Rational thoughts in neural codes
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Abstract
Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning to reach subjective goals. A fundamental challenge in neuroscience is, How can we use behavior and neural activity to understand this internal model and its dynamic latent variables? Here we interpret behavioral data by assuming an agent behaves rationally—that is, it takes actions that optimize its subjective reward according to its understanding of the task and its relevant causal variables. We apply a method, inverse rational control (IRC), to learn an agent’s internal model and reward function by maximizing the likelihood of its measured sensory observations and actions. This thereby extracts rational and interpretable thoughts of the agent from its behavior. We also provide a framework for interpreting encoding, recoding, and decoding of neural data in light of this rational model for behavior. When applied to behavioral and neural data from simulated agents performing suboptimally on a naturalistic foraging task, this method successfully recovers their internal model and reward function, as well as the Markovian computational dynamics within the neural manifold that represent the task. This work lays a foundation for discovering how the brain represents and computes with dynamic latent variables.
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Using an RNN resolves the problem of how to compute belief states, by replacing the finetuned Bayesian machinery needed for beliefs with a more general learned function approximator.
“From a theoretical perspective, using an RNN resolves the problem of how to compute belief states, by replacing the finetuned Bayesian machinery needed for beliefs with a more general learned function approximator (e.g., a recurrent neural network) [32,34,37].”
From Emergence of belief-like representations through reinforcement learning (2023) · cites this paper as [34]
Cites
Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588 · see citing works
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561 · see citing works
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471 · see citing works
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972 · see citing works
Reinforcement Learning: An Introduction
Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,821 · see citing works
Proceedings of the 25th international conference on Machine learning - ICML '08
(2008) · doi:10.1145/1390156 · Cited by 6,865 · see citing works
Image Style Transfer Using Convolutional Neural Networks
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.265 · Cited by 6,025 · see citing works
Marr, D. (1982) · Cumulative Index of Computer Aided Architectural Design · Cited by 5,733 · see citing works
Barlow, H. B. (1983) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(83)90030-5 · Cited by 5,502 · see citing works
Dynamic Programming and Markov Processes.
Freimer, M. L., & Howard, R. A. (1961) · Journal of the American Statistical Association · doi:10.2307/2282268 · Cited by 3,388 · see citing works
The economy of brain network organization
Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284 · see citing works
Artificial intelligence and statistics
Yu, B., & Kumbier, K. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700813 · Cited by 2,329 · see citing works
Feiner, N., Motone, F., Meyer, A., & Kuraku, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-40055-1 · Cited by 1,988 · see citing works
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1403112111 · Cited by 1,924 · see citing works
Context-dependent computation by recurrent dynamics in prefrontal cortex
Mante, V., Sussillo, D., Shenoy, K. V., & Newsome, W. T. (2013) · Nature · doi:10.1038/nature12742 · Cited by 1,807 · see citing works
Proceedings for the Annual Meeting of the Cognitive Science Society
Dziemianko, M., Clarke, A. D. F., & Keller, F. (2013) · Cited by 1,726 · see citing works
Proceedings of the 29th International Conference on Machine Learning (ICML-12)Preprint
Langford, J. C., & Pineau, J. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.4676 · Cited by 1,687 · see citing works
Neural population dynamics during reaching
Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I., & Shenoy, K. V. (2012) · Nature · doi:10.1038/nature11129 · Cited by 1,636 · see citing works
