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Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability
Open accessAbstract
Behavior varies from trial to trial even when the stimulus is maintained as constant as possible. In many models, this variability is attributed to noise in the brain. Here, we propose that there is another major source of variability: suboptimal inference. Importantly, we argue that in most tasks of interest, and particularly complex ones, suboptimal inference is likely to be the dominant component of behavioral variability. This perspective explains a variety of intriguing observations, including why variability appears to be larger on the sensory than on the motor side, and why our sensors are sometimes surprisingly unreliable.
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“The representational capacity required exceeds what could plausibly be implemented in neural tissue, particularly for continuous variables with theoretically infinite possible states (Beck et al. 2012; Fiser et al. 2010).”
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Citations
Cites
Probability, Random Variables, and Stochastic Processes.
Abrahams, J., & Papoulis, A. (1984) · Journal of the American Statistical Association · doi:10.2307/2288754 · Cited by 16,301
Faisal, A. A., Selen, L. P. J., & Wolpert, D. M. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2258 · Cited by 2,883
Shadlen, M. N., & Newsome, W. T. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-10-03870.1998 · Cited by 2,178
The computational complexity of probabilistic inference using bayesian belief networks
Cooper, G. F. (1990) · Artificial Intelligence · doi:10.1016/0004-3702(90)90060-d · Cited by 1,979
Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity
van Vreeswijk, C., & Sompolinsky, H. (1996) · Science · doi:10.1126/science.274.5293.1724 · Cited by 1,831
Bayesian inference with probabilistic population codes
Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006) · Nature Neuroscience · doi:10.1038/nn1790 · Cited by 1,457
Self-citationThe Normalization Model of Attention
Reynolds, J. H., & Heeger, D. J. (2009) · Neuron · doi:10.1016/j.neuron.2009.01.002 · Cited by 1,457
Forward models: supervised learning with a distal teacher
Jordan, M. I., & Rumelhart, D. E. (1992) · Cognitive Science · doi:10.1207/s15516709cog1603_1 · Cited by 1,451
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
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Sorted by citations
Lieder, F., & Griffiths, T. L. (2020) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1900061x · Cited by 892
Robust speech perception: Recognize the familiar, generalize to the similar, and adapt to the novel.
Kleinschmidt, D., & Jaeger, T. F. (2015) · Psychological Review · doi:10.1037/a0038695 · Cited by 684
Probabilistic brains: knowns and unknowns
Pouget, A., Beck, J. M., Ma, W. J., & Latham, P. E. (2013) · Nature Neuroscience · doi:10.1038/nn.3495 · Cited by 615
“If this is not the case, then the brain must either be adding noise or making approximations (or both 92 ), and the problem is to determine whether approximations are being used and, if so, what they are.”
BackgroundSelf-citationHumans use directed and random exploration to solve the explore–exploit dilemma.
Wilson, R. C., Geana, A., White, J. M., Ludvig, E. A., & Cohen, J. D. (2014) · Journal of Experimental Psychology: General · doi:10.1037/a0038199 · Cited by 580
Information-limiting correlations
Moreno‐Bote, R., Beck, J. M., Kanitscheider, I., Pitkow, X., Latham, P. E., & Pouget, A. (2014) · Nature Neuroscience · doi:10.1038/nn.3807 · Cited by 565
Self-citationConfidence and certainty: Distinct probabilistic quantities for different goals
Pouget, A., Drugowitsch, J., & Kepecs, A. (2016) · Nature Neuroscience · doi:10.1038/nn.4240 · Cited by 528
Self-citationThe Role of Variability in Motor Learning
Dhawale, A. K., Smith, M. A., & Ölveczky, B. P. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031548 · Cited by 545
Correlations and Neuronal Population Information
Kohn, A., Coen-Cagli, R., Kanitscheider, I., & Pouget, A. (2016) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-070815-013851 · Cited by 372
Self-citationSuboptimality in perceptual decision making
Rahnev, D., & Denison, R. N. (2018) · Behavioral and Brain Sciences · doi:10.1017/s0140525x18000936 · Cited by 342
Knowing how much you don't know: a neural organization of uncertainty estimates
Bach, D. R., & Dolan, R. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3289 · Cited by 339
Dynamic modulation of decision biases by brainstem arousal systems
de Gee, J. W., Colizoli, O., Kloosterman, N. A., Knapen, T. H. J., Nieuwenhuis, S., & Donner, T. H. (2017) · eLife · doi:10.7554/elife.23232 · Cited by 312
Neural Coding of Uncertainty and Probability
Ma, W. J., & Jazayeri, M. (2014) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071013-014017 · Cited by 290
Self-citationBehavioral Variability through Stochastic Choice and Its Gating by Anterior Cingulate Cortex
Tervo, D. G. R., Proskurin, M., Manakov, M., Kabra, M., Vollmer, A., Branson, K. M., & Karpova, A. Y. (2014) · Cell · doi:10.1016/j.cell.2014.08.037 · Cited by 283
