Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization
Open accessAbstract
Categorization is a cornerstone of perception and cognition. Computationally, categorization amounts to applying decision boundaries in the space of stimulus features. We designed a visual categorization task in which optimal performance requires observers to incorporate trial-to-trial knowledge of the level of sensory uncertainty when setting their decision boundaries. We found that humans and monkeys did adjust their decision boundaries from trial to trial as the level of sensory noise varied, with some subjects performing near optimally. We constructed a neural network that implements uncertainty-based, near-optimal adjustment of decision boundaries. Divisive normalization emerges automatically as a key neural operation in this network. Our results offer an integrated computational and mechanistic framework for categorization under uncertainty.
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Correctness feedback was withheld during testing to avoid the possibility that confidence simply reflects a learned mapping between stimulus orientation and reliability and the probability of being correct.
“During testing, correctness feedback was withheld to avoid the possibility that confidence simply reflects a learned mapping between stimulus orientation and reliability and the probability of being correct [30–33].”
From Comparing Bayesian and non-Bayesian accounts of human confidence reports (2018) · cites this paper as [30]
The power law relationship encompasses a large family of monotonically decreasing relationships using only three parameters.
“We have previously [30] used this power law relationship because it encompasses a large family of monotonically decreasing relationships using only three parameters.”
From Comparing Bayesian and non-Bayesian accounts of human confidence reports (2018) · cites this paper as [30]
Cites
(1966) · Medical Entomology and Zoology · Cited by 10,293 · see citing works
Signal detection theory and psychophysics
Ingleby, D. (1967) · Journal of Sound and Vibration · doi:10.1016/0022-460x(67)90197-6 · Cited by 8,616 · see citing works
Jeffreys, H., Moran, P. A. P., & Chatfield, C. (1922) · Nature · doi:10.1038/109132a0 · Cited by 7,837 · see citing works
The psychometric function: I. Fitting, sampling, and goodness of fit
Wichmann, F. A., & Hill, N. J. (2001) · Perception & Psychophysics · doi:10.3758/bf03194544 · Cited by 2,359 · see citing works
Georgopoulos, A. P., Kalaska, J. F., Caminiti, R., & Massey, J. (1982) · Journal of Neuroscience · doi:10.1523/jneurosci.02-11-01527.1982 · Cited by 2,133 · see citing works
Normalization as a canonical neural computation
Carandini, M., & Heeger, D. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3136 · Cited by 1,906 · see citing works
Normalization of cell responses in cat striate cortex
Heeger, D. J. (1992) · Visual Neuroscience · doi:10.1017/s0952523800009640 · Cited by 1,835 · see citing works
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 · see citing works
Self-citationPerception as Bayesian Inference
(1996) · doi:10.1017/cbo9780511984037 · Cited by 1,425 · see citing works
Encoding of Spatial Location by Posterior Parietal Neurons
Andersen, R., Essick, G. K., & Siegel, R. M. (1985) · Science · doi:10.1126/science.4048942 · Cited by 1,413 · see citing works
Object perception as Bayesian inference
Kersten, D., Mamassian, P., & Yuille, A. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.55.090902.142005 · Cited by 1,269 · see citing works
Neuronal correlates of a perceptual decision
Newsome, W. T., Britten, K. H., & Movshon, J. A. (1989) · Nature · doi:10.1038/341052a0 · Cited by 1,177 · see citing works
Categorical Representation of Visual Stimuli in the Primate Prefrontal Cortex
Freedman, D. J., Riesenhuber, M., Poggio, T., & Miller, E. K. (2001) · Science · doi:10.1126/science.291.5502.312 · Cited by 1,123 · see citing works
Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex
Kiani, R., & Shadlen, M. N. (2009) · Science · doi:10.1126/science.1169405 · Cited by 1,027 · see citing works
Ceramide-orchestrated signalling in cancer cells
