Abstract
The study of the brain’s representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer’s beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish between ‘code-driven’ and ‘correlational’ approaches. Code-driven approaches make assumptions about the neural code for representing world states and the associated uncertainty. By contrast, correlational approaches search for relationships between uncertainty and neural activity without constraints on the neural representation of the world state that this uncertainty accompanies. To compare these two approaches, we apply several criteria for neural representations: sensitivity, specificity, invariance and functionality. Our analysis reveals that the two approaches lead to different but complementary findings, shaping new research questions and guiding future experiments.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Ballard, D. H. Brain Computation as Hierarchical Abstraction (MIT Press, 2015).
Hoyer, P. O. & Hyvärinen, A. Interpreting neural response variability as Monte Carlo sampling of the posterior. In Advances in Neural Information Processing Systems 293–300 (2002). An influential article that proposed that neural activity could be explained with a sampling-based code.
Knill, D. C. & Pouget, A. The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 27, 712–719 (2004).
Lee, T. S. & Mumford, D. Hierarchical Bayesian inference in the visual cortex. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 20, 1434–1448 (2003).
Ma, W. J. & Jazayeri, M. Neural coding of uncertainty and probability. Annu. Rev. Neurosci. 37, 205–220 (2014).
Bach, D. R. & Dolan, R. J. Knowing how much you don’t know: a neural organization of uncertainty estimates. Nat. Rev. Neurosci. 13, 572–586 (2012).
Muller, T. H., Mars, R. B., Behrens, T. E. & O’Reilly, J. X. Control of entropy in neural models of environmental state. eLife 8, e39404 (2019).
Rushworth, M. F. S. & Behrens, T. E. J. Choice, uncertainty and value in prefrontal and cingulate cortex. Nat. Neurosci. 11, 389–397 (2008).
Tomov, M. S., Truong, V. Q., Hundia, R. A. & Gershman, S. J. Dissociable neural correlates of uncertainty underlie different exploration strategies. Nat. Commun. 11, 2371 (2020).
Behrens, T. E. J., Woolrich, M. W., Walton, M. E. & Rushworth, M. F. S. Learning the value of information in an uncertain world. Nat. Neurosci. 10, 1214–1221 (2007).
McGuire, J. T., Nassar, M. R., Gold, J. I. & Kable, J. W. Functionally dissociable influences on learning rate in a dynamic environment. Neuron 84, 870–881 (2014).
Meyniel, F., Schlunegger, D. & Dehaene, S. The sense of confidence during probabilistic learning: a normative account. PLoS Comput Biol. 11, e1004305 (2015).
O’Reilly, J. X. Making predictions in a changing world—inference, uncertainty, and learning. Front. Neurosci. 7, 105 (2013).
Kersten, D., Mamassian, P. & Yuille, A. Object perception as Bayesian inference. Annu. Rev. Psychol. 55, 271–304 (2004).
Qamar, A. T. et al. Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization. Proc. Natl Acad. Sci. USA 110, 20332–20337 (2013).
Zhou, Y., Acerbi, L. & Ma, W. J. The role of sensory uncertainty in simple contour integration. PLoS Comput. Biol. 16, e1006308 (2020).
Alais, D. & Burr, D. The ventriloquist effect results from near-optimal bimodal integration. Curr. Biol. 14, 257–262 (2004).
Deroy, O., Spence, C. & Noppeney, U. Metacognition in multisensory perception. Trends Cogn. Sci. 20, 736–747 (2016).
Ernst, M. O. & Banks, M. S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433 (2002).
Trommershäuser, J., Kording, K. & Landy, M. S. Sensory Cue Integration (Oxford Univ. Press, 2011).
Todorov, E. Optimality principles in sensorimotor control. Nat. Neurosci. 7, 907–915 (2004).
Trommershäuser, J., Maloney, L. T. & Landy, M. S. Decision making, movement planning and statistical decision theory. Trends Cogn. Sci. 12, 291–297 (2008).
