A probability distribution over latent causes, in the orbitofrontal cortex
Open accessPublished version of a preprint
Abstract
UNLABELLED: The orbitofrontal cortex (OFC) has been implicated in both the representation of "state," in studies of reinforcement learning and decision making, and also in the representation of "schemas," in studies of episodic memory. Both of these cognitive constructs require a similar inference about the underlying situation or "latent cause" that generates our observations at any given time. The statistically optimal solution to this inference problem is to use Bayes' rule to compute a posterior probability distribution over latent causes. To test whether such a posterior probability distribution is represented in the OFC, we tasked human participants with inferring a probability distribution over four possible latent causes, based on their observations. Using fMRI pattern similarity analyses, we found that BOLD activity in the OFC is best explained as representing the (log-transformed) posterior distribution over latent causes. Furthermore, this pattern explained OFC activity better than other task-relevant alternatives, such as the most probable latent cause, the most recent observation, or the uncertainty over latent causes. SIGNIFICANCE STATEMENT: Our world is governed by hidden (latent) causes that we cannot observe, but which generate the observations we see. A range of high-level cognitive processes require inference of a probability distribution (or "belief distribution") over the possible latent causes that might be generating our current observations. This is true for reinforcement learning and decision making (where the latent cause comprises the true "state" of the task), and for episodic memory (where memories are believed to be organized by the inferred situation or "schema"). Using fMRI, we show that this belief distribution over latent causes is encoded in patterns of brain activity in the orbitofrontal cortex, an area that has been separately implicated in the representations of both states and schemas.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Orbitofrontal cortex tracks hidden states and encodes how likely each candidate latent cause is at a given moment in inference.
“Neurally, the orbitofrontal cortex, which is sensitive to unobservable states [51], has been shown to represent the probability that each latent cause is currently active during the inference process [52].”
From Does mental context drift or shift? (2017) · cites this paper as [52]
It is possible that the brain represents probability distributions over small collections of variables or variational approximations to probability distributions, but this would not affect the key argument, which stems from the unavoidable difficulty of finding isolated peaks in, and calculating the volume of, complex probability distributions.
“Of course, it is possible that the brain represents probability distributions over small collections of variables [90,91], or variational approximations to probability distributions [92], but this would not affect our key argument, which stems from the unavoidable difficulty of finding isolated peaks in, and calculating the volume of, complex probability distributions.”
From Bayesian Brains without Probabilities (2016) · cites this paper as [91]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Efron, B., & Tibshirani, R. (1986) · Statistical Science · doi:10.1214/ss/1177013815 · Cited by 6,033 · see citing works
Planning and acting in partially observable stochastic domains
Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998) · Artificial Intelligence · doi:10.1016/s0004-3702(98)00023-x · Cited by 4,149 · see citing works
Representational similarity analysis-connecting the branches of systems neuroscience
Kriegeskorte, N. (2008) · Frontiers in Systems Neuroscience · doi:10.3389/neuro.06.004.2008 · Cited by 3,355 · see citing works
Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature
Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.010 · Cited by 2,956 · see citing works
Scalar expectancy theory and Weber's law in animal timing.
Gibbon, J. (1977) · Psychological Review · doi:10.1037/0033-295x.84.3.279 · Cited by 2,224 · see citing works
A neostriatal habit learning system in humans
Knowlton, B. J., Mangels, J. A., & Squire, L. R. (1996) · Science · doi:10.1126/science.273.5280.1399 · Cited by 1,777 · see citing works
Two cortical systems for memory-guided behaviour
Ranganath, C., & Ritchey, M. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3338 · Cited by 1,377 · see citing works
Schemas and Memory Consolidation.
Tse, D., Langston, R. F., Kakeyama, M., Bethus, I., Spooner, P. A., Wood, E. R., Witter, M. P., & Morris, R. G. M. (2007) · Science · doi:10.1126/science.1135935 · Cited by 1,244 · see citing works
Orbitofrontal Cortex as a Cognitive Map of Task Space
Wilson, R. C., Takahashi, Y. K., Schoenbaum, G., & Niv, Y. (2014) · Neuron · doi:10.1016/j.neuron.2013.11.005 · Cited by 885 · see citing works
Self-citationHow schema and novelty augment memory formation
van Kesteren, M. T. R., Ruiter, D. J., Fernández, G., & Henson, R. N. (2012) · Trends in Neurosciences · doi:10.1016/j.tins.2012.02.001 · Cited by 860 · see citing works
Optimized EPI for fMRI studies of the orbitofrontal cortex
Deichmann, R., Gottfried, J. A., Hutton, C., & Turner, R. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00073-9 · Cited by 821 · see citing works
How does nature program neuron types?
Borst, A. (2008) · Frontiers in Neuroscience · doi:10.3389/neuro.01.016.2008 · Cited by 705 · see citing works
Neural activity in the human brain relating to uncertainty and arousal during anticipation.
Critchley, H. D., Mathias, C. J., & Dolan, R. J. (2001) · Neuron · doi:10.1016/s0896-6273(01)00225-2 · Cited by 681 · see citing works
Schema-dependent gene activation and memory encoding in neocortex
Tse, D., Takeuchi, T., Kakeyama, M., Kajii, Y., Okuno, H., Tohyama, C., Bito, H., & Morris, R. G. M. (2011) · Science · doi:10.1126/science.1205274 · Cited by 618 · see citing works
Probabilistic reasoning by neurons
Yang, T., & Shadlen, M. N. (2007) · Nature · doi:10.1038/nature05852 · Cited by 581 · see citing works
What is a memory schema? A historical perspective on current neuroscience literature
Ghosh, V. E., & Gilboa, A. (2014) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2013.11.010 · Cited by 529 · see citing works
Reward Value Coding Distinct From Risk Attitude-Related Uncertainty Coding in Human Reward Systems
Tobler, P. N., O'Doherty, J. P., Dolan, R. J., & Schultz, W. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00745.2006 · Cited by 472 · see citing works
On Adaptation, Maximization, and Reinforcement Learning Among Cognitive Strategies.