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Frankle, J., & Carbin, M. (2018) · arXiv · doi:10.48550/arxiv.1803.03635 · Cited by 1,529 · see citing works
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., & Brendel, W. (2018) · arXiv · doi:10.48550/arxiv.1811.12231 · Cited by 1,490 · see citing works
Hierarchical Bayesian inference in the visual cortex
Lee, T. S., & Mumford, D. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.001434 · Cited by 1,473 · 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
Universally Sloppy Parameter Sensitivities in Systems Biology Models
Gutenkunst, R. N., Waterfall, J. J., Casey, F. P., Brown, K. S., Myers, C. R., & Sethna, J. P. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030189 · Cited by 1,261 · see citing works
Kriegeskorte, N. (2015) · Annual Review of Vision Science · doi:10.1146/annurev-vision-082114-035447 · Cited by 1,093 · see citing works
Similar network activity from disparate circuit parameters
Prinz, A. A., Bucher, D., & Marder, E. (2004) · Nature Neuroscience · doi:10.1038/nn1352 · Cited by 1,091 · see citing works
Single-trial neural dynamics are dominated by richly varied movements
Musall, S., Kaufman, M. T., Juavinett, A., Gluf, S., & Churchland, A. K. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0502-4 · Cited by 1,057 · see citing works
Matching Behavior and the Representation of Value in the Parietal Cortex
Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2004) · Science · doi:10.1126/science.1094765 · Cited by 1,030 · see citing works
Texture Synthesis Using Convolutional Neural NetworksPreprint
Gatys, L. A., Ecker, A. S., & Bethge, M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.07376 · Cited by 902 · see citing works
Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment
Berkes, P., Orbán, G., Lengyel, M., & Fiser, J. (2011) · Science · doi:10.1126/science.1195870 · Cited by 782 · see citing works
Neural constraints on learning
Sadtler, P. T., Quick, K. M., Golub, M. D., Chase, S. M., Ryu, S. I., Tyler-Kabara, E. C., Yu, B. M., & Batista, A. P. (2014) · Nature · doi:10.1038/nature13665 · Cited by 713 · see citing works
Simon, H. A. (1990) · Utility and Probability · doi:10.1007/978-1-349-20568-4_5 · Cited by 705 · see citing works
Linking Spontaneous Activity of Single Cortical Neurons and the Underlying Functional Architecture
Tsodyks, M. V., Kenet, T., Grinvald, A., & Arieli, A. (1999) · Science · doi:10.1126/science.286.5446.1943 · Cited by 702 · see citing works
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines
Gershman, S. J., Horvitz, E., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aac6076 · Cited by 677 · see citing works
High-dimensional geometry of population responses in visual cortex
Stringer, C., Pachitariu, M., Steinmetz, N. A., Carandini, M., & Harris, K. D. (2019) · Nature · doi:10.1038/s41586-019-1346-5 · Cited by 591 · see citing works
Energy as a constraint on the coding and processing of sensory information
Laughlin, S. B. (2001) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(00)00237-3 · Cited by 571 · see citing works
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 541 · see citing works
Self-citationRational quantitative attribution of beliefs, desires and percepts in human mentalizing
Baker, C. L., Jara-Ettinger, J., Saxe, R., & Tenenbaum, J. B. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0064 · Cited by 515 · see citing works
Vul (2014) · doi:10.1111/cogs.12101 · Cited by 502 · see citing works
Causality for Machine Learning
Schölkopf, B. (2022) · ACM eBooks · doi:10.1145/3501714.3501755 · Cited by 442 · see citing works
On simplicity and complexity in the brave new world of large-scale neuroscience
Gao, P., & Ganguli, S. (2015) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.04.003 · Cited by 406 · see citing works
Cortical Areas Interact through a Communication Subspace
Semedo, J. D., Zandvakili, A., Machens, C. K., Yu, B. M., & Kohn, A. (2019) · Neuron · doi:10.1016/j.neuron.2019.01.026 · Cited by 383 · see citing works
Chaudhuri, R., Gerçek, B., Pandey, B., Peyrache, A., & Fiete, I. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0460-x · Cited by 367 · see citing works
Artificial Intelligence and StatisticsPreprint
Yu, B., & Kumbier, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.03779 · Cited by 356 · see citing works
