{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T05:52:30Z","timestamp":1781502750843,"version":"3.54.1"},"reference-count":164,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Minds &amp; Machines"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Biological and artificial agents operating in complex environments have to leverage environmental structures to accomplish vital tasks. Recent research across a variety of domains\u2014from the study of animal and human behaviour in different developmental periods and for different tasks, to computational studies of learning\u2014has unveiled many ways in which structures are processed. This gave rise to a burgeoning field of study\u2014structure learning. However, the diversity of phenomena studied, and the different aims and focuses of the researchers, have led to ambiguity and limited consensus on the nature of structure learning and its underlying mechanisms. In this paper we provide a synopsis of illustrative examples of structure learning, introduce the Active Inference Framework (AIF) with a focus on Structure Learning, and discuss points of contact between the two. The Active Inference Framework provides a mechanistic theory which distinguishes three levels of learning: Active Inference, Parametric Learning, and Bayesian Model Selection (a.k.a., Structure Learning), a method for the comparison and selection of models based on model evidence. We argue that when formalised under the Active Inference Framework, Structure Learning provides not only an underlying computational mechanism with aims of ecological validity, but also provides features relevant to computational accounts of structure learning more generally. The unifying aspect of the AIF in terms of having a single objective function for optimising behaviour should not be confounded with the exclusivity of this framework. The integration with other computational accounts is advised.<\/jats:p>","DOI":"10.1007\/s11023-026-09787-8","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T03:57:19Z","timestamp":1781495839000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reviewing Structure Learning in and Out of the Active Inference Framework"],"prefix":"10.1007","volume":"36","author":[{"given":"Victorita","family":"Neacsu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wing Yi","family":"So","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danaja","family":"Rutar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lancelot","family":"da Costa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rick A.","family":"Adams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karl J.","family":"Friston","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"issue":"1","key":"9787_CR1","first-page":"1","volume":"1","author":"J Albus","year":"2008","unstructured":"Albus, J. (2008). Toward a computational theory of mind. Journal of Mind Theory, 1(1), 1\u201338.","journal-title":"Journal of Mind Theory"},{"key":"9787_CR2","doi-asserted-by":"crossref","unstructured":"Arnold, C. (2017). Jellyfish caught snoozing give clues to origin of sleep. Nature.","DOI":"10.1038\/nature.2017.22654"},{"key":"9787_CR3","first-page":"167","volume":"254","author":"S Baek","year":"2020","unstructured":"Baek, S., Jaffe-Dax, S., Emberson, L. L., Hunnius, S., & Meyer, M. (2020). Elsevier 254: 167\u2013186.","journal-title":"Elsevier"},{"key":"9787_CR4","doi-asserted-by":"publisher","first-page":"907","DOI":"10.3389\/fpsyg.2013.00907","volume":"4","author":"A Barto","year":"2013","unstructured":"Barto, A., Mirolli, M., & Baldassarre, G. (2013). Novelty or surprise? Frontiers in psychology, 4, 907.","journal-title":"Frontiers in