The time course of perceptual choice: The leaky, competing accumulator model.

Usher, Marius; McClelland, James L.

doi:10.1037/0033-295x.108.3.550

Cited by 2,152Neural and Behavioral Psychology StudiesDecision-Making and Behavioral EconomicsVisual perception and processing mechanisms

The time course of perceptual choice: The leaky, competing accumulator model.

Usher, Marius, McClelland, James L.·2001·Psychological Review

DOI 10.1037/0033-295x.108.3.550

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Abstract

The time course of perceptual choice is discussed in a model of gradual, leaky, stochastic, and competitive information accumulation in nonlinear decision units. Special cases of the model match a classical diffusion process, but leakage and competition work together to address several challenges to existing diffusion, random walk, and accumulator models. The model accounts for data from choice tasks using both time-controlled (e.g., response signal) and standard reaction time paradigms and its adequacy compares favorably with other approaches. A new paradigm that controls the time of arrival of information supporting different choice alternatives provides further support. The model captures choice behavior regardless of the number of alternatives, accounting for the log-linear relation between reaction time and number of alternatives (Hick's law) and explains a complex pattern of visual and contextual priming in visual word identification.

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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 Usher and McClelland, 2001
The hypothesis that the bounded accumulation framework applies to decisions among 4 choices was suggested by theorists but had not been tested.
“This hypothesis was suggested by theorists5,28,29 but had not been tested.”
From Decision-making with multiple alternatives (2008) · cites this paper as [28]
In human studies, mean reaction times typically range from tens of milliseconds to about a second in simple perceptual tasks.
“In human studies, mean RTs typically range from tens of milliseconds to about a second in simple perceptual tasks (Luce, 1986; Usher and McClelland, 2001).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Usher and McClelland, 2001