Morad, S. A. F., & Cabot, M. C. (2012) · Nature reviews. Cancer · doi:10.1038/nrc3398 · Cited by 1,018 · see citing works
Ashby, F. G., & Maddox, W. T. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.56.091103.070217 · Cited by 940 · see citing works
Neural correlates, computation and behavioural impact of decision confidence
Kepecs, A., Uchida, N., Zariwala, H. A., & Mainen, Z. F. (2008) · Nature · doi:10.1038/nature07200 · Cited by 809 · see citing works
Probabilistic reasoning by neurons
Yang, T., & Shadlen, M. N. (2007) · Nature · doi:10.1038/nature05852 · Cited by 581 · see citing works
Experience-dependent representation of visual categories in parietal cortex
Freedman, D. J., & Assad, J. A. (2006) · Nature · doi:10.1038/nature05078 · Cited by 542 · see citing works
The statistical determinants of adaptation rate in human reaching
Burge, J., Ernst, M. O., & Banks, M. S. (2008) · Journal of Vision · doi:10.1167/8.4.20 · Cited by 400 · see citing works
The contingent negative variation (CNV) event-related potential (ERP) predicts the attentional blink
Shapiro, K. L., Martin, E., Arend, I. C., Johnston, S. J., & Klein, C. (2008) · Journal of Vision · doi:10.1167/8.6.1 · Cited by 368 · see citing works
Gaze-dependent visual neurons in area V3A of monkey prestriate cortex
Galletti, C., & Battaglini, P. P. (1989) · Journal of Neuroscience · doi:10.1523/jneurosci.09-04-01112.1989 · Cited by 261 · see citing works
Trommershäuser, J., Kording, K. P., & Landy, M. (2011) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195387247.001.0001 · Cited by 253 · see citing works
Decoding the activity of neuronal populations in macaque primary visual cortex
Graf, A. B. A., Kohn, A., Jazayeri, M., & Movshon, J. A. (2011) · Nature Neuroscience · doi:10.1038/nn.2733 · Cited by 247 · see citing works
Gaze direction controls response gain in primary visual-cortex neurons
Trotter, Y., & Celebrini, S. (1999) · Nature · doi:10.1038/18444 · Cited by 188 · see citing works
Preferential encoding of visual categories in parietal cortex compared with prefrontal cortex
Swaminathan, S. K., & Freedman, D. J. (2012) · Nature Neuroscience · doi:10.1038/nn.3016 · Cited by 180 · see citing works
Predicting similarity and categorization from identification.
Ashby, F. G., & Lee, W. W. (1991) · Journal of Experimental Psychology General · doi:10.1037//0096-3445.120.2.150 · Cited by 171 · see citing works
Organizing probabilistic models of perception
Ma, W. J. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.08.010 · Cited by 167 · see citing works
Self-citationObject classification for human and ideal observers
Liu, Z., Knill, D. C., & Kersten, D. (1995) · Vision Research · doi:10.1016/0042-6989(94)00150-k · Cited by 164 · see citing works
Marginalization in Neural Circuits with Divisive Normalization
Beck, J. M., Latham, P. E., & Pouget, A. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.1706-11.2011 · Cited by 131 · see citing works
Self-citationImplicit knowledge of visual uncertainty guides decisions with asymmetric outcomes
Whiteley, L., & Sahani, M. (2008) · Journal of Vision · doi:10.1167/8.3.2 · Cited by 114 · see citing works
Behavior and neural basis of near-optimal visual search
Ma, W. J., Navalpakkam, V., Beck, J. M., van den Berg, R., & Pouget, A. (2011) · Nature Neuroscience · doi:10.1038/nn.2814 · Cited by 112 · see citing works
Self-citationMixture models and the probabilistic structure of depth cues.
Knill, D. (2003) · Vision Research · doi:10.1016/s0042-6989(03)00003-8 · Cited by 108 · see citing works
A Fast and Simple Population Code for Orientation in Primate V1
Berens, P., Ecker, A. S., Cotton, R. J., Ma, W. J., Bethge, M., & Tolias, A. S. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.1335-12.2012 · Cited by 107 · see citing works
Self-citationSequence effects in categorization of simple perceptual stimuli.
Stewart, N., Brown, G. D. A., & Chater, N. (2002) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037//0278-7393.28.1.3 · Cited by 106 · see citing works
The Dynamics of Scaling: A Memory-Based Anchor Model of Category Rating and Absolute Identification.