Erev, I., & Barron, G. (2005) · Psychological Review · doi:10.1037/0033-295x.112.4.912 · Cited by 466 · see citing works
Memory integration: neural mechanisms and implications for behavior
Schlichting, M. L., & Preston, A. R. (2014) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2014.07.005 · Cited by 404 · see citing works
An Economist’s Perspective on Probability Matching
Vulkan, N. (2000) · Journal of Economic Surveys · doi:10.1111/1467-6419.00106 · Cited by 401 · see citing works
Multiplicative computation in a visual neuron sensitive to looming
Gabbiani, F., Krapp, H. G., Christof Koch, A., & Laurent, G. (2002) · Nature · doi:10.1038/nature01190 · Cited by 395 · see citing works
Dendritic Integration in Mammalian Neurons, a Century after Cajal
Yuste, R., & Tank, D. W. (1996) · Neuron · doi:10.1016/s0896-6273(00)80091-4 · Cited by 390 · see citing works
Context, learning, and extinction
Gershman, S. J., Blei, D. M., & Niv, Y. (2010) · Psychological Review · doi:10.1037/a0017808 · Cited by 386 · cited 2× · see citing works
“This inference can be concretely formulated in terms of Bayesian latent cause models (e.g., Gershman et al., 2010).”
BackgroundMethodSelf-citationLearning latent structure: carving nature at its joints
Gershman, S. J., & Niv, Y. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.02.008 · Cited by 326 · see citing works
Self-citationWhat constitutes an episode in episodic memory?
Ezzyat, Y., & Davachi, L. (2011) · Psychological Science · doi:10.1177/0956797610393742 · Cited by 298 · see citing works
Libby, L. A., Ekstrom, A. D., Ragland, J. D., & Ranganath, C. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.3711-11.2012 · Cited by 296 · see citing works
Gluck, M. A., Daphna Shohamy, A., & Myers, C. E. (2002) · Learning & Memory · doi:10.1101/lm.45202 · Cited by 265 · see citing works
The dynamics of memory: Context-dependent updatingPreprint
Hupbach, A., Hardt, O. M., Gómez, R. L., & Nadel, L. (2008) · Learning & Memory · doi:10.1101/lm.1022308 · Cited by 232 · see citing works
Auditory Spatial Receptive Fields Created by Multiplication
Peña, J. L., & Konishi, M. (2001) · Science · doi:10.1126/science.1059201 · Cited by 223 · see citing works
Spatial attention and the mental number line: Evidence for characteristic biases and compression
Longo, M. R., & Lourenco, S. F. (2006) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2006.11.002 · Cited by 201 · see citing works
Neural activity in the human brain relating to uncertainty and arousal during anticipation
Critchley, H., Mathiast, C. J., & Dolan, R. J. (2001) · NeuroImage · doi:10.1016/s1053-8119(01)91735-5 · Cited by 191 · see citing works
Differential Representations of Prior and Likelihood Uncertainty in the Human Brain
Vilares, I., Howard, J. D., Fernandes, H. L., Gottfried, J. A., & Kording, K. P. (2012) · Current Biology · doi:10.1016/j.cub.2012.07.010 · Cited by 165 · see citing works
Patterns across multiple memories are identified over time
Richards, B. A., Xia, F., Santoro, A., Husse, J., Woodin, M. A., Josselyn, S. A., & Frankland, P. W. (2014) · Nature Neuroscience · doi:10.1038/nn.3736 · Cited by 150 · see citing works
Model Uncertainty in Classical Conditioning
Courville, A. C., Gordon, G. J., Touretzky, D. S., & Daw, N. D. (2003) · Cited by 51 · see citing works
Ting, C.-C., Yu, C.-C., Maloney, L. T., & Wu, S. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.3161-14.2015 · Cited by 37 · see citing works
d’Acremont, M., Fornari, E., & Bossaerts, P. L. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002895 · Cited by 34 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Learning task-state representations
Niv, Y. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0470-8 · Cited by 356 · see citing works
Self-citationBayesian Brains without Probabilities
Sanborn, A. N., & Chater, N. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.10.003 · Cited by 307 · see citing works
Does mental context drift or shift?
DuBrow, S., Rouhani, N., Niv, Y., & Norman, K. A. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.08.003 · Cited by 118 · see citing works
Self-citation
- Authors
- Chan, S. C. Y., Niv, Y., & Norman, K. A.
- Affiliations
- Princeton University (US)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2016-07-27
- Volume
- 36
- Issue
- 30
- Pages
- 7817-7828
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC4961772
- OpenAlex
- W2952868242
- Cited by
- 97
- References
- 36
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 9 · 2023: 6 · 2022: 9 · 2021: 11 · 2020: 11 · 2019: 9 · 2018: 12 · 2017: 10 · 2016: 1
- FWCI
- 6.72
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (1009542)
- National Institutes of Health (IIS-1009542)
- National Institute of Mental Health (T32MH065214)
- Army Research Office (W911NF1410101)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- orbitofrontal cortex, posterior distribution, Bayesian inference, belief representation, state representation, schemas, episodic memory, reinforcement learning, decision-making, BOLD activation
- MeSH terms
- Action Potentials, Adolescent, Adult, Cognition, Computer Simulation, Decision Making, Female, Humans, Learning, Male, Models, Neurological, Reaction Time, Models, Statistical, Causality, Prefrontal Cortex, Young Adult
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