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Conflict monitoring and cognitive control
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The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks
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Decision making, the P3, and the locus coeruleus–norepinephrine system
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“Some investigations have supported nonstationarity but have not generated the critical predictions for correct and error RT distributions ([52,87,111–113], see also [45,86,114]).”
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Visual fixations and the computation and comparison of value in simple choice
Krajbich, I., Armel, C., & Rangel, A. (2010) · Nature Neuroscience · doi:10.1038/nn.2635 · Cited by 1,408 · see citing works
Spectral fingerprints of large-scale neuronal interactions
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Probabilistic decision making by slow reverberation in cortical circuits
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Neuronal Reward and Decision Signals: From Theories to Data
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A Recurrent Network Mechanism of Time Integration in Perceptual Decisions
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Psychology and neurobiology of simple decisions
Smith, P. L., & Ratcliff, R. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.01.006 · Cited by 1,051 · see citing works
Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes
Badre, D. (2008) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2008.02.004 · Cited by 980 · see citing works
Expectation in perceptual decision making: neural and computational mechanisms
Summerfield, C., & de Lange, F. P. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3838 · Cited by 832 · see citing works
A context maintenance and retrieval model of organizational processes in free recall
Polyn, S. M., Norman, K. A., & Kahana, M. J. (2009) · Psychological Review · doi:10.1037/a0014420 · Cited by 726 · see citing works
Hold your horses: a dynamic computational role for the subthalamic nucleus in decision making
Frank, M. J. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.03.006 · Cited by 706 · see citing works
Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.
Pleskac, T. J., & Busemeyer, J. R. (2010) · Psychological Review · doi:10.1037/a0019737 · Cited by 722 · see citing works
Probabilistic population codes for Bayesian decision making
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Changes of mind in decision-making
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The effect of stimulus strength on the speed and accuracy of a perceptual decision
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Decision Making in Recurrent Neuronal Circuits
Wang, X.-J. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.034 · Cited by 679 · see citing works
“For the two-alternative tasks, the LCA model is reduced to the diffusion model in the special case when the leak and inhibition cancel out each other (Usher and McClelland, 2001).”
BackgroundMethodInfluential
Decision-making with multiple alternatives
Churchland, A. K., Kiani, R., & Shadlen, M. N. (2008) · Nature Neuroscience · doi:10.1038/nn.2123 · Cited by 671 · see citing works
Chapter 2 Design for a Working Memory
Oberauer, K. (2009) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(09)51002-x · Cited by 636 · see citing works
A role for neural integrators in perceptual decision making
Mazurek, M. E., Roitman, J. D., Ditterich, J., & Shadlen, M. N. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg097 · Cited by 620 · see citing works
Focusing Attention on the Health Aspects of Foods Changes Value Signals in vmPFC and Improves Dietary Choice
Hare, T. A., Malmaud, J., & Rangel, A. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.6383-10.2011 · Cited by 556 · see citing works
Decision theory, reinforcement learning, and the brain
Dayan, P., & Daw, N. D. (2008) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/cabn.8.4.429 · Cited by 580 · see citing works
Neural computations associated with goal-directed choice
Rangel, A., & Hare, T. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.03.001 · Cited by 532 · see citing works
Neural Activity in Macaque Parietal Cortex Reflects Temporal Integration of Visual Motion Signals during Perceptual Decision Making
Huk, A. C., & Shadlen, M. N. (2005) · The Journal of Neuroscience · doi:10.1523/jneurosci.4684-04.2005 · Cited by 538 · see citing works
Flexible control of mutual inhibition: a neural model of two-interval discrimination
Machens, C. K., Romo, R., & Brody, C. D. (2005) · Science · doi:10.1126/science.1104171 · Cited by 535 · see citing works
Neural basis of deciding, choosing and acting
Schall, J. D. (2001) · Nature reviews. Neuroscience · doi:10.1038/35049054 · Cited by 528 · see citing works
Inhibitory control in mind and brain: An interactive race model of countermanding saccades.
Boucher, L., Palmeri, T. J., Logan, G. D., & Schall, J. D. (2007) · Psychological Review · doi:10.1037/0033-295x.114.2.376 · Cited by 485 · see citing works
Bounded Integration in Parietal Cortex Underlies Decisions Even When Viewing Duration Is Dictated by the Environment
Kiani, R., Hanks, T. D., & Shadlen, M. N. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4761-07.2008 · Cited by 500 · see citing works
Cortico–basal ganglia circuit mechanism for a decision threshold in reaction time tasks
Lo, C.-C., & Wang, X.-J. (2006) · Nature Neuroscience · doi:10.1038/nn1722 · Cited by 505 · see citing works
Choice Certainty Is Informed by Both Evidence and Decision Time
Kiani, R., Corthell, L., & Shadlen, M. N. (2014) · Neuron · doi:10.1016/j.neuron.2014.12.015 · Cited by 470 · see citing works
How the brain integrates costs and benefits during decision making
Basten, U., Biele, G., Heekeren, H. R., & Fiebach, C. J. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0908104107 · Cited by 466 · see citing works
Mechanisms underlying cortical activity during value-guided choice