Petrov, A. A., & Anderson, J. R. (2005) · Psychological Review · doi:10.1037/0033-295x.112.2.383 · Cited by 100 · see citing works
Uncovering mental representations with Markov chain Monte Carlo
Sanborn, A. N., Griffiths, T. L., & Shiffrin, R. M. (2009) · Cognitive Psychology · doi:10.1016/j.cogpsych.2009.07.001 · Cited by 75 · see citing works
Simplified learning in complex situations: Knowledge partitioning in function learning.
Lewandowsky, S., Kalish, M. L., & Ngang, S. K. (2002) · Journal of Experimental Psychology General · doi:10.1037//0096-3445.131.2.163 · Cited by 63 · see citing works
Probabilistic Computation in Human Perception under Variability in Encoding Precision
Keshvari, S., van den Berg, R., & Ma, W. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040216 · Cited by 60 · see citing works
Self-citationFlexible tuning of similarity in exemplar-based categorization.
Lamberts, K. (1994) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.20.5.1003 · Cited by 49 · see citing works
van den Berg, R., Vogel, M., Josić́, K., & Ma, W. J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1108790109 · Cited by 36 · see citing works
Self-citationIntegrative Processing of Perception and Reward in an Auditory Localization Paradigm
Maiworm, M., König, P., & Röder, B. (2010) · Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) · doi:10.1027/1618-3169/a000088 · Cited by 5 · see citing works
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Boundy-Singer, Z. M., Ziemba, C. M., & Goris, R. L. T. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01464-x · Cited by 71 · see citing works
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Heng, J., Woodford, M., & Polanía, R. H. (2020) · eLife · doi:10.7554/elife.54962 · Cited by 68 · see citing works
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Edelson, M. G., Polanía, R. H., Ruff, C. C., Fehr, E., & Hare, T. A. (2018) · Science · doi:10.1126/science.aat0036 · Cited by 63 · see citing works
Hanley, D., López, A. V., Fiorini, V. D., REBOREDA, J. C., Grim, T., & Hauber, M. E. (2019) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2018.0195 · Cited by 62 · see citing works
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Alós‐Ferrer, C., & Garagnani, M. (2025) · Cognitive Psychology · doi:10.1016/j.cogpsych.2025.101779 · Cited by 4 · see citing works
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Rosenberg, A., & Sunkara, A. (2018) · Cortex · doi:10.1016/j.cortex.2018.06.017 · Cited by 4 · see citing works
Haefner, R. M., Berkes, P., & Fiser, J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.0257 · Cited by 9 · see citing works
The role of sensory uncertainty in simple contour integrationPreprint
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Dynamical Mechanism of Sampling-Based Probabilistic Inference Under Probabilistic Population Codes
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Hong, F., Xu, J., Landy, M. S., & Badde, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.01.626278 · Cited by 1 · see citing works
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Self-citation
- Authors
- Qamar, A. T., Cotton, R. J., George, R. G., Beck, J. M., Prezhdo, E., Laudano, A., Tolias, A. S., & Ma, W. J.
- Affiliations
- Baylor College of Medicine (US)
- University of Rochester (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2013-11-22
- Volume
- 110
- Issue
- 50
- Pages
- 20332-20337
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3864339
- OpenAlex
- W2141516486
- Cited by
- 64
- References
- 42
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 4 · 2023: 5 · 2022: 7 · 2021: 9 · 2020: 3 · 2019: 10 · 2018: 7 · 2017: 3 · 2016: 4 · 2015: 2 · 2014: 4
- FWCI
- 1.83
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Innovative Research Group Project of the National Natural Science Foundation of China
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- visual categorization, decision boundaries, sensory uncertainty, sensory noise, monkeys, neural network model, divisive normalization, perceptual decision making
- MeSH terms
- Animals, Haplorhini, Bayes Theorem, Concept Formation, Decision Making, Humans, Models, Neurological, Nerve Net, Species Specificity, Visual Perception, Likelihood Functions
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