A role for neural integrators in perceptual decision making
Abstract
Decisions based on uncertain information may benefit from an accumulation of information over time. We asked whether such an accumulation process may underlie decisions about the direction of motion in a random dot kinetogram. To address this question we developed a computational model of the decision process using ensembles of neurons whose spiking activity mimics neurons recorded in the extrastriate visual cortex (area MT or V5) and a sensorimotor association area of the parietal lobe (area LIP). The model instantiates the hypothesis that neurons in sensorimotor association areas compute the time integral of sensory signals from the visual cortex, construed as evidence for or against a proposition, and that the decision is made when the integrated evidence reaches a threshold. The model explains a variety of behavioral and physiological measurements obtained from monkeys.
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The process involves a race between a mechanism that accumulates evidence for right, and against left, and a process that integrates evidence for left, and against right.
“The process involves a race between a mechanism that accumulates evidence for right, and against left, and a process that integrates evidence for left, and against right (Usher and McClelland, 2001; Mazurek et al., 2003).”
From A common mechanism underlies changes of mind about decisions and confidence (2016) · cites this paper as Mazurek et al., 2003
A formal model of bounded accumulation accounts quantitatively for subjects' speed and accuracy in 2-choice tasks.
“First, a formal model of bounded accumulation accounts quantitatively for subjects’ speed and accuracy3,6.”
From Decision-making with multiple alternatives (2008) · cites this paper as [6]
A simple diffusion model provides a plausible account for the stimulation effects in both areas.
“A simple diffusion model provides a plausible account for the stimulation effects in both areas (Fig. 5).”
From Microstimulation of macaque area LIP affects decision-making in a motion discrimination task (2006) · cites this paper as [18]
Both the momentary evidence and the decision variable of the diffusion model can be related to neural responses.
“Both the momentary evidence and the decision variable of this model can be related to neural responses18.”
From Microstimulation of macaque area LIP affects decision-making in a motion discrimination task (2006) · cites this paper as [18]
A neural implementation of the diffusion model yields the opposite effect: shorter response times in error trials than in correct trials.
“By contrast, a neural implementation of the diffusion model yields the opposite effect (Mazurek et al., 2003).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Mazurek et al., 2003
The diffusion model assumes that a difference signal about the conflicting inputs is computed at the input level.
“The diffusion model assumes that a difference signal about the conflicting inputs is computed occurs at the input level (Ratcliff, 1978; Mazurek et al., 2003).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Mazurek et al., 2003
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“By contrast, a neural implementation of the diffusion model yields the opposite effect (Mazurek et al., 2003).”
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“7, insets), consistent with the hypothesis that the firing rates represent the accumulated difference in motion evidence from opposing pools of motion-sensing neurons, such as those in middle temporal area MT (Shadlen and Newsome, 1996, 2001; Mazurek et al., 2003; Huk and Shadlen, 2005).”
BackgroundSelf-citationCerebellar Neurodynamics Predict Decision Timing and Outcome on the Single-Trial Level
Lin, Q., Manley, J., Helmreich, M., Schlumm, F., Li, J. M., Robson, D. N., Engert, F., Schier, A., Nöbauer, T., & Vaziri, A. (2020) · Cell · doi:10.1016/j.cell.2019.12.018 · Cited by 122 · see citing works
Confidence Is the Bridge between Multi-stage Decisions
van den Berg, R., Zylberberg, A., Kiani, R., Shadlen, M. N., & Wolpert, D. M. (2016) · Current Biology · doi:10.1016/j.cub.2016.10.021 · Cited by 125 · see citing works
Functional dissection of circuitry in a neural integrator
Aksay, E., Olasagasti, I., Mensh, B. D., Baker, R., Goldman, M. S., & Tank, D. W. (2007) · Nature Neuroscience · doi:10.1038/nn1877 · Cited by 134 · see citing works
Motor effort alters changes of mind in sensorimotor decision making
Burk, D., Ingram, J. N., Franklin, D. W., Shadlen, M. N., & Wolpert, D. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0092681 · Cited by 117 · see citing works
Neural mechanisms of saccade target selection: gated accumulator model of the visual-motor cascade
Schall, J. D., Purcell, B. A., Heitz, R. P., Logan, G. D., & Palmeri, T. J. (2011) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2011.07715.x · Cited by 94 · see citing works
Evidence accumulation during a sensorimotor decision task revealed by whole-brain imaging
Dragomir, E. I., Štih, V., & Portugues, R. (2020) · Nature Neuroscience · doi:10.1038/s41593-019-0535-8 · Cited by 89 · see citing works
The hippocampus and exploration: dynamically evolving behavior and neural representations
Johnson, A., Varberg, Z., Benhardus, J., Maahs, A., & Schrater, P. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00216 · Cited by 61 · see citing works
Extended LATER model can account for trial-by-trial variability of both pre- and post-processes
Nakahara, H., Nakamura, K., & Hikosaka, O. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.07.001 · Cited by 45 · see citing works
From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents
Uddin, M. N., Shubham, K., Blanco, E., Baral, C., & Wang, G. (2026) · Findings of the Association for Computational Linguistics: ACL 2026 · doi:10.18653/v1/2026.findings-acl.1337 · Cited by 2 · see citing works
From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents
Uddin, M. N., Shubham, K., Blanco, E., Baral, C., & Wang, G. (2026) · arXiv · doi:10.48550/arxiv.2604.20006
“People naturally remember prior conversations (Brown-Schmidt et al., 2025), integrate information across time (Mazurek et al., 2003), revise beliefs when new evidence arises (Hogarth and Einhorn, 1992), and discard outdated knowledge (Bekinschtein Figure 1: The three tasks of the Memora benchmark:…”
- Authors
- Mazurek, M. E., Roitman, J. D., Ditterich, J., & Shadlen, M. N.
- Affiliations
- Howard Hughes Medical Institute (US)
- Corresponding author
- Mazurek, Mark E.
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-11-01
- Volume
- 13
- Issue
- 11
- Pages
- 1257-1269
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- OpenAlex
- W2168648043
- Semantic Scholar
- dc430a30bd045914a837bc2527ab57f5f90c6f35
- Cited by
- 620
- References
- 71
- Citations by year
- 2026: 11 · 2025: 19 · 2024: 14 · 2023: 17 · 2022: 25 · 2021: 25 · 2020: 20 · 2019: 29 · 2018: 24 · 2017: 24 · 2016: 36 · 2015: 35 · 2014: 36 · 2013: 31 · 2012: 50
- FWCI
- 8.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cerebral Cortex (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5t32gm007108-40)
- National Eye Institute (EY11378, R01EY011378)
- National Institute of General Medical Sciences (GM-07108)
- National Center for Research Resources (RR00166, P51RR000166)
- 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 › Motor Control and Adaptation
- Keywords
- perceptual decision making, neural integrator, random dot motion, direction discrimination, evidence accumulation, decision threshold, MT/V5, lateral intraparietal area, extrastriate cortex, parietal lobe
- MeSH terms
- Action Potentials, Animals, Haplorhini, Decision Making, Discrimination, Psychological, Perception, Visual Cortex, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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