Hunt, L. T., Kolling, N., Soltani, A., Woolrich, M. W., Rushworth, M. F. S., & Behrens, T. E. J. (2012) · Nature Neuroscience · doi:10.1038/nn.3017 · Cited by 454 · see citing works
Different time courses for visual perception and action priming
Vorberg, D., Mattler, U., Heinecke, A., Schmidt, T., & Schwarzbach, J. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0931489100 · Cited by 450 · see citing works
Bayesian just-so stories in psychology and neuroscience.
Bowers, J. S., & Davis, C. J. (2012) · Psychological Bulletin · doi:10.1037/a0026450 · Cited by 436 · see citing works
Variance as a signature of neural computations during decision making
Churchland, A. K., Kiani, R., Chaudhuri, R., Wang, X.-J., Pouget, A., & Shadlen, M. N. (2011) · Neuron · doi:10.1016/j.neuron.2010.12.037 · Cited by 350 · see citing works
“Lastly, we consider a recurrent neural network that is exemplary of a class of dynamical ‘‘attractor’’ models of decision making (e.g., Albantakis and Deco, 2009; Bogacz et al., 2006; Machens et al., 2005; Usher and McClelland, 2001; Wang, 2002).”
Method
Integrated neural processes for defining potential actions and deciding between them: a computational model
Cisek, P. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.5605-05.2006 · Cited by 344 · see citing works
Neural computations underlying action-based decision making in the human brain
Wunderlich, K., Rangel, A., & O'Doherty, J. P. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0901077106 · Cited by 301 · see citing works
Dynamics of neuronal responses in macaque MT and VIP during motion detection
Cook, E. P., & Maunsell, J. H. R. (2002) · Nature Neuroscience · doi:10.1038/nn924 · Cited by 289 · see citing works
Eye tracking and pupillometry are indicators of dissociable latent decision processes.
Cavanagh, J. F., Wiecki, T. V., Kochar, A., & Frank, M. J. (2014) · Journal of Experimental Psychology: General · doi:10.1037/a0035813 · Cited by 289 · see citing works
A neurobiological theory of automaticity in perceptual categorization
Ashby, F. G., Ennis, J. M., & Spiering, B. J. (2007) · Psychological Review · doi:10.1037/0033-295x.114.3.632 · Cited by 286 · see citing works
Evidence accumulation and the moment of recognition: dissociating perceptual recognition processes using fMRI
Ploran, E. J., Nelson, S. M., Velanova, K., Donaldson, D. I., Petersen, S. E., & Wheeler, M. E. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.3522-07.2007 · Cited by 282 · see citing works
“Accumulator models (Audley and Pike, 1965; Link and Heath, 1975; Ratcliff, 1978; Ratcliff and McKoon, 1982; Smith and Vickers, 1988; Usher and McClelland, 2001) describe evidence gathering as a gradual process in which evidence supporting different choices accrues over time.”
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A common mechanism underlies changes of mind about decisions and confidence
van den Berg, R., Anandalingam, K., Zylberberg, A., Kiani, R., Shadlen, M. N., & Wolpert, D. M. (2016) · eLife · doi:10.7554/elife.12192 · Cited by 270 · see citing works
Bayesian Computation in Recurrent Neural Circuits
Rao, R. P. N. (2004) · Neural Computation · doi:10.1162/08997660460733976 · Cited by 252 · see citing works
Executive control in the Simon effect: an electromyographic and distributional analysis
Burle, B., Possamaï, C.-A., Vidal, F., Bonnet, M., & Hasbroucq, T. (2002) · Psychological Research · doi:10.1007/s00426-002-0105-6 · Cited by 249 · see citing works
Stochastic models of decisions about motion direction: Behavior and physiology
Ditterich, J. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.05.042 · Cited by 233 · see citing works
Dual Diffusion Model for Single-Cell Recording Data From the Superior Colliculus in a Brightness-Discrimination Task
Ratcliff, R., Hasegawa, Y. T., Hasegawa, R. P., Smith, P. L., & Segraves, M. A. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00393.2006 · Cited by 236 · see citing works
Microstimulation of visual cortex affects the speed of perceptual decisions
Ditterich, J., Mazurek, M. E., & Shadlen, M. N. (2003) · Nature Neuroscience · doi:10.1038/nn1094 · Cited by 234 · see citing works
A ballistic model of choice response time
Brown, S., & Heathcote, A. (2005) · Psychological Review · doi:10.1037/0033-295x.112.1.117 · Cited by 228 · see citing works
A neural mechanism of speed-accuracy tradeoff in macaque area LIP
Hanks, T., Kiani, R., & Shadlen, M. N. (2014) · eLife · doi:10.7554/elife.02260 · Cited by 222 · see citing works
Accuracy, Information, and Response Time in a Saccadic Decision Task
Reddi, B. A. J., Asrress, K. N., & Carpenter, R. H. S. (2003) · Journal of Neurophysiology · doi:10.1152/jn.00689.2002 · Cited by 217 · see citing works
Banishing the Control Homunculi in Studies of Action Control and Behavior Change
Verbruggen, F., McLaren, I. P. L., & Chambers, C. D. (2014) · Perspectives on Psychological Science · doi:10.1177/1745691614526414 · Cited by 214 · see citing works
Embodied choice: how action influences perceptual decision making
Lepora, N. F., & Pezzulo, G. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004110 · Cited by 209 · see citing works
Neural circuit dynamics underlying accumulation of time-varying evidence during perceptual decision-making
Wong, K.-F. (2007) · Frontiers in Computational Neuroscience · doi:10.3389/neuro.10.006.2007 · Cited by 204 · see citing works
Memory search and the neural representation of context
Polyn, S. M., & Kahana, M. J. (2008) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.10.010 · Cited by 202 · see citing works
Brain mechanisms for perceptual and reward-related decision-making
Deco, G., Rolls, E. T., Albantakis, L., & Romo, R. (2013) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2012.01.010 · Cited by 196 · see citing works
Twenty-Five Lessons from Computational Neuromodulation
Dayan, P. (2012) · Neuron · doi:10.1016/j.neuron.2012.09.027 · Cited by 190 · see citing works
A mechanism for value-guided choice based on the excitation-inhibition balance in prefrontal cortex