psychology"},{"issue":"2","key":"9787_CR5","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1016\/j.neuron.2018.10.002","volume":"100","author":"TE Behrens","year":"2018","unstructured":"Behrens, T. E., Muller, T. H., Whittington, J. C., Mark, S., Baram, A. B., Stachenfeld, K. L., & Kurth-Nelson, Z. (2018). What is a cognitive map? Organizing knowledge for flexible behavior. Neuron, 100(2), 490\u2013509.","journal-title":"Neuron"},{"key":"9787_CR6","first-page":"3","volume":"17","author":"K Blischke","year":"2007","unstructured":"Blischke, K., & Erlacher, D. (2007). How sleep enhances motor learning-a review. Journal of human kinetics, 17, 3.","journal-title":"Journal of human kinetics"},{"issue":"8","key":"9787_CR7","doi-asserted-by":"publisher","first-page":"1442","DOI":"10.1037\/xlm0000824","volume":"46","author":"CR Bowman","year":"2020","unstructured":"Bowman, C. R., & Zeithamova, D. (2020). Training set coherence and set size effects on concept generalization and recognition. J Exp Psychol Learn Mem Cogn, 46(8), 1442\u20131464.","journal-title":"J Exp Psychol Learn Mem Cogn"},{"issue":"20","key":"9787_CR8","doi-asserted-by":"publisher","first-page":"6472","DOI":"10.1523\/JNEUROSCI.3075-08.2009","volume":"29","author":"DA Braun","year":"2009","unstructured":"Braun, D. A., Aertsen, A., Wolpert, D. M., & Mehring, C. (2009a). Learning optimal adaptation strategies in unpredictable motor tasks. Journal of Neuroscience, 29(20), 6472\u20136478.","journal-title":"Journal of Neuroscience"},{"issue":"4","key":"9787_CR9","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1016\/j.cub.2009.01.036","volume":"19","author":"DA Braun","year":"2009","unstructured":"Braun, D. A., Aertsen, A., Wolpert, D. M., & Mehring, C. (2009b). Motor task variation induces structural learning. Current Biology, 19(4), 352\u2013357.","journal-title":"Current Biology"},{"issue":"2","key":"9787_CR10","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.bbr.2009.08.031","volume":"206","author":"DA Braun","year":"2010","unstructured":"Braun, D. A., Mehring, C., & Wolpert, D. M. (2010). Structure learning in action. Behavioural brain research, 206(2), 157\u2013165.","journal-title":"Behavioural brain research"},{"key":"9787_CR11","doi-asserted-by":"crossref","unstructured":"Brown, T. H., Zhao, Y., & Leung, V. (2009). Hebbian Plasticity. Encyclopedia of Neuroscience. L. R. Squire (pp. 1049\u20131056). Academic.","DOI":"10.1016\/B978-008045046-9.00796-8"},{"key":"9787_CR12","doi-asserted-by":"crossref","unstructured":"Bruner, J. S., Goodnow, J. J., & Austin, G. A. (1956). A study of thinking. Wiley.","DOI":"10.2307\/1292061"},{"issue":"10","key":"9787_CR13","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1002\/hipo.22488","volume":"25","author":"G Buzs\u00e1ki","year":"2015","unstructured":"Buzs\u00e1ki, G. (2015). Hippocampal sharp wave-ripple: A cognitive biomarker for episodic memory and planning. Hippocampus, 25(10), 1073\u20131188.","journal-title":"Hippocampus"},{"key":"9787_CR14","unstructured":"Carey, S. (2011). The Origin of Concepts. Oxford University Press."},{"key":"9787_CR15","unstructured":"Chollet, F. (2019). On the measure of intelligence. arXiv preprint arXiv:1911.01547."},{"issue":"8","key":"9787_CR16","doi-asserted-by":"publisher","first-page":"e216","DOI":"10.1371\/journal.pbio.0060216","volume":"6","author":"C Cirelli","year":"2008","unstructured":"Cirelli, C., & Tononi, G. (2008). Is sleep essential? PLoS biology 6(8): e216.","journal-title":"Is sleep essential? PLoS biology"},{"key":"9787_CR17","doi-asserted-by":"crossref","unstructured":"Clark, A. (2015). Surfing uncertainty: Prediction, action, and the embodied mind. Oxford University Press.","DOI":"10.1093\/acprof:oso\/9780190217013.001.0001"},{"key":"9787_CR18","doi-asserted-by":"publisher","first-page":"679","DOI":"10.3389\/fpsyg.2019.00679","volume":"10","author":"A