The structures and functions of correlations in neural population codes
Panzeri, S., Moroni, M., Safaai, H., & Harvey, C. D. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00606-4 · Cited by 260
Variability in neural activity and behavior
Renart, A., & Machens, C. K. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.02.013 · Cited by 237
Perceptual Decision-Making as Probabilistic Inference by Neural Sampling
Haefner, R. M., Berkes, P., & Fiser, J. (2016) · Neuron · doi:10.1016/j.neuron.2016.03.020 · Cited by 207
Computational noise in reward-guided learning drives behavioral variability in volatile environments
Findling, C., Skvortsova, V., Dromnelle, R., Palminteri, S., & Wyart, V. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0518-9 · Cited by 205
Learning what to expect (in visual perception)
Seriès, P., & Seitz, A. R. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00668 · Cited by 194
Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality
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Self-citationHigh-precision coding in visual cortex
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Modeling Imprecision in Perception, Valuation, and Choice
Woodford, M. (2020) · Annual Review of Economics · doi:10.1146/annurev-economics-102819-040518 · Cited by 176
Origin of information-limiting noise correlations
Kanitscheider, I., Coen-Cagli, R., & Pouget, A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508738112 · Cited by 166
Self-citationComparing Bayesian and non-Bayesian accounts of human confidence reports
Adler, W. T., & Ma, W. J. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006572 · Cited by 118
Self-citationDo humans make good decisions?
Summerfield, C., & Tsetsos, K. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.11.005 · Cited by 148
On the Origins of Suboptimality in Human Probabilistic Inference
Acerbi, L., Vijayakumar, S., & Wolpert, D. M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003661 · Cited by 145
Computational Phenotyping in Psychiatry: A Worked Example
Schwartenbeck, P., & Friston, K. J. (2016) · eNeuro · doi:10.1523/eneuro.0049-16.2016 · Cited by 134
Pupil-linked phasic arousal predicts a reduction of choice bias across species and decision domains
de Gee, J. W., Tsetsos, K., Schwabe, L., Urai, A. E., McCormick, D. A., McGinley, M. J., & Donner, T. H. (2020) · eLife · doi:10.7554/elife.54014 · Cited by 135
Learning-Based Control: A Tutorial and Some Recent Results
Jiang, Z., Bian, T., & Weinan, G. (2020) · Foundations and Trends® in Systems and Control · doi:10.1561/2600000023 · Cited by 138
Choice variability and suboptimality in uncertain environments
Wyart, V., & Koechlin, É. (2016) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2016.07.003 · Cited by 109
Optimal inference with suboptimal models: Addiction and active Bayesian inference
Schwartenbeck, P., FitzGerald, T. H. B., Mathys, C. D., Dolan, R. J., Wurst, F. M., Kronbichler, M., & Friston, K. J. (2014) · Medical Hypotheses · doi:10.1016/j.mehy.2014.12.007 · Cited by 111
Kalman Filtering Naturally Accounts for Visually Guided and Predictive Smooth Pursuit Dynamics
de Xivry, J. O., Coppe, S., Blohm, G., & Lefèvre, P. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2321-13.2013 · Cited by 106
Focality, stochasticity and neuroanatomic propagation in ALS pathogenesis
Ravits, J. (2014) · Experimental Neurology · doi:10.1016/j.expneurol.2014.07.021 · Cited by 110
Bayesian Decision Models: A Primer
Ma, W. J. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.037 · Cited by 111
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Self-citationKafashan, M., Jaffe, A. W., Chettih, S. N., Nogueira, R., Arandia-Romero, I., Harvey, C. D., Moreno-Bote, R., & Drugowitsch, J. (2021) · Nature Communications · doi:10.1038/s41467-020-20722-y · Cited by 96
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Gardner, J. L. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0340-4 · Cited by 94
The Value of the Follow-Through Derives from Motor Learning Depending on Future Actions
Howard, I. S., Wolpert, D. M., & Franklin, D. W. (2015) · Current Biology · doi:10.1016/j.cub.2014.12.037 · Cited by 86
A resource-rational theory of set size effects in human visual working memory
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Self-citationStochastic sampling provides a unifying account of visual working memory limits
Schneegans, S., Taylor, R. T., & Bays, P. M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2004306117 · Cited by 82
Feedforward and feedback sources of choice probability in neural population responses
Cumming, B. G., & Nienborg, H. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.01.009 · Cited by 84
Neuronal variability reflects probabilistic inference tuned to natural image statistics
Festa, D., Aschner, A., Davila, A., Kohn, A., & Coen-Cagli, R. (2021) · Nature Communications · doi:10.1038/s41467-021-23838-x · Cited by 81
Uncertainty-based inference of a common cause for body ownership
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Noisy Synaptic Conductance: Bug or a Feature?