Flavell, J. H. & Wellman, H. M. in Perspectives on the Development of Memory and Cognition (eds. Kail, R. V. Jr & Hagen, J. W.) 3–33 (L. Erlbaum, 1977).
Koriat, A., Sheffer, L. & Ma’ayan, H. Comparing objective and subjective learning curves: Judgments of learning exhibit increased underconfidence with practice. J. Exp. Psychol. 131, 147–162 (2002).
Rademaker, R. L., Tredway, C. H. & Tong, F. Introspective judgments predict the precision and likelihood of successful maintenance of visual working memory. J. Vis. 12, 21 (2012).
Yoo, A. H., Acerbi, L. & Ma, W. J. Uncertainty is maintained and used in working memory. J. Vis. 21, 13 (2021).
Dekleva, B. M., Ramkumar, P., Wanda, P. A., Kording, K. P. & Miller, L. E. Uncertainty leads to persistent effects on reach representations in dorsal premotor cortex. eLife 5, e14316 (2016).
Devkar, D., Wright, A. A. & Ma, W. J. Monkeys and humans take local uncertainty into account when localizing a change. J. Vis. 17, 4 (2017).
Fiorillo, C. D. Discrete coding of reward probability and uncertainty by dopamine neurons. Science 299, 1898–1902 (2003).
Kepecs, A., Uchida, N., Zariwala, H. A. & Mainen, Z. F. Neural correlates, computation and behavioural impact of decision confidence. Nature 455, 227–231 (2008).
Kiani, R. & Shadlen, M. N. Representation of confidence associated with a decision by neurons in the parietal cortex. Science 324, 759–764 (2009).
Komura, Y., Nikkuni, A., Hirashima, N., Uetake, T. & Miyamoto, A. Responses of pulvinar neurons reflect a subject’s confidence in visual categorization. Nat. Neurosci. 16, 749–755 (2013).
Lak, A. et al. Orbitofrontal cortex is required for optimal waiting based on decision confidence. Neuron 84, 190–201 (2014).
Odegaard, B. et al. Superior colliculus neuronal ensemble activity signals optimal rather than subjective confidence. Proc. Natl Acad. Sci. USA 115, E1588–E1597 (2018).
Walker, E. Y., Cotton, R. J., Ma, W. J. & Tolias, A. S. A neural basis of probabilistic computation in visual cortex. Nat. Neurosci. 23, 122–129 (2020). Example of the code-driven approach that uses a probabilistic population code estimated in a data-driven manner by means of an artificial neural network. The uncertainty derived from multiunit recordings accounts for the monkey choices.
Helmholtz, H. Handbuch der Physiologischen Optik (Leopold Voss, 1867).
Shannon, C. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).
Beck, J. M., Ma, W. J., Pitkow, X., Latham, P. E. & Pouget, A. Not noisy, just wrong: the role of suboptimal inference in behavioral variability. Neuron 74, 30–39 (2012).
Rahnev, D. & Denison, R. N. Suboptimality in perceptual decision making. Behav. Brain Sci. 41, e223 (2018).
Iglesias, S. et al. Hierarchical prediction errors in midbrain and basal forebrain during sensory learning. Neuron 80, 519–530 (2013).
Mathys, C. D. et al. Uncertainty in perception and the hierarchical gaussian filter. Front. Hum. Neurosci. 8, 825 (2014).
Norton, E. H., Acerbi, L., Ma, W. J. & Landy, M. S. Human online adaptation to changes in prior probability. PLOS Comput. Biol. 15, e1006681 (2019).
Barthelmé, S. & Mamassian, P. Evaluation of objective uncertainty in the visual system. PLoS Comput. Biol. 5, e1000504 (2009).