Constant","year":"2019","unstructured":"Constant, A., Ramstead, M. J. D., Veissi\u00e8re, S. P. L., & Friston, K. (2019). Regimes of expectations: an active inference model of social conformity and human decision making. Frontiers in Psychology, 10, 679.","journal-title":"Frontiers in Psychology"},{"issue":"8","key":"9787_CR19","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1038\/s41583-019-0189-2","volume":"20","author":"J Cox","year":"2019","unstructured":"Cox, J., & Witten, I. B. (2019). Striatal circuits for reward learning and decision-making. Nature Reviews Neuroscience, 20(8), 482\u2013494.","journal-title":"Nature Reviews Neuroscience"},{"key":"9787_CR96","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1016\/j.neuroimage.2017.12.016","volume":"185","author":"C D Monroy","year":"2019","unstructured":"D Monroy, C., Meyer, M., Schr\u00f6er, L., A Gerson, S., & Hunnius, S. (2019b). The infant motor system predicts actions based on visual statistical learning. Neuroimage, 185, 947\u2013954.","journal-title":"Neuroimage"},{"key":"9787_CR21","doi-asserted-by":"publisher","first-page":"102447","DOI":"10.1016\/j.jmp.2020.102447","volume":"99","author":"L Da Costa","year":"2020","unstructured":"Da Costa, L., Parr, T., Sajid, N., Veselic, S., Neacsu, V., & Friston, K. (2020). Active inference on discrete state-spaces: A synthesis. Journal of Mathematical Psychology, 99, 102447.","journal-title":"Journal of Mathematical Psychology"},{"key":"9787_CR20","doi-asserted-by":"crossref","unstructured":"Da Costa, L., Gaven\u010diak, T., Hyland, D., Samiei, M., Dragos-Manta, C., Pattisapu, C., Razi, A., & Friston, K. (2024). Possible principles for aligned structure learning agents. arXiv preprint arXiv:2410.00258.","DOI":"10.1162\/NECO.a.39"},{"key":"9787_CR22","first-page":"103","volume":"3","author":"P Dayan","year":"2002","unstructured":"Dayan, P., & Watkins, C. J. (2002). Reinforcement learning. Stevens\u2019 handbook of experimental psychology, 3, 103\u2013129.","journal-title":"Stevens\u2019 handbook of experimental psychology"},{"issue":"49","key":"9787_CR23","doi-asserted-by":"publisher","first-page":"19373","DOI":"10.1523\/JNEUROSCI.0414-13.2013","volume":"33","author":"L Deuker","year":"2013","unstructured":"Deuker, L., Olligs, J., Fell, J., Kranz, T. A., Mormann, F., Montag, C., Reuter, M., Elger, C. E., & Axmacher, N. (2013). Memory consolidation by replay of stimulus-specific neural activity. Journal of Neuroscience, 33(49), 19373\u201319383.","journal-title":"Journal of Neuroscience"},{"key":"9787_CR24","doi-asserted-by":"publisher","first-page":"315","DOI":"10.3389\/fnhum.2016.00315","volume":"10","author":"F Di Rienzo","year":"2016","unstructured":"Di Rienzo, F., Debarnot, U., Daligault, S., Saruco, E., Delpuech, C., Doyon, J., Collet, C., & Guillot, A. (2016). Online and offline performance gains following motor imagery practice: a comprehensive review of behavioral and neuroimaging studies. Frontiers in human neuroscience, 10, 315.","journal-title":"Frontiers in human neuroscience"},{"issue":"27","key":"9787_CR25","doi-asserted-by":"publisher","first-page":"9032","DOI":"10.1523\/JNEUROSCI.23-27-09032.2003","volume":"23","author":"O Donchin","year":"2003","unstructured":"Donchin, O., Francis, J. T., & Shadmehr, R. (2003). Quantifying generalization from trial-by-trial behavior of adaptive systems that learn with basis functions: theory and experiments in human motor control. Journal of Neuroscience, 23(27), 9032\u20139045.","journal-title":"Journal of Neuroscience"},{"issue":"2","key":"9787_CR26","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.conb.2005.03.004","volume":"15","author":"J