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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
The time course of perceptual choice: The leaky, competing accumulator model.
Usher, M., & McClelland, J. L. (2001) · Psychological Review · doi:10.1037/0033-295x.108.3.550 · Cited by 2,152
Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment.
Busemeyer, J. R., & Townsend, J. T. (1993) · Psychological Review · doi:10.1037/0033-295x.100.3.432 · Cited by 1,925
Neural Basis of a Perceptual Decision in the Parietal Cortex (Area LIP) of the Rhesus Monkey
Shadlen, M. N., & Newsome, W. T. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.4.1916 · Cited by 1,755
Roitman, J. D., & Shadlen, M. N. (2002) · The Journal of Neuroscience · doi:10.1523/jneurosci.22-21-09475.2002 · Cited by 1,589
Modeling Response Times for Two-Choice Decisions
Ratcliff, R., & Rouder, J. N. (1998) · Psychological Science · doi:10.1111/1467-9280.00067 · Cited by 1,478
A Comparison of Sequential Sampling Models for Two-Choice Reaction Time.
Ratcliff, R., & Smith, P. L. (2004) · Psychological Review · doi:10.1037/0033-295x.111.2.333 · Cited by 1,288
Probabilistic decision making by slow reverberation in cortical circuits
Wang, X.-J. (2002) · Neuron · doi:10.1016/s0896-6273(02)01092-9 · Cited by 1,242
Correlated neuronal discharge rate and its implications for psychophysical performance
Zohary, E., Shadlen, M. N., & Newsome, W. T. (1994) · Nature · doi:10.1038/370140a0 · Cited by 1,237
Attentional modulation of visual processing
Reynolds, J. H., & Chelazzi, L. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.26.041002.131039 · Cited by 1,158
A relationship between behavioral choice and the visual responses of neurons in macaque MT
Britten, K. H., Newsome, W. T., Shadlen, M. N., Celebrini, S., & Movshon, J. A. (1996) · Visual Neuroscience · doi:10.1017/s095252380000715x · Cited by 1,049
Cited by
Sorted by citations
Diffusion decision model: Current issues and history
Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.01.007 · Cited by 1,630
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
The speed-accuracy tradeoff: history, physiology, methodology, and behavior
Heitz, R. P. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00150 · Cited by 1,054
Decision-making with multiple alternatives
Churchland, A. K., Kiani, R., & Shadlen, M. N. (2008) · Nature Neuroscience · doi:10.1038/nn.2123 · Cited by 671
The Cost of Accumulating Evidence in Perceptual Decision Making
Drugowitsch, J., Moreno-Bote, R., Churchland, A. K., Shadlen, M. N., & Pouget, A. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.4010-11.2012 · Cited by 577
Rats and Humans Can Optimally Accumulate Evidence for Decision-Making
Brunton, B. W., Botvinick, M., & Brody, C. D. (2013) · Science · doi:10.1126/science.1233912 · Cited by 607
Decisions in Changing Conditions: The Urgency-Gating Model
Cisek, P., Puskas, G. A., & El-Murr, S. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.1844-09.2009 · Cited by 541
Transformation of stimulus value signals into motor commands during simple choice
Hare, T. A., Schultz, W., Camerer, C. F., O’Doherty, J. P., & Rangel, A. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1109322108 · Cited by 388
Neurally constrained modeling of perceptual decision making.