Learning agents for uncertain environments (extended abstract)
Russell, S. (1998) · Conference on Learning Theory · doi:10.1145/279943.279964 · Cited by 298 · see citing works
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 · see citing works
Bayesian Theory of Mind: Modeling Joint Belief-Desire Attribution
Baker, C. L., Saxe, R. R., & Tenenbaum, J. B. (2011) · Cited by 199 · see citing works
Rao, R. P. N. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00146 · Cited by 190 · see citing works
Observing the observer (I): meta-bayesian models of learning and decision-making
Daunizeau, J., den Ouden, H. E. M., Pessiglione, M., Kiebel, S. J., Stephan, K. E., & Friston, K. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015554 · Cited by 167 · see citing works
Engineering a Less Artificial Intelligence
Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M., & Tolias, A. S. (2019) · Neuron · doi:10.1016/j.neuron.2019.08.034 · Cited by 164 · see citing works
Self-citationRecurrent Independent MechanismsPreprint
Goyal, A., Lamb, A., Hoffmann, J., Sodhani, S., Levine, S., Bengio, Y., & Schölkopf, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.10893 · Cited by 164 · see citing works
Shamir, M., & Sompolinsky, H. (2004) · Neural Computation · doi:10.1162/089976604773717559 · Cited by 140 · see citing works
Apprenticeship Learning About Multiple Intentions
Babes, M., Marivate, V. N., Subramanian, K. A., & Littman, M. L. (2011) · International Conference on Machine Learning · Cited by 136 · see citing works
Neuronal message passing using mean-field, bethe, and marginal approximations
Parr, T., Markovic, D., Kiebel, S. J., & Friston, K. J. (2019) · Scientific Reports · doi:10.1038/s41598-018-38246-3 · Cited by 128 · see citing works
Inverse Optimal Control with Linearly-Solvable MDPs
Dvijotham, K., & Todorov, E. (2010) · Cited by 116 · see citing works
Linear dynamical neural population models through nonlinear embeddingsPreprint
Gao, Y., Archer, E., Paninski, L., & Cunningham, J. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.08454 · Cited by 109 · see citing works
Visual Evidence Accumulation Guides Decision-Making in Unrestrained Mice
Odoemene, O., Pisupati, S., Nguyen, H., & Churchland, A. K. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.3478-17.2018 · Cited by 86 · see citing works
Glaze, C. M., Filipowicz, A. L. S., Kable, J. W., Balasubramanian, V., & Gold, J. I. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0297-4 · Cited by 80 · see citing works
Inverse Reinforcement Learning in Partially Observable Environments
Choi, J., & Kim, K.-E. (2009) · Cited by 78 · see citing works
Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback
Orhan, A. E., & Ma, W. J. (2017) · Nature Communications · doi:10.1038/s41467-017-00181-8 · Cited by 77 · see citing works
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 73 · see citing works
Self-citationCognitive Tomography Reveals Complex, Task-Independent Mental Representations
Houlsby, N., Huszár, F., Ghassemi, M. M., Orbán, G., Wolpert, D. M., & Lengyel, M. (2013) · Current Biology · doi:10.1016/j.cub.2013.09.012 · Cited by 54 · see citing works
Efficient Computation of the Hidden Markov Model Entropy for a Given Observation Sequence
Hernando, D., Crespi, V., & Cybenko, G. V. (2005) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.850223 · Cited by 51 · see citing works
Spatio-temporal Representations of Uncertainty in Spiking Neural Networks
Savin, C., & Denève, S. (2014) · Cited by 48 · see citing works
Inverse Reinforcement Learning in Partially Observable Environments
ChoiJaedeug, & KimKee-Eung (2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2021028 · Cited by 44 · see citing works
Learning probabilistic neural representations with randomly connected circuits
Maoz, O., Tkačik, G., Esteki, M. S., Kiani, R., & Schneidman, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912804117 · Cited by 44 · see citing works
What does it mean to understand a neural network?Preprint
Lillicrap, T., & Kording, K. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.06374 · Cited by 37 · see citing works
The quest for interpretable models of neural population activity
Whiteway, M. R., & Butts, D. A. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.07.004 · Cited by 35 · see citing works
Inferring Learners' Knowledge From Their Actions