Necker, L. A. Observations on some remarkable optical phænomena seen in Switzerland; and on an optical phænomenon which occurs on viewing a figure of a crystal or geometrical solid. Lond. Edinb. Dublin Philos. Mag. J. Sci. 1, 329–337 (1832).
Faisal, A. A., Selen, L. P. J. & Wolpert, D. M. Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008).
Meyniel, F. Brain dynamics for confidence-weighted learning. PLOS Comput. Biol. 16, e1007935 (2020).
Meyniel, F. & Dehaene, S. Brain networks for confidence weighting and hierarchical inference during probabilistic learning. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1615773114 (2017). Example of a correlational approach that uses an ideal observer model of the input to derive uncertainty about a probability. The study reports fMRI correlates of this uncertainty distinct from correlates of confounding factors like unpredictability and surprise.
O’Reilly, J. X., Jbabdi, S., Rushworth, M. F. S. & Behrens, T. E. J. Brain systems for probabilistic and dynamic prediction: computational specificity and integration. PLoS Biol. 11, e1001662 (2013).
Payzan-LeNestour, E., Dunne, S., Bossaerts, P. & O’Doherty, J. P. The neural representation of unexpected uncertainty during value-based decision making. Neuron 79, 191–201 (2013).
Vilares, I., Howard, J. D., Fernandes, H. L., Gottfried, J. A. & Kording, K. P. Differential representations of prior and likelihood uncertainty in the human brain. Curr. Biol. 22, 1641–1648 (2012). Example of correlational approach that used specific features of the input (scatter) as a proxy for uncertainty (about the location of a cloud of dots). The fMRI correlates of this uncertainty are distinct from prior uncertainty.
Geurts, L. S., Cooke, J. R. H., van Bergen, R. S. & Jehee, J. F. M. Subjective confidence reflects representation of Bayesian probability in cortex. Nat. Hum. Behav. https://doi.org/10.1038/s41562-021-01247-w (2022). Example of a code-driven approach that uses a probabilistic population code estimated in a data-driven manner by means of a generalized linear model. The uncertainty derived from fMRI activity correlates with subjective reports of uncertainty.
Adler, W. T. & Ma, W. J. Comparing Bayesian and non-Bayesian accounts of human confidence reports. PLOS Comput. Biol. 14, e1006572 (2018).
De Martino, B., Fleming, S. M., Garrett, N. & Dolan, R. J. Confidence in value-based choice. Nat. Neurosci. 16, 105–110 (2013).
Guggenmos, M., Wilbertz, G., Hebart, M. N. & Sterzer, P. Mesolimbic confidence signals guide perceptual learning in the absence of external feedback. eLife 5, e13388 (2016).
Hebart, M. N., Schriever, Y., Donner, T. H. & Haynes, J.-D. The relationship between perceptual decision variables and confidence in the human brain. Cereb. Cortex https://doi.org/10.1093/cercor/bhu181 (2014).
Lebreton, M., Abitbol, R., Daunizeau, J. & Pessiglione, M. Automatic integration of confidence in the brain valuation signal. Nat. Neurosci. 18, 1159–1167 (2015).
Li, H.-H., Sprague, T. C., Yoo, A. H., Ma, W. J. & Curtis, C. E. Joint representation of working memory and uncertainty in human cortex. Neuron 109, 3699–3712 (2021).
Meyniel, F., Sigman, M. & Mainen, Z. F. Confidence as bayesian probability: from neural origins to behavior. Neuron 88, 78–92 (2015).
Peirce, C. S. & Jastrow, J. On small differences in sensation. Mem. Natl Acad. Sci. 3, 75–83 (1884).
Pouget, A., Drugowitsch, J. & Kepecs, A. Confidence and certainty: distinct probabilistic quantities for different goals. Nat. Neurosci. 19, 366–374 (2016).
Kepecs, A. & Mainen, Z. F. A computational framework for the study of confidence in humans and animals. Philos. Trans. R. Soc. Lond. B Biol. Sci. 367, 1322–1337 (2012).