Rusakov, D. A., Savtchenko, L. P., & Latham, P. E. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.03.009 · Cited by 72
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Findling, C., & Wyart, V. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.018 · Cited by 83
Park, W. J., Schauder, K. B., Zhang, R., Bennetto, L., & Tadin, D. (2017) · Scientific Reports · doi:10.1038/s41598-017-17676-5 · Cited by 82
Inference in the Brain: Statistics Flowing in Redundant Population Codes
Pitkow, X., & Angelaki, D. E. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.028 · Cited by 79
Self-citationRelation between belief and performance in perceptual decision making
Drugowitsch, J., Moreno-Bote, R., & Pouget, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096511 · Cited by 72
Self-citationHu, Y., Zylberberg, J., & Shea‐Brown, E. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003469 · Cited by 73
Late Development of Cue Integration Is Linked to Sensory Fusion in Cortex
Dekker, T. M., Ban, H., van der Velde, B., Sereno, M. I., Welchman, A. E., & Marko Nardini, A. (2015) · Current Biology · doi:10.1016/j.cub.2015.09.043 · Cited by 66
Modeling Imprecision in Perception, Valuation and Choice
Woodford, M. (2019) · National Bureau of Economic Research · doi:10.3386/w26258 · Cited by 60
The impact of learning on perceptual decisions and its implication for speed-accuracy tradeoffs
Mendonça, A. G., Drugowitsch, J., Vicente, M. I., DeWitt, E. E. J., Pouget, A., & Mainen, Z. F. (2020) · Nature Communications · doi:10.1038/s41467-020-16196-7 · Cited by 56
Self-citationRobust information propagation through noisy neural circuits
Zylberberg, J., Pouget, A., Latham, P. E., & Shea‐Brown, E. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005497 · Cited by 60
Self-citationIigaya, K., Ahmadian, Y., Sugrue, L. P., Corrado, G. S., Loewenstein, Y., Newsome, W. T., & Fusi, S. (2019) · Nature Communications · doi:10.1038/s41467-019-09388-3 · Cited by 58
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Liu, L. D., Haefner, R. M., & Pack, C. C. (2016) · eLife · doi:10.7554/elife.16167 · Cited by 55
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Bell, H. C. (2014) · International Journal of Comparative Psychology · doi:10.46867/ijcp.2014.27.02.02 · Cited by 45
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Adler, W. T., & Ma, W. J. (2018) · Neural Computation · doi:10.1162/neco_a_01141 · Cited by 44
Self-citationPutting perception into action with inverse optimal control for continuous psychophysics
Straub, D., & Rothkopf, C. A. (2022) · eLife · doi:10.7554/elife.76635 · Cited by 47
Human online adaptation to changes in prior probability
Norton, E. H., Acerbi, L., Ma, W. J., & Landy, M. S. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006681 · Cited by 42
Self-citationThe role of untuned neurons in sensory information codingPreprint
Zylberberg, J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/134379 · Cited by 45
Averaging, not internal noise, limits the development of coherent motion processing
Manning, C., Dakin, S. C., Tibber, M. S., & Pellicano, E. (2014) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2014.07.004 · Cited by 47
Distance estimation from monocular cues in an ethological visuomotor task
Parker, P. R. L., Abe, E. T. T., Beatie, N. T., Leonard, E. S. P., Martins, D. M., Sharp, S. L., Wyrick, D. G., Mazzucato, L., & Niell, C. M. (2022) · eLife · doi:10.7554/elife.74708 · Cited by 42
A Bayesian Attractor Model for Perceptual Decision Making
Bitzer, S., Bruineberg, J. P., & Kiebel, S. J. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004442 · Cited by 42
Knowledge generalization and the costs of multitasking
Garner, K., & Dux, P. E. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00653-x · Cited by 45
Motor invariants in action execution and perception
Torricelli, F., Tomassini, A., Pezzulo, G., Pozzo, T., Fadiga, L., & D’Ausilio, A. (2022) · Physics of Life Reviews · doi:10.1016/j.plrev.2022.11.003 · Cited by 46
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Laquitaine, S., & Gardner, J. L. (2017) · Neuron · doi:10.1016/j.neuron.2017.12.011 · Cited by 40
How learning unfolds in the brain: toward an optimization view
Hennig, J. A., Oby, E. R., Losey, D. M., Batista, A. P., Yu, B. M., & Chase, S. M. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.005 · Cited by 38
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Ma, W. J., & Peters, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.02181 · Cited by 53
Self-citationA neural population model for visual pattern detection.