Rafferty, A. N., LaMar, M., & Griffiths, T. L. (2014) · Cognitive Science · doi:10.1111/cogs.12157 · Cited by 32 · see citing works
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and DynamicsPreprint
Herman, M., Gindele, T., Wagner, J., Schmitt, F., & Burgard, W. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.03912 · Cited by 25 · see citing works
Revealing nonlinear neural decoding by analyzing choices
Yang, Q., Walker, E. Y., Cotton, R. J., Tolias, A. S., & Pitkow, X. (2021) · Nature Communications · doi:10.1038/s41467-021-26793-9 · Cited by 25 · see citing works
Self-citationFlexible and accurate inference and learning for deep generative modelsPreprint
Vértes, E., & Sahani, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.11051 · Cited by 24 · see citing works
Single-trial neural dynamics are dominated by richly varied movements: dataset
Musall, S., Kaufman, M. T., Juavinett, A., Gluf, S., & Churchland, A. K. (2019) · doi:10.14224/1.38599 · Cited by 21 · see citing works
Inference by Reparameterization in Neural Population CodesPreprint
Raju, R. V., & Pitkow, X. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06544 · Cited by 20 · see citing works
Self-citationInverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
Kwon, M., Daptardar, S., Schrater, P., & Pitkow, X. (2020) · PubMed · doi:10.48550/arxiv.2009.12576 · Cited by 18 · see citing works
Self-citationInferring the function performed by a recurrent neural network
Chalk, M., Tkačik, G., & Marre, O. (2021) · PLoS ONE · doi:10.1371/journal.pone.0248940 · Cited by 11 · see citing works
The population dynamics of a canonical cognitive circuitPreprint
Chaudhuri, R., Gerçek, B., Pandey, B., Peyrache, A., & Fiete, I. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/516021 · Cited by 10 · see citing works
Inverse Rational Control with Partially Observable Continuous Nonlinear DynamicsPreprint
Daptardar, S., Schrater, P., & Pitkow, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.04696 · Cited by 9 · see citing works
Self-citationI See What You See: Inferring Sensor and Policy Models of Human Real-World Motor Behavior
Schmitt, F., Bieg, H., Herman, M., & Rothkopf, C. A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11049 · Cited by 8 · see citing works
A Bayesian framework for modeling confidence in perceptual decision making
Khalvati, K., & Rao, R. P. N. (2015) · neural information processing systems · Cited by 7 · see citing works
Revealing nonlinear neural decoding by analyzing choicesPreprint
Yang, Q., Walker, E. Y., Cotton, R. J., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/332353 · Cited by 5 · see citing works
Compressive neural representation of sparse, high-dimensional probabilitiesPreprint
Pitkow, Z. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.1800 · Cited by 5 · see citing works
Learning probabilistic representations with randomly connected neural circuitsPreprint
Maoz, O., Tkacčik, G., Esteki, M. S., Kiani, R., & Schneidman, E. (2018) · bioRxiv · doi:10.1101/478545 · Cited by 4 · see citing works
Plato (2016) · Democracy · doi:10.7312/blau17412-051 · Cited by 3 · see citing works
Flagged first, then by citations
5 of the 93 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Mattar, M. G., & Lengyel, M. (2022) · Neuron · doi:10.1016/j.neuron.2021.12.018 · Cited by 113 · see citing works
Beyond simple laboratory studies: Developing sophisticated models to study rich behavior
Maselli, A., Gordon, J. R., Eluchans, M., Lancia, G. L., Thiery, T., Moretti, R., Cisek, P., & Pezzulo, G. (2023) · Physics of Life Reviews · doi:10.1016/j.plrev.2023.07.006 · Cited by 94 · see citing works
Kessler, F., Frankenstein, J., & Rothkopf, C. A. (2024) · Nature Communications · doi:10.1038/s41467-024-49722-y · Cited by 49 · see citing works
Lakshminarasimhan, K. J., Avila, E., Neyhart, E., DeAngelis, G. C., Pitkow, X., & Angelaki, D. E. (2020) · Neuron · doi:10.1016/j.neuron.2020.02.023 · Cited by 48 · see citing works
Self-citationEmergence of belief-like representations through reinforcement learning
Hennig, J. A., Romero Pinto, S. A., Yamaguchi, T., Linderman, S. W., Uchida, N., & Gershman, S. J. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011067 · Cited by 41 · see citing works
Major, J. C. (2021) · Biosystems · doi:10.1016/j.biosystems.2021.104501 · Cited by 32 · see citing works
Sensory Evidence Accumulation Using Optic Flow in a Naturalistic Navigation Task