Tzagarakis, C., Ince, N. F., Leuthold, A. C. & Pellizzer, G. Beta-band activity during motor planning reflects response uncertainty. J. Neurosci. 30, 11270–11277 (2010).
Zylberberg, A., Fetsch, C. R. & Shadlen, M. N. The influence of evidence volatility on choice, reaction time and confidence in a perceptual decision. eLife 5, e17688 (2016).
Masset, P., Ott, T., Lak, A., Hirokawa, J. & Kepecs, A. Behavior- and modality-general representation of confidence in orbitofrontal cortex. Cell 182, 112–126 (2020). Studies decision confidence in rats using waiting times as a proxy for uncertainty and identifies a neural representation of decision confidence in the orbitofrontal cortex that passes the tests of sensitivity, specificity (with respect to the features of the input), invariance (to the sensory modality) and functionality (correlation with learning).
Schmack, K., Bosc, M., Ott, T., Sturgill, J. F. & Kepecs, A. Striatal dopamine mediates hallucination-like perception in mice. Science 372, eabf4740 (2021).
Gherman, S. & Philiastides, M. G. Neural representations of confidence emerge from the process of decision formation during perceptual choices. NeuroImage 106, 134–143 (2015).
Hampton, R. R. Rhesus monkeys know when they remember. Proc. Natl Acad. Sci. USA 98, 5359–5362 (2001).
Middlebrooks, P. G. & Sommer, M. A. Neuronal correlates of metacognition in primate frontal cortex. Neuron 75, 517–530 (2012).
van Bergen, R. S., Ma, W. J., Pratte, M. S. & Jehee, J. F. M. Sensory uncertainty decoded from visual cortex predicts behavior. Nat. Neurosci. 18, 1728–1730 (2015).
van Bergen, R. S. & Jehee, J. F. M. Probabilistic representation in human visual cortex reflects uncertainty in serial decisions. J. Neurosci. 39, 8164–8176 (2019).
Badre, D., Doll, B. B., Long, N. M. & Frank, M. J. Rostrolateral prefrontal cortex and individual differences in uncertainty-driven exploration. Neuron 73, 595–607 (2012). Example of a correlational approach that uses an ideal observer model of the learning process to infer uncertainty in a task. Findings show evidence of a functional role for uncertainty (here, in terms of exploration).
Stern, E. R., Gonzalez, R., Welsh, R. C. & Taylor, S. F. Updating beliefs for a decision: neural correlates of uncertainty and underconfidence. J. Neurosci. 30, 8032–8041 (2010).
Sedley, W. et al. Neural signatures of perceptual inference. eLife 5, e11476 (2016).
Festa, D., Aschner, A., Davila, A., Kohn, A. & Coen-Cagli, R. Neuronal variability reflects probabilistic inference tuned to natural image statistics. Nat. Commun. 12, 3635 (2021).
Hénaff, O. J., Boundy-Singer, Z. M., Meding, K., Ziemba, C. M. & Goris, R. L. T. Representation of visual uncertainty through neural gain variability. Nat. Commun. 11, 2513 (2020).
Orbán, G., Berkes, P., Fiser, J. & Lengyel, M. Neural variability and sampling-based probabilistic representations in the visual cortex. Neuron 92, 530–543 (2016). Example of a code-driven approach that uses a sampling-based code and finds that neural variability (in spiking activity and membrane potential) changes along features of visual input related to uncertainty (for example, it quenches at the stimulus onset, decreases with contrast and aperture).
Bang, D. & Fleming, S. M. Distinct encoding of decision confidence in human medial prefrontal cortex. Proc. Natl Acad. Sci. USA 115, 6082–6087 (2018).
Friston, K., Ashburner, J., Kiebel, S., Nichols, T. & Penny, W. Statistical Parametric Mapping: the Analysis of Functional Brain Images (Academic, 2007).