Jocham, G., Hunt, L. T., Near, J., & Behrens, T. E. J. (2012) · Nature Neuroscience · doi:10.1038/nn.3140 · Cited by 183 · see citing works
Quantum Cognition
Pothos, E. M., & Busemeyer, J. R. (2022) · Annual Review of Psychology · doi:10.1146/annurev-psych-033020-123501 · Cited by 184 · see citing works
From salience to saccades: multiple-alternative gated stochastic accumulator model of visual search
Purcell, B. A., Schall, J. D., Logan, G. D., & Palmeri, T. J. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.4622-11.2012 · Cited by 179 · see citing works
Building Bridges between Perceptual and Economic Decision-Making: Neural and Computational Mechanisms
Summerfield, C., & Tsetsos, K. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00070 · Cited by 167 · see citing works
The dynamics of choice among multiple alternatives
McMillen, T., & Holmes, P. (2006) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2005.10.003 · Cited by 153 · see citing works
Choice, difficulty, and confidence in the brain
Rolls, E. T., Grabenhorst, F., & Deco, G. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.073 · Cited by 148 · see citing works
Bayesian modelling of Jumping-to-Conclusions bias in delusional patients
Moutoussis, M., Bentall, R. P., El-Deredy, W., & Dayan, P. (2011) · Cognitive Neuropsychiatry · doi:10.1080/13546805.2010.548678 · Cited by 137 · see citing works
Emergence in Cognitive Science
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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
Human memory reconsolidation can be explained using the temporal context model
Sederberg, P. B., Gershman, S. J., Polyn, S. M., & Norman, K. A. (2011) · Psychonomic Bulletin & Review · doi:10.3758/s13423-011-0086-9 · Cited by 125 · see citing works
Basic impairments in regulating the speed-accuracy tradeoff predict symptoms of attention-deficit/hyperactivity disorder
Mulder, M. J., Bos, D., Weusten, J. M. H., van Belle, J., van Dijk, S. C., Simen, P., van Engeland, H., & Durston, S. (2010) · Biological Psychiatry · doi:10.1016/j.biopsych.2010.07.031 · Cited by 131 · see citing works
Modelling ADHD: A review of ADHD theories through their predictions for computational models of decision-making and reinforcement learning
Ziegler, S., Pedersen, M. L., Mowinckel, A. M., & Biele, G. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.09.002 · Cited by 110 · see citing works
The successor representation and temporal context
Gershman, S. J., Moore, C. D., Todd, M. T., Norman, K. A., & Sederberg, P. B. (2012) · Neural Computation · doi:10.1162/neco_a_00282 · Cited by 117 · see citing works
Riding the Lexical Speedway: A Critical Review on the Time Course of Lexical Selection in Speech Production
Strijkers, K., & Costa, A. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00356 · Cited by 106 · see citing works
Confidence-Related Decision Making
Insabato, A., Pannunzi, M., Rolls, E. T., & Deco, G. (2010) · Journal of Neurophysiology · doi:10.1152/jn.01068.2009 · Cited by 95 · 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
Understanding neural signals of post-decisional performance monitoring: An integrative review
Desender, K., Ridderinkhof, K. R., & Murphy, P. R. (2021) · eLife · doi:10.7554/elife.67556 · Cited by 77 · 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
Modeling individual differences in response time and accuracy in numeracy
Ratcliff, R., Thompson, C. A., & McKoon, G. (2015) · Cognition · doi:10.1016/j.cognition.2014.12.004 · Cited by 77 · see citing works
Binding by random bursts: a computational model of cognitive control
Verguts, T. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01117 · Cited by 76 · see citing works
Gamelike features might not improve data
Hawkins, G. E., Rae, B., Nesbitt, K. V., & Brown, S. D. (2013) · Behavior Research Methods · doi:10.3758/s13428-012-0264-3 · Cited by 61 · see citing works
Transformation of perception from sensory to motor cortex
Fassihi, A., Akrami, A., Pulecchi, F., Schönfelder, V., & Diamond, M. E. (2017) · Current Biology · doi:10.1016/j.cub.2017.05.011 · Cited by 64 · see citing works
“When a judgement must be made about a single noisy stimulus, evidence can be accumulated over time [21, 22].”
Task-general efficiency of evidence accumulation as a computationally defined neurocognitive trait: implications for clinical neuroscience
Weigard, A., & Sripada, C. (2021) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2021.02.001 · Cited by 43 · see citing works
Extended LATER model can account for trial-by-trial variability of both pre- and post-processes
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Authors: Usher, M., & McClelland, J. L.
Affiliations: Birkbeck, University of London (GB)
ORCID: Usher, Marius
McClelland, James L.
Venue: Psychological Review
Publisher: American Psychological Association (APA)
Published: 2001
Volume: 108
Issue: 3
Pages: 550-592
ISSN: 0033-295X (ISSN-L)
1939-1471
DOI: 10.1037/0033-295x.108.3.550
OpenAlex: W2136582516
Cited by: 2,152
References: 140
Citations by year: 2026: 70 · 2025: 75 · 2024: 83 · 2023: 121 · 2022: 98 · 2021: 119 · 2020: 122 · 2019: 124 · 2018: 118 · 2017: 104 · 2016: 105 · 2015: 128 · 2014: 117 · 2013: 104 · 2012: 111
FWCI: 14.67
Type: journal article
Language: en
Open access: closed
Locations: Psychological Review (publishedVersion)
PubMed (publishedVersion)
Research Explorer (The University of Manchester) (publishedVersion)
Funders: National Institute of Mental Health (MH00385, MH-47566, P01MH047566)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Keywords: perceptual choice, decision-making, diffusion models, reaction time, Hick's law, number of alternatives, random walks, visual word recognition, visual priming, contextual priming
MeSH terms: Choice Behavior, Humans, Models, Psychological, Neural Inhibition, Perception, Stochastic Processes, Time Factors, Visual Perception, Nonlinear Dynamics

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The time course of perceptual choice: The leaky, competing accumulator model.

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