Alefantis, P., Lakshminarasimhan, K. J., Avila, E., Noel, J., Pitkow, X., & Angelaki, D. E. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.2203-21.2022 · Cited by 32 · see citing works
Self-citationPopulation coding of strategic variables during foraging in freely moving macaques
Shahidi, N., Franch, M. C., Parajuli, A., Schrater, P., Wright, A. A., Pitkow, X., & Dragoi, V. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01575-w · Cited by 30 · see citing works
Self-citationWang, H., Wang, W., Yuan, S., & Li, X. (2021) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2021.3103179 · Cited by 20 · see citing works
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
Kwon, M., Daptardar, S., Schrater, P., & Pitkow, X. (2020) · PubMed · doi:10.48550/arxiv.2009.12576 · Cited by 18 · see citing works
Self-citationDynamical latent state computation in the male macaque posterior parietal cortex
Lakshminarasimhan, K. J., Avila, E., Pitkow, X., & Angelaki, D. E. (2023) · Nature Communications · doi:10.1038/s41467-023-37400-4 · Cited by 17 · see citing works
Self-citationHierarchical control over foraging behavior by anterior cingulate cortex
Alejandro, R. J., & Holroyd, C. B. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105623 · Cited by 17 · see citing works
Noel, J., Caziot, B., Bruni, S., Fitzgerald, N., Avila, E., & Angelaki, D. E. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.101996 · Cited by 13 · see citing works
Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System
Schultheis, M., Straub, D., & Rothkopf, C. A. (2021) · TUbilio (Technical University of Darmstadt) · doi:10.17863/cam.125526 · Cited by 12 · see citing works
Modelling dataset bias in machine-learned theories of economic decision-making
Thomas, T., Straub, D., Tatai, F., Shene, M., Tosik, T., Kersting, K., & Rothkopf, C. A. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01784-6 · Cited by 11 · see citing works
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Shiffrin, R. M., Bassett, D. S., Kriegeskorte, N., & Tenenbaum, J. B. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912340117 · Cited by 10 · see citing works
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Urbaniak, R., Xie, M., & Mackevicius, E. L. (2024) · Scientific Reports · doi:10.1038/s41598-024-71931-0 · Cited by 4 · see citing works
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A naturalistic argument against libertarian free will scepticism
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Alefantis, P., Guo, Y., Quazi, N., Savin, C., Angelaki, D. E., Pitkow, X., & Majaj, N. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.14.718537
Self-citationVisual uncertainty and task demands shape active sensing strategies in mice
Benquet, C., Sainsbury, T. T. J., Bruneau, L., Lin, Y., Cai, C., Popova, M., Ponder, K., Ntanavara, L., Froebe, R., Tan, Z., et al. (2026) · Current Biology · doi:10.1016/j.cub.2026.06.011
Self-citationRationalizing sensorimotor deficits in autism spectrum disorderPreprint
Chen, Y., Noel, J., Schrater, P., Angelaki, D. E., & Pitkow, X. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.12.758517
Self-citation
- Authors
- Wu, Z., Kwon, M., Daptardar, S., Schrater, P., & Pitkow, X.
- Affiliations
- Baylor College of Medicine (US)
- Google (United States) (US)
- University of Minnesota (US)
- Corresponding author
- Pitkow, Xaq
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2020-11-23
- Volume
- 117
- Issue
- 47
- Pages
- 29311-29320
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC7703629
- OpenAlex
- W3107207547
- Cited by
- 27
- References
- 93
- Citations by year
- 2026: 6 · 2024: 7 · 2023: 3 · 2022: 2 · 2021: 5 · 2020: 3 · 2019: 1
- FWCI
- 1.75
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- HHS | National Institutes of Health (5U01NS094368)
- Simons Foundation (SCGB 324143)
- National Science Foundation (1450923 BRAIN 43092, CAREER Award IOS-1552868)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- internal models, reward function estimation, latent variables, neural coding, neural decoding, foraging task, computational dynamics, rational action, neural manifolds
- MeSH terms
- Animals, Brain, Cognition, Computer Simulation, Decision Making, Feeding Behavior, Markov Chains, Models, Psychological, Rationalization, Reward, Neural Networks, Computer, Uncertainty
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