Naselaris, T., Kay, K. N., Nishimoto, S. & Gallant, J. L. Encoding and decoding in fMRI. NeuroImage 56, 400–410 (2011).
Poldrack, R. A., Huckins, G. & Varoquaux, G. Establishment of best practices for evidence for prediction: a review. JAMA Psychiatry 77, 534–540 (2020).
Lange, R. D., Shivkumar, S., Chattoraj, A. & Haefner, R. M. Bayesian encoding and decoding as distinct perspectives on neural coding. Preprint at bioRxiv https://doi.org/10.1101/2020.10.14.339770 (2021).
Shivkumar, S., Lange, R., Chattoraj, A. & Haefner, R. A probabilistic population code based on neural samples. In Advances in Neural Information Processing Systems (eds. S. Bengio et al.) 31, 1–10 (MIT Press, 2018).
Barlow, H. B. Pattern recognition and the responses of sensory neurons. Ann. N. Y. Acad. Sci. 156, 872–881 (1969).
Deneve, S. Bayesian spiking neurons I: inference. Neural Comput. 20, 91–117 (2008).
Jazayeri, M. & Movshon, J. A. Optimal representation of sensory information by neural populations. Nat. Neurosci. 9, 690–696 (2006).
Sahani, M. & Dayan, P. Doubly distributional population codes: simultaneous representation of uncertainty and multiplicity. Neural Comput. 15, 2255–2279 (2003).
Sohn, H. & Narain, D. Neural implementations of Bayesian inference. Curr. Opin. Neurobiol. 70, 121–129 (2021).
Dayan, P. & Abbott, L. F. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (MIT Press, 2005).
Pouget, A., Dayan, P. & Zemel, R. S. Inference and computation with population codes. Annu. Rev. Neurosci. 26, 381–410 (2003).
Deneve, S., Latham, P. E. & Pouget, A. Reading population codes: a neural implementation of ideal observers. Nat. Neurosci. 2, 740–745 (1999).
Fetsch, C. R., Pouget, A., DeAngelis, G. C. & Angelaki, D. E. Neural correlates of reliability-based cue weighting during multisensory integration. Nat. Neurosci. 15, 146–154 (2012).
Ma, W. J., Beck, J. M., Latham, P. E. & Pouget, A. Bayesian inference with probabilistic population codes. Nat. Neurosci. 9, 1432–1438 (2006). Introduced the concept of probabilistic population code as the idea that the representation of probability distribution over a latent world state by a population of neurons, conferred by an internal model of neural variability, allows certain Bayesian computations to be implemented by simple neural operations.
Fiser, J., Berkes, P., Orbán, G. & Lengyel, M. Statistically optimal perception and learning: from behavior to neural representations. Trends Cogn. Sci. 14, 119–130 (2010).
Echeveste, R., Aitchison, L., Hennequin, G. & Lengyel, M. Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference. Nat. Neurosci. 23, 1138–1149 (2020). Shows that an artificial neural network can be trained to emit spikes that correspond to samples from a posterior distribution of some feature of the input. Although not trained to do so, the artificial network shows dynamics similar to those of actual neural networks.
Bach, D. R., Hulme, O., Penny, W. D. & Dolan, R. J. The known unknowns: neural representation of second-order uncertainty, and ambiguity. J. Neurosci. 31, 4811–4820 (2011).
Bányai, M. et al. Stimulus complexity shapes response correlations in primary visual cortex. Proc. Natl Acad. Sci. USA 116, 2723–2732 (2019). Example of a code-driven approach that uses a sampling-based code and shows that the covariance of neural activity in a population of neurons can be explained by hierarchical inference with a prominent impact of the image’s higher-level features even in regions tuned to local features, such as the primary visual cortex.
Grinband, J., Hirsch, J. & Ferrera, V. P. A neural representation of categorization uncertainty in the human brain. Neuron 49, 757–763 (2006).