Doyon","year":"2005","unstructured":"Doyon, J., & Benali, H. (2005). Reorganization and plasticity in the adult brain during learning of motor skills. Current opinion in neurobiology, 15(2), 161\u2013167.","journal-title":"Current opinion in neurobiology"},{"key":"9787_CR27","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1146\/annurev-statistics-060116-053803","volume":"4","author":"M Drton","year":"2017","unstructured":"Drton, M., & Maathuis, M. H. (2017). Structure learning in graphical modeling. Annual Review of Statistics and Its Application, 4, 365\u2013393.","journal-title":"Annual Review of Statistics and Its Application"},{"issue":"2251","key":"9787_CR28","doi-asserted-by":"publisher","first-page":"20220050","DOI":"10.1098\/rsta.2022.0050","volume":"381","author":"K Ellis","year":"2023","unstructured":"Ellis, K., Wong, L., Nye, M., Sable-Meyer, M., Cary, L., Anaya Pozo, L., Hewitt, L., Solar-Lezama, A., & Tenenbaum, J. B. (2023). DreamCoder: growing generalizable, interpretable knowledge with wake\u2013sleep Bayesian program learning. Philosophical Transactions of the Royal Society A, 381(2251), 20220050.","journal-title":"Philosophical Transactions of the Royal Society A"},{"issue":"31","key":"9787_CR30","doi-asserted-by":"publisher","first-page":"9585","DOI":"10.1073\/pnas.1510343112","volume":"112","author":"LL Emberson","year":"2015","unstructured":"Emberson, L. L., Richards, J. E., & Aslin, R. N. (2015). Top-down modulation in the infant brain: Learning-induced expectations rapidly affect the sensory cortex at 6 months. Proceedings of the National Academy of Sciences, 112(31), 9585\u20139590.","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"6","key":"9787_CR29","doi-asserted-by":"publisher","first-page":"e12847","DOI":"10.1111\/desc.12847","volume":"22","author":"LL Emberson","year":"2019","unstructured":"Emberson, L. L., Misyak, J. B., Schwade, J. A., Christiansen, M. H., & Goldstein, M. H. (2019). Comparing statistical learning across perceptual modalities in infancy: An investigation of underlying learning mechanism (s). Developmental science, 22(6), e12847.","journal-title":"Developmental science"},{"key":"9787_CR31","doi-asserted-by":"crossref","unstructured":"Erdmann, T., & Mathys, C. (2021). Rule Learning Through Active Inductive Inference. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer.","DOI":"10.1007\/978-3-030-93736-2_51"},{"key":"9787_CR32","unstructured":"Evans, T., & Burgess, N. (2019). Coordinated hippocampal-entorhinal replay as structural inference. Advances in Neural Information Processing Systems 32."},{"key":"9787_CR33","unstructured":"Eysenbach, B., Salakhutdinov, R. R., & Levine, S. (2019). Search on the replay buffer: Bridging planning and reinforcement learning. Advances in Neural Information Processing Systems 32."},{"issue":"4","key":"9787_CR34","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1080\/20445911.2013.779248","volume":"25","author":"JI Fleck","year":"2013","unstructured":"Fleck, J. I., & Weisberg, R. W. (2013). Insight versus analysis: Evidence for diverse methods in problem solving. Journal of Cognitive Psychology, 25(4), 436\u2013463.","journal-title":"Journal of Cognitive Psychology"},{"issue":"2","key":"9787_CR35","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1038\/nrn2787","volume":"11","author":"K Friston","year":"2010","unstructured":"Friston, K. (2010). The free-energy principle: a unified brain theory? Nature reviews neuroscience, 11(2), 127\u2013138.","journal-title":"Nature reviews neuroscience"},{"issue":"7","key":"9787_CR36","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1016\/j.tics.2016.05.001","volume":"20","author":"K Friston","year":"2016","unstructured":"Friston, K., & Buzs\u00e1ki, G. (2016). The