Purcell, B. A., Heitz, R. P., Cohen, J. Y., Schall, J. D., Logan, G. D., & Palmeri, T. J. (2010) · Psychological Review · doi:10.1037/a0020311 · Cited by 342
Stop Signals Provide Cross Inhibition in Collective Decision-Making by Honeybee Swarms
Seeley, T. D., Visscher, P. K., Schlegel, T., Hogan, P. M., Franks, N. R., & Marshall, J. A. R. (2011) · Science · doi:10.1126/science.1210361 · Cited by 335
Milosavljevic, M., Malmaud, J., Huth, A. G., Koch, C., & Rangel, A. (2010) · Judgment and Decision Making · doi:10.1017/s1930297500001285 · Cited by 333
Revisiting the Evidence for Collapsing Boundaries and Urgency Signals in Perceptual Decision-Making
Hawkins, G. E., Forstmann, B. U., Wagenmakers, E., Ratcliff, R., & Brown, S. D. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.2410-14.2015 · Cited by 298
Ding, L., & Gold, J. I. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr178 · Cited by 273
Decision making by urgency gating: theory and experimental support
Thura, D., Beauregard-Racine, J., Fradet, C.-W., & Cisek, P. (2012) · Journal of Neurophysiology · doi:10.1152/jn.01071.2011 · Cited by 279
Two views on the cognitive brain
Barack, D. L., & Krakauer, J. W. (2021) · Nature reviews. Neuroscience · doi:10.1038/s41583-021-00448-6 · Cited by 230
Ratcliff, R., & Frank, M. J. (2012) · Neural Computation · doi:10.1162/neco_a_00270 · Cited by 211
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
Speed, Accuracy, and the Optimal Timing of Choices
Fudenberg, D., Strack, P., & Strzalecki, T. (2018) · American Economic Review · doi:10.1257/aer.20150742 · Cited by 211
Rorie, A. E., Gao, J., McClelland, J. L., & Newsome, W. T. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009308 · Cited by 200
Gluth, S., Rieskamp, J., & Büchel, C. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.0727-12.2012 · Cited by 196
Ratcliff, R., & Starns, J. J. (2013) · Psychological Review · doi:10.1037/a0033152 · Cited by 173
Causal contribution and dynamical encoding in the striatum during evidence accumulation
Yartsev, M. M., Hanks, T. D., Yoon, A. M., & Brody, C. D. (2018) · eLife · doi:10.7554/elife.34929 · Cited by 154
Perceptual decision making: drift-diffusion model is equivalent to a Bayesian model
Bitzer, S., Park, H., Blankenburg, F., & Kiebel, S. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00102 · Cited by 168
One-dimensional dynamics of attention and decision making in LIP
Ganguli, S., Bisley, J. W., Roitman, J. D., Shadlen, M. N., Goldberg, M. E., & Miller, K. D. (2008) · Neuron · doi:10.1016/j.neuron.2008.01.038 · Cited by 152
The hippocampus supports deliberation during value-based decisions
Bakkour, A., Palombo, D. J., Zylberberg, A., Kang, Y. H., Reid, A. G., Verfaellie, M. H., Shadlen, M. N., & Shohamy, D. (2019) · eLife · doi:10.7554/elife.46080 · Cited by 156
Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy
Steinemann, N. A., O’Connell, R. G., & Kelly, S. P. (2018) · Nature Communications · doi:10.1038/s41467-018-06117-0 · Cited by 166
A flexible framework for simulating and fitting generalized drift-diffusion models
Shinn, M., Lam, N. H., & Murray, J. D. (2020) · eLife · doi:10.7554/elife.56938 · Cited by 133
Advancement of motion psychophysics: Review 2001-2010
Nishida, S. (2011) · Journal of Vision · doi:10.1167/11.5.11 · Cited by 144
Beyond Trial-Based Paradigms: Continuous Behavior, Ongoing Neural Activity, and Natural Stimuli
Huk, A. C., Bonnen, K., & He, B. J. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1920-17.2018 · Cited by 149
A Mechanism for Value-Sensitive Decision-Making
Pais, D., Hogan, P. M., Schlegel, T., Franks, N. R., Leonard, N. E., & Marshall, J. A. R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073216 · Cited by 136
The Attentional Drift Diffusion Model of Simple Perceptual Decision-Making
Tavares, G., Perona, P., & Rangel, A. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00468 · Cited by 151
Decision Threshold Modulation in the Human Brain
Domenech, P., & Dreher, J. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.2371-10.2010 · Cited by 131
Mammalian choices: combining fast-but-inaccurate and slow-but-accurate decision-making systems
Trimmer, P. C., Houston, A. I., Marshall, J. A. R., Bogacz, R., Paul, E. S., Mendl, M., & McNamara, J. M. (2008) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2008.0417 · Cited by 129
Mormann, M., Malmaud, J., Huth, A. G., Koch, C., & Rangel, A. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1901533 · Cited by 144
Functional dissection of signal and noise in MT and LIP during decision-making
Yates, J. L., Park, I. M., Katz, L. N., Pillow, J. W., & Huk, A. C. (2017) · Nature Neuroscience · doi:10.1038/nn.4611 · Cited by 120