Trudel, N. et al. Polarity of uncertainty representation during exploration and exploitation in ventromedial prefrontal cortex. Nat. Hum. Behav. 5, 83–98 (2021).
Strange, B. A., Duggins, A., Penny, W., Dolan, R. J. & Friston, K. J. Information theory, novelty and hippocampal responses: unpredicted or unpredictable? Neural Netw. 18, 225–230 (2005).
Tan, H., Wade, C. & Brown, P. Post-movement beta activity in sensorimotor cortex indexes confidence in the estimations from internal models. J. Neurosci. 36, 1516–1528 (2016).
Hsu, M., Bhatt, M., Adolphs, R., Tranel, D. & Camerer, C. F. Neural systems responding to degrees of uncertainty in human decision-making. Science 310, 1680–1683 (2005). Presented a distinction between uncertainty about a latent feature and uncertainty about an outcome (referred to as ambiguity and risk, respectively, in behavioral economics), whose fMRI correlates are anatomically segregated in the human brain.
Monosov, I. E., Leopold, D. A. & Hikosaka, O. Neurons in the primate medial basal forebrain signal combined information about reward uncertainty, value, and punishment anticipation. J. Neurosci. 35, 7443–7459 (2015).
Monosov, I. E. & Hikosaka, O. Selective and graded coding of reward uncertainty by neurons in the primate anterodorsal septal region. Nat. Neurosci. 16, 756–762 (2013).
Preuschoff, K., Bossaerts, P. & Quartz, S. R. Neural differentiation of expected reward and risk in human subcortical structures. Neuron 51, 381–390 (2006).
So, N. & Stuphorn, V. Supplementary eye field encodes confidence in decisions under risk. Cereb. Cortex 26, 764–782 (2016).
Michael, E., de Gardelle, V., Nevado-Holgado, A. & Summerfield, C. Unreliable evidence: 2 sources of uncertainty during perceptual choice. Cereb. Cortex 25, 937–947 (2015). Example of a correlational approach that uses a categorization task based on either shape or color from trial to trial and identifies representations of uncertainty about the decision that are invariant to the perceptual feature (shape or color) on which a decision is based.
Nastase, S. A., Davis, B. & Hasson, U. Cross-modal and non-monotonic representations of statistical regularity are encoded in local neural response patterns. NeuroImage 173, 509–517 (2018).
Fleming, S. M. & Daw, N. D. Self-evaluation of decision-making: a general Bayesian framework for metacognitive computation. Psychol. Rev. 124, 91–114 (2017).
Zylberberg, A., Roelfsema, P. R. & Sigman, M. Variance misperception explains illusions of confidence in simple perceptual decisions. Conscious. Cognition 27, 246–253 (2014).
Fleming, S. M. & Dolan, R. J. Effects of loss aversion on post-decision wagering: implications for measures of awareness. Conscious. Cognition 19, 352–363 (2010).
Blankenstein, N. E., Peper, J. S., Crone, E. A. & van Duijvenvoorde, A. C. K. Neural mechanisms underlying risk and ambiguity attitudes. J. Cogn. Neurosci. 29, 1845–1859 (2017).
Ting, C. -C., Yu, C. -C., Maloney, L. T. & Wu, S. -W. Neural mechanisms for integrating prior knowledge and likelihood in value-based probabilistic inference. J. Neurosci. 35, 1792–1805 (2015).
Haefner, R. M., Berkes, P. & Fiser, J. Perceptual decision-making as probabilistic inference by neural sampling. Neuron 90, 649–660 (2016).
Rahnev, D. et al. Attention induces conservative subjective biases in visual perception. Nat. Neurosci. 14, 1513–1515 (2011).
Schultz, W. et al. Explicit neural signals reflecting reward uncertainty. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 3801–3811 (2008).
DiCarlo, J. J., Zoccolan, D. & Rust, N. C. How does the brain solve visual object recognition? Neuron 73, 415–434 (2012).
