The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Stochastic models of decisions about motion direction: Behavior and physiology
Abstract
Roitman and Shadlen [Roitman J. D., & Shadlen M. N. (2002). Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. Journal of Neuroscience, 22, 9475-9489] have published a non-human primate study on visual decision making. They collected both behavioral and neurophysiological data and provided evidence that the data are qualitatively consistent with a mechanism based on accumulating sensory evidence up to a decision threshold. I have previously demonstrated that a time-variant diffusion model can account quite well quantitatively for both the behavioral and the neural data. In this manuscript I discuss how well the data constrains different components and parameters of the computational process. I also discuss the biological plausibility of the model parameters. I will demonstrate that a relatively large class of models, both with and without temporal integration and both stationary and time-variant could account for the behavioral data. Both the single cell recordings from the parietal cortex and previously published data from the extrastriate visual cortex provide additional constraints. Overall, the data favor a diffusion model with time-variant gain and leaky integrators. The integration time constant, however, turns out not to be well-constrained by the data.
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“The model with urgency explains the longer RTs on error trials 36 (Supplementary Fig. 7).”
From Decision-making with multiple alternatives (2008) · cites this paper as [36]
In the citing paper
“Because chance represents 50% correct, there is less impetus to complete the trial at weak motion strengths, which probably explains the weaker urgency signal, u(t), seen previously in 2-choice experiments2,36.”
From Decision-making with multiple alternatives (2008) · cites this paper as [36]
In the citing paper
“According to the model, the longer RT on errors arises as a consequence of the urgency signal, u(t), which effectively imposes a more lax criterion for terminating the decision (as if the bound were dropping as a function of time), thereby rendering errors more frequent36.”
From Decision-making with multiple alternatives (2008) · cites this paper as [36]
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Metadata
- Authors
- Ditterich, J.
- Affiliations
- University of California, Davis (US)
- Corresponding author
- Ditterich, Jochen
- ORCID
- Ditterich, Jochen
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2006-10
- Volume
- 19
- Issue
- 8
- Pages
- 981-1012
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2002824768
- Cited by
- 233
- References
- 44
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 13 · 2023: 16 · 2022: 13 · 2021: 11 · 2020: 16 · 2019: 9 · 2018: 18 · 2017: 15 · 2016: 13 · 2015: 15 · 2014: 15 · 2013: 11 · 2012: 12
- FWCI
- 3.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Networks (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- motion direction discrimination, visual decision making, diffusion models, decision threshold, sensory evidence accumulation, lateral intraparietal area, parietal cortex, extrastriate cortex, leaky integrator, computational neuroscience, single-unit recording
- MeSH terms
- Action Potentials, Animals, Behavior, Behavior, Animal, Decision Making, Discrimination, Psychological, Humans, Models, Biological, Models, Neurological, Motion, Motion Perception, Neurons, Reaction Time, Stochastic Processes, Time Factors, Visual Cortex
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