Sel, K., Hawkins-Daarud, A. J., Chaudhuri, A., Osman, D., Bahai, A. R. S., Paydarfar, D., Willcox, K. E., Chung, C., & Jafari, R. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01447-y · Cited by 148
Modeling reaction time and accuracy of multiple-alternative decisions
Leite, F. P., & Ratcliff, R. (2009) · Attention Perception & Psychophysics · doi:10.3758/app.72.1.246 · Cited by 122
Ditterich, J. (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00184 · Cited by 104
Self-citationPerceptual Decisions between Multiple Directions of Visual Motion
Niwa, M., & Ditterich, J. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.5564-07.2008 · Cited by 103
Self-citationThe Speed and Accuracy of a Simple Perceptual Decision: A Mathematical Primer
Shadlen, M. N., Hanks, T. D., Churchland, A. K., Kiani, R., & Yang, T. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/9780262042383.003.0010 · Cited by 95
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
Differentiating between integration and non-integration strategies in perceptual decision making
Stine, G. M., Zylberberg, A., Ditterich, J., & Shadlen, M. N. (2020) · eLife · doi:10.7554/elife.55365 · Cited by 93
Self-citationComparing fixed and collapsing boundary versions of the diffusion model
Voskuilen, C., Ratcliff, R., & Smith, P. L. (2016) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2016.04.008 · Cited by 104
Coallier, É., Michelet, T., & Kalaska, J. F. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00166.2014 · Cited by 88
Neural dynamics of choice: Single-trial analysis of decision-related activity in parietal cortex
Bollimunta, A., Totten, D., & Ditterich, J. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.5752-11.2012 · Cited by 83
Self-citationA neural mechanism for terminating decisions
Stine, G. M., Trautmann, E. M., Jeurissen, D., & Shadlen, M. N. (2023) · Neuron · doi:10.1016/j.neuron.2023.05.028 · Cited by 83
Tillman, G., Zandt, T. V., & Logan, G. D. (2020) · Psychonomic Bulletin & Review · doi:10.3758/s13423-020-01719-6 · Cited by 91
Time-varying decision boundaries: insights from optimality analysis
Malhotra, G., Leslie, D. S., Ludwig, C. J. H., & Bogacz, R. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1340-6 · Cited by 86
Psychophysical reverse correlation reflects both sensory and decision-making processes
Okazawa, G., Sha, L., Purcell, B. A., & Kiani, R. (2018) · Nature Communications · doi:10.1038/s41467-018-05797-y · Cited by 84
On the neural implementation of the speed-accuracy trade-off
Standage, D., Blohm, G., & Dorris, M. C. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00236 · Cited by 78
The Limits of Deliberation in a Perceptual Decision Task
Zariwala, H. A., Kepecs, Á., Uchida, N., Hirokawa, J., & Mainen, Z. F. (2013) · Neuron · doi:10.1016/j.neuron.2013.02.010 · Cited by 79
Evidence for the speed–value trade-off: Human and monkey decision making is magnitude sensitive.
Pirrone, A., Azab, H., Hayden, B. Y., Stafford, T., & Marshall, J. A. R. (2017) · Decision · doi:10.1037/dec0000075 · Cited by 75
Neural Characterization of the Speed–Accuracy Tradeoff in a Perceptual Decision-Making Task
Wenzlaff, H., Bauer, M., Maeß, B., & Heekeren, H. R. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.4000-10.2011 · Cited by 83
Some task demands induce collapsing bounds: Evidence from a behavioral analysis
Palestro, J. J., Weichart, E. R., Sederberg, P. B., & Turner, B. M. (2018) · Psychonomic Bulletin & Review · doi:10.3758/s13423-018-1479-9 · Cited by 77
Neural Mechanisms That Make Perceptual Decisions Flexible
Okazawa, G., & Kiani, R. (2022) · Annual Review of Physiology · doi:10.1146/annurev-physiol-031722-024731 · Cited by 83
Modeling 2-alternative forced-choice tasks: Accounting for both magnitude and difference effects
Ratcliff, R., Voskuilen, C., & Teodorescu, A. R. (2018) · Cognitive Psychology · doi:10.1016/j.cogpsych.2018.02.002 · Cited by 90
Response Times and Decision‐Making
Donkin, C., & Brown, S. D. (2018) · Wiley eBooks · doi:10.1002/9781119170174.epcn509 · Cited by 75
New advances in understanding decisions among multiple alternatives
Churchland, A. K., & Ditterich, J. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.04.009 · Cited by 72
Self-citationStandage, D., You, H., Wang, D., & Dorris, M. C. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00007 · Cited by 68
When natural selection should optimize speed-accuracy trade-offs
Pirrone, A., Stafford, T., & Marshall, J. A. R. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00073 · Cited by 69
Temporal dynamics of decision-making during motion perception in the visual cortex
Grossberg, S., & Pilly, P. K. (2008) · Vision Research · doi:10.1016/j.visres.2008.02.019 · Cited by 67
Overcoming indecision by changing the decision boundary.