Kriegeskorte, N. & Diedrichsen, J. Peeling the onion of brain representations. Annu. Rev. Neurosci. 42, 407–432 (2019).
Pouget, A., Beck, J. M., Ma, W. J. & Latham, P. E. Probabilistic brains: knowns and unknowns. Nat. Neurosci. 16, 1170–1178 (2013).
Koblinger, Á., Fiser, J. & Lengyel, M. Representations of uncertainty: where art thou? Curr. Opin. Behav. Sci. 38, 150–162 (2021).
FitzGerald, T. H. B., Seymour, B., Bach, D. R. & Dolan, R. J. Differentiable neural substrates for learned and described value and risk. Curr. Biol. 20, 1823–1829 (2010).
Huettel, S. A. Decisions under uncertainty: probabilistic context influences activation of prefrontal and parietal cortices. J. Neurosci. 25, 3304–3311 (2005).
Monosov, I. E. Anterior cingulate is a source of valence-specific information about value and uncertainty. Nat. Commun. 8, 134 (2017).
Acerbi, L., Vijayakumar, S. & Wolpert, D. M. On the origins of suboptimality in human probabilistic inference. PLoS Comput Biol. 10, e1003661 (2014).
Yeon, J. & Rahnev, D. The suboptimality of perceptual decision making with multiple alternatives. Nat. Commun. 11, 3857 (2020).
Dabney, W. et al. A distributional code for value in dopamine-based reinforcement learning. Nature 577, 671–675 (2020).
Haxby, J. V. et al. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430 (2001).
Haxby, J. V., Connolly, A. C. & Guntupalli, J. S. Decoding neural representational spaces using multivariate pattern analysis. Annu. Rev. Neurosci. 37, 435–456 (2014).
Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E. & Gallant, J. L. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 532, 453–458 (2016).
Park, I. M., Meister, M. L. R., Huk, A. C. & Pillow, J. W. Encoding and decoding in parietal cortex during sensorimotor decision-making. Nat. Neurosci. 17, 1395–1403 (2014).
Pillow, J. W. et al. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature 454, 995–999 (2008).
Haynes, J. -D. A primer on pattern-based approaches to fMRI: principles, pitfalls and perspectives. Neuron 87, 257–270 (2015).
Hubel, D. H. & Wiesel, T. N. Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148, 574–591 (1959).
DeWind, N. K., Adams, G. K., Platt, M. L. & Brannon, E. M. Modeling the approximate number system to quantify the contribution of visual stimulus features. Cognition 142, 247–265 (2015).
Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C. & Fried, I. Invariant visual representation by single neurons in the human brain. Nature 435, 1102–1107 (2005).
Baker, B., Lansdell, B. & Kording, K. A philosophical understanding of representation for neuroscience. Preprint at https://doi.org/10.48550/arXiv.2102.06592 (2021).
Nichols, M. J. & Newsome, W. T. Middle temporal visual area microstimulation influences veridical judgments of motion direction. J. Neurosci. 22, 9530–9540 (2002).
Cortese, A., Amano, K., Koizumi, A., Kawato, M. & Lau, H. Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance. Nat. Commun. 7, 13669 (2016).
Gherman, S. & Philiastides, M. G. Human VMPFC encodes early signatures of confidence in perceptual decisions. eLife 7, e38293 (2018).
Author information
Authors and Affiliations
Contributions
All authors contributed to the writing of this Review.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Neuroscience thanks Máté Lengyel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Supplementary Information
Supplementary Table 1 and Glossary
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Walker, E.Y., Pohl, S., Denison, R.N. et al. Studying the neural representations of uncertainty. Nat Neurosci 26, 1857–1867 (2023). https://doi.org/10.1038/s41593-023-01444-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41593-023-01444-y
This article is cited by
-
Bayesian encoding and decoding as distinct perspectives on neural coding
Nature Neuroscience (2023)