Goris, R. L. T., Putzeys, T., Wagemans, J. P., & Wichmann, F. A. (2013) · Psychological Review · doi:10.1037/a0033136 · Cited by 40
An image-computable psychophysical spatial vision model
Schütt, H. H., & Wichmann, F. A. (2017) · Journal of Vision · doi:10.1167/17.12.12 · Cited by 53
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Findling, C., & Wyart, V. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.10.145300 · Cited by 36
Variable precision in visual perception.
Shen, S., & Ma, W. J. (2018) · Psychological Review · doi:10.1037/rev0000128 · Cited by 36
Self-citationRegulation of evidence accumulation by pupil-linked arousal processes
Keung, W., Hagen, T. A., & Wilson, R. C. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0551-4 · Cited by 48
Jin, M., Beck, J. M., & Glickfeld, L. L. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.3172-18.2019 · Cited by 31
Self-citationModel-Free Robust Optimal Feedback Mechanisms of Biological Motor Control
Bian, T., Wolpert, D. M., & Jiang, Z. (2020) · Neural Computation · doi:10.1162/neco_a_01260 · Cited by 35
Task-induced neural covariability as a signature of approximate Bayesian learning and inference
Lange, R. D., & Haefner, R. M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009557 · Cited by 45
Imperfect Bayesian inference in visual perception
Stengård, E., & van den Berg, R. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006465 · Cited by 34
Integration of cortical population signals for visual perception
Andrei, A. R., Pojoga, S., Janz, R., & Dragoi, V. (2019) · Nature Communications · doi:10.1038/s41467-019-11736-2 · Cited by 34
Illusory Tactile Motion Perception: An Analog of the Visual Filehne Illusion
Moscatelli, A., Hayward, V., Wexler, M., & Ernst, M. O. (2015) · Scientific Reports · doi:10.1038/srep14584 · Cited by 33
Park, I. M., & Pillow, J. W. (2026) · PLoS Computational Biology · doi:10.1101/178418 · Cited by 31
Chaotic neural dynamics facilitate probabilistic computations through sampling
Terada, Y., & Toyoizumi, T. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2312992121 · Cited by 39
Human noise blindness drives suboptimal cognitive inference
Castañón, S. H., Moran, R., Ding, J., Egner, T., Bang, D., & Summerfield, C. (2019) · Nature Communications · doi:10.1038/s41467-019-09330-7 · Cited by 34
Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons
Zhang, W., Wu, S., Josić́, K., & Doiron, B. (2023) · Nature Communications · doi:10.1038/s41467-023-41743-3 · Cited by 31
Cue Reliability Represented in the Shape of Tuning Curves in the Owl's Sound Localization System
Cazettes, F., Fischer, B. J., & Peña, J. L. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.3753-15.2016 · Cited by 29
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Self-citationSosa, R. (2022) · Psychological Review · doi:10.1037/rev0000405 · Cited by 31
Optimal models of decision-making in dynamic environments
Kilpatrick, Z. P., Holmes, W. R., Eissa, T. L., & Josić́, K. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.06.006 · Cited by 32
Li, S., Li, N., Girard, A., & Kolmanovsky, I. V. (2019) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc40024.2019.9029646 · Cited by 28
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Independence is elusive: Set size effects on encoding precision in visual search
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Self-citationRevealing nonlinear neural decoding by analyzing choices
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Metadata
- Authors
- Beck, J. M., Ma, W. J., Pitkow, X., Latham, P. E., & Pouget, A.
- Affiliations
- University of Rochester (US)
- Baylor College of Medicine (US)
- Gatsby Computational Neuroscience Unit (GB)
- University of Geneva (CH)
- Corresponding author
- Pouget, Alexandre
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2012-04
- Volume
- 74
- Issue
- 1
- Pages
- 30-39
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC4486264
- OpenAlex
- W2144984120
- Cited by
- 316
- References
- 70
- Citations by year
- 2026: 10 · 2025: 19 · 2024: 25 · 2023: 12 · 2022: 26 · 2021: 22 · 2020: 36 · 2019: 31 · 2018: 22 · 2017: 24 · 2016: 24 · 2015: 22 · 2014: 29 · 2013: 11 · 2012: 3
- FWCI
- 10.51
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- National Science Foundation (0446730, 1132009)
- James S. McDonnell Foundation
- Multidisciplinary University Research Initiative (N00014-07-1-0937)
- National Eye Institute (R01EY020958)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- behavioral variability, trial-to-trial variability, neural noise, sensory uncertainty, sensor reliability, Bayesian inference, perceptual decision making
- MeSH terms
- Animals, Behavior, Brain, Field Dependence-Independence, Humans, Models, Neurological, Models, Psychological, Neurons, Perceptual Masking, Sensation, Reproducibility of Results, Uncertainty, Behavioral Research
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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