Malhotra, G., Leslie, D. S., Ludwig, C. J. H., & Bogacz, R. (2017) · Journal of Experimental Psychology General · doi:10.1037/xge0000286 · Cited by 70
Markkula, G. M., Boer, E. R., Romano, R., & Merat, N. (2018) · Biological Cybernetics · doi:10.1007/s00422-017-0743-9 · Cited by 80
Effect of Interactions between Harvester Ants on Forager Decisions
Davidson, J. D., Arauco-Aliaga, R. P., Crow, S. P., Gordon, D. M., & Goldman, M. S. (2016) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2016.00115 · Cited by 61
Maffei, G., Santos-Pata, D., Marcos, E., Sánchez-Fibla, M., & Verschure, P. F. M. J. (2015) · Neural Networks · doi:10.1016/j.neunet.2015.10.004 · Cited by 60
Hedge, C., Powell, G., Bompas, A., Vivian-Griffiths, S., & Sumner, P. (2018) · Psychological Bulletin · doi:10.1037/bul0000164 · Cited by 67
Internal and external sources of variability in perceptual decision-making.
Ratcliff, R., Voskuilen, C., & McKoon, G. (2017) · Psychological Review · doi:10.1037/rev0000080 · Cited by 61
Decision making on spatially continuous scales.
Ratcliff, R. (2018) · Psychological Review · doi:10.1037/rev0000117 · Cited by 60
Urgency, leakage, and the relative nature of information processing in decision-making.
Trueblood, J. S., Heathcote, A., Evans, N. J., & Holmes, W. R. (2020) · Psychological Review · doi:10.1037/rev0000255 · Cited by 57
Time course of free-choice priming effects explained by a simple accumulator model
Mattler, U., & Palmer, S. (2012) · Cognition · doi:10.1016/j.cognition.2012.03.002 · Cited by 57
Hedge, C., Vivian-Griffiths, S., Powell, G., Bompas, A., & Sumner, P. (2019) · Consciousness and Cognition · doi:10.1016/j.concog.2019.102797 · Cited by 60
Zoltowski, D. M., Latimer, K. W., Yates, J. L., Huk, A. C., & Pillow, J. W. (2019) · Neuron · doi:10.1016/j.neuron.2019.04.031 · Cited by 57
Bayesian Analysis of Interleaved Learning and Response Bias in Behavioral Experiments
Smith, A. C., Wirth, S., SUZUKI, W. A., & Brown, E. N. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00946.2006 · Cited by 55
Irrational time allocation in decision-making
Oud, B., Krajbich, I. M., Miller, K. J., Cheong, J. H., Botvinick, M., & Fehr, E. (2016) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2015.1439 · Cited by 51
Diffusion Modelling Reveals the Decision Making Processes Underlying Negative Judgement Bias in Rats
Hales, C. A., Robinson, E. S. J., & Houghton, C. J. (2016) · PLOS ONE · doi:10.1371/journal.pone.0152592 · Cited by 49
Local Computation of Decision-Relevant Net Sensory Evidence in Parietal Cortex
Bollimunta, A., & Ditterich, J. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr165 · Cited by 51
Self-citationNeurocomputational mechanisms of reinforcement-guided learning in humans: A review
Cohen, M. X. (2008) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/cabn.8.2.113 · Cited by 51
On diffusion processes with variable drift rates as models for decision making during learning
Eckhoff, P. A., Holmes, P. J., Law, C., Connolly, P. M., & Gold, J. I. (2008) · New Journal of Physics · doi:10.1088/1367-2630/10/1/015006 · Cited by 46
Robust versus optimal strategies for two-alternative forced choice tasks
Zacksenhouse, M., Bogacz, R., & Holmes, P. J. (2010) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2009.12.004 · Cited by 46
Averbeck, B. B., Djamshidian, A., O’Sullivan, S. S., Housden, C. R., Roiser, J. P., & Lees, A. J. (2013) · Behavioral Neuroscience · doi:10.1037/a0032079 · Cited by 48