Functional Anatomy of Time: What and When in the Brain. Trends In Cognitive Sciences, 20(7), 500\u2013511.","journal-title":"Trends In Cognitive Sciences"},{"issue":"4","key":"9787_CR41","first-page":"2089","volume":"56","author":"K Friston","year":"2011","unstructured":"Friston, K., & Penny, W. (2011). Post hoc Bayesian model selection Neuroimage 56(4): 2089\u20132099.","journal-title":"Post hoc Bayesian model selection Neuroimage"},{"issue":"1","key":"9787_CR39","first-page":"220","volume":"34","author":"K Friston","year":"2007","unstructured":"Friston, K., Mattout, J., Trujillo-Barreto, N., Ashburner, J., & Penny, W. (2007). Variational free energy and the Laplace approximation NeuroImage 34(1): 220\u2013234.","journal-title":"Variational free energy and the Laplace approximation NeuroImage"},{"key":"9787_CR43","doi-asserted-by":"publisher","first-page":"598","DOI":"10.3389\/fnhum.2013.00598","volume":"7","author":"K Friston","year":"2013","unstructured":"Friston, K., Schwartenbeck, P., Fitzgerald, T., Moutoussis, M., Behrens, T., & Dolan, R. (2013). The anatomy of choice: active inference and agency. Frontiers in Human Neuroscience, 7, 598.","journal-title":"Frontiers in Human Neuroscience"},{"issue":"4","key":"9787_CR42","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1080\/17588928.2015.1020053","volume":"6","author":"K Friston","year":"2015","unstructured":"Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015). Active inference and epistemic value. Cognitive neuroscience, 6(4), 187\u2013214.","journal-title":"Cognitive neuroscience"},{"key":"9787_CR37","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1016\/j.neubiorev.2016.06.022","volume":"68","author":"K Friston","year":"2016","unstructured":"Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O. D. J and, & Pezzulo, G. (2016). Active inference and learning. Neuroscience And Biobehavioral Reviews, 68, 862\u2013879.","journal-title":"Neuroscience And Biobehavioral Reviews"},{"issue":"1","key":"9787_CR38","first-page":"1","volume":"29","author":"K Friston","year":"2017","unstructured":"Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2017a). Active Inference: A Process Theory Neural Computation 29(1): 1\u201349.","journal-title":"Active Inference: A Process Theory Neural Computation"},{"issue":"10","key":"9787_CR45","first-page":"2633","volume":"29","author":"KJ Friston","year":"2017","unstructured":"Friston, K. J., Lin, M., Frith, C. D., Pezzulo, G., Hobson, J. A., & Ondobaka, S. (2017b). Active Inference Curiosity and Insight Neural Computation 29(10): 2633\u20132683.","journal-title":"Active Inference Curiosity and Insight Neural Computation"},{"key":"9787_CR46","unstructured":"Friston, K. J., Parr, T., & Zeidman, P. (2018). Bayesian model reduction. arXiv: Methodology."},{"key":"9787_CR40","first-page":"573","volume":"144","author":"K Friston","year":"2021","unstructured":"Friston, K., Moran, R. J., Nagai, Y., Taniguchi, T., Gomi, H., & Tenenbaum, J. (2021). World model learning and inference Neural Networks 144: 573\u2013590.","journal-title":"World model learning and inference Neural Networks"},{"key":"9787_CR44","doi-asserted-by":"crossref","unstructured":"Friston, K. J., Da Costa, L., Tschantz, A., Kiefer, A., Salvatori, T., Neacsu, V., Koudahl, M., Heins, C., Sajid, N., & Markovic, D. (2023). Supervised structure learning. arXiv preprint arXiv:2311.10300.","DOI":"10.1016\/j.biopsycho.2024.108891"},{"key":"9787_CR47","first-page":"141","volume":"34","author":"D Geeraerts","year":"2006","unstructured":"Geeraerts, D. (2006). Prototype theory. Cognitive linguistics. Basic readings, 34, 141\u2013165.","journal-title":"Basic readings"},{"issue":"11","key":"9787_CR48","doi-asserted-by":"publisher","first-page":"e1004567","DOI":"10.1371\/journal.pcbi.1004567","volume":"11","author":"SJ Gershman","year":"2015","unstructured":"Gershman, S. J. (2015). A unifying probabilistic view of associative learning. PLoS computational biology, 11(11), e1004567.","journal-title":"PLoS computational biology"},{"key":"9787_CR49","doi-asserted-by":"publisher","first-page":"105324","DOI":"10.1016\/j.cognition.2022.105324","volume":"231","author":"T Ghilardi","year":"2023","unstructured":"Ghilardi, T., Meyer, M., & Hunnius, S. (2023). Predictive motor activation: Modulated by expectancy or predictability? Cognition, 231, 105324.","journal-title":"Cognition"},{"issue":"3","key":"9787_CR50","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1111\/j.1750-8606.2011.00179.x","volume":"5","author":"A Gopnik","year":"2011","unstructured":"Gopnik, A. (2011). The Theory Theory 2.0: Probabilistic Models and Cognitive Development. Child Development Perspectives, 5(3), 161\u2013163.","journal-title":"Child Development Perspectives"},{"issue":"1","key":"9787_CR51","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1037\/0033-295X.111.1.3","volume":"111","author":"A Gopnik","year":"2004","unstructured":"Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T., & Danks, D. (2004). A theory of causal learning in children: causal maps and Bayes nets. Psychological review, 111(1), 3.","journal-title":"Psychological review"},{"key":"9787_CR52","doi-asserted-by":"crossref","unstructured":"Gopnik, A., Schulz, L., & Schulz, L. E. (2007). Causal learning: Psychology, philosophy, and computation. Oxford University Press.","DOI":"10.1093\/acprof:oso\/9780195176803.001.0001"},{"issue":"9","key":"9787_CR54","first-page":"767","volume":"17","author":"TL Griffiths","year":"2006","unstructured":"Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal Predictions in Everyday Cognition Psychological Science 17(9): 767\u2013773.","journal-title":"Optimal Predictions in Everyday Cognition Psychological Science"},{"key":"9787_CR53","doi-asserted-by":"crossref","unstructured":"Griffiths, T. L., Sanborn, A. N., Canini, K. R., Navarro, D. J., & Tenenbaum, J. B. (2011). Nonparametric Bayesian models of categorization. Formal approaches in categorization, 173\u2013198.","DOI":"10.1017\/CBO9780511921322.008"},{"issue":"5","key":"9787_CR55","first-page":"1110","volume":"89","author":"MJ Gruber","year":"2016","unstructured":"Gruber, M. J., Ritchey, M., Wang, S. F., Doss, M. K., & Ranganath, C. (2016). Post-learning hippocampal dynamics promote preferential retention of rewarding events Neuron 89(5): 1110\u20131120.","journal-title":"Post-learning hippocampal dynamics promote preferential retention of rewarding events Neuron"},{"key":"9787_CR56","unstructured":"Gupta, A., Mendonca, R., Liu, Y., Abbeel, P., & Levine, S. (2018). Meta-reinforcement learning of structured exploration strategies. Advances in neural information processing systems, 31."},{"issue":"1","key":"9787_CR57","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1037\/h0062474","volume":"56","author":"HF Harlow","year":"1949","unstructured":"Harlow, H. F. (1949). The formation of learning sets. Psychological review, 56(1), 51.","journal-title":"Psychological review"},{"issue":"1","key":"9787_CR58","doi-asserted-by":"publisher","first-page":"5320","DOI":"10.1038\/s41467-023-41027-w","volume":"14","author":"WJ Harrison","year":"2023","unstructured":"Harrison, W. J., Bays, P. M., & Rideaux, R. (2023). Neural tuning instantiates prior expectations in the human visual system. Nature Communications, 14(1), 5320.","journal-title":"Nature Communications"},{"issue":"2","key":"9787_CR59","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1162\/neco_a_01341","volume":"33","author":"C Hesp","year":"2021