations. I also show that a Bayesian sequential probability ratio test for multiple alternatives is, in fact, equivalent to these same linear DDMs, but with time-varying thresholds. If the original neuronal system is nonlinear, one can once again derive a model describing a lower-dimensional diffusion process. The dynamics of the nonlinear DDM can be recast as the motion of a particle on a potential, the general form of which is given analytically for an arbitrary number of alternatives.
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