Niyogi, R. K., & Wong-Lin, K. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003099 · Cited by 47
Ratcliff, R., Sederberg, P. B., Smith, T. A., & Childers, R. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.09.026 · Cited by 47
Trial-history biases in evidence accumulation can give rise to apparent lapses in decision-making
Gupta, D., DePasquale, B., Kopec, C. D., & Brody, C. D. (2024) · Nature Communications · doi:10.1038/s41467-024-44880-5 · Cited by 44
Discriminating evidence accumulation from urgency signals in speeded decision making
Hawkins, G. E., Wagenmakers, E., Ratcliff, R., & Brown, S. D. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00088.2015 · Cited by 45
Modeling numerosity representation with an integrated diffusion model.
Ratcliff, R., & McKoon, G. (2017) · Psychological Review · doi:10.1037/rev0000085 · Cited by 50
A parameter recovery assessment of time-variant models of decision-making
Evans, N. J., Trueblood, J. S., & Holmes, W. R. (2019) · Behavior Research Methods · doi:10.3758/s13428-019-01218-0 · Cited by 45
Striatal activation reflects urgency in perceptual decision making
van Maanen, L., Fontanesi, L., Hawkins, G. E., & Forstmann, B. U. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.045 · Cited by 36
Patients with Parkinson’s Disease Show Impaired Use of Priors in Conditions of Sensory Uncertainty
Perugini, A., Ditterich, J., & Basso, M. A. (2016) · Current Biology · doi:10.1016/j.cub.2016.05.039 · Cited by 44
Self-citationOptimal decision making in neural inhibition models.
van Ravenzwaaij, D., van der Maas, H. L. J., & Wagenmakers, E. (2011) · Psychological Review · doi:10.1037/a0026275 · Cited by 43
Evans, N. J., & Hawkins, G. E. (2018) · Cognition · doi:10.1016/j.cognition.2018.11.014 · Cited by 45
Diederich, A., & Oswald, P. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00697 · Cited by 36
Markkula, G., Boer, E. R., Romano, R., & Merat, N. (2018) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 38
Motor cortex disruption delays motor processes but not deliberation about action choices
Derosière, G., Thura, D., Cisek, P., & Duqué, J. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00163.2019 · Cited by 36
Smith, P. L., & Ratcliff, R. (2021) · Psychological Review · doi:10.1037/rev0000301 · Cited by 42
Reward Optimization in the Primate Brain: A Probabilistic Model of Decision Making under Uncertainty
Huang, Y., & Rao, R. P. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0053344 · Cited by 38
Detecting changes in dynamic and complex acoustic environments
Boubenec, Y., Lawlor, J., Górska, U., Shamma, S., & Englitz, B. (2017) · eLife · doi:10.7554/elife.24910 · Cited by 35
Content-specific evidence accumulation in inferior temporal cortex during perceptual decision-making
Tremel, J. J., & Wheeler, M. E. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.072 · Cited by 40
The Computation and Comparison of Value in Goal-directed Choice
Rangel, A. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374176-9.00028-2 · Cited by 35
Of monkeys and men: Impatience in perceptual decision-making
Boehm, U., Hawkins, G. E., Brown, S. D., van Rijn, H., & Wagenmakers, E. (2015) · Psychonomic Bulletin & Review · doi:10.3758/s13423-015-0958-5 · Cited by 35
Costa, V. D., & Averbeck, B. B. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht286 · Cited by 34
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
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 ›
Are you an author, or do you see a problem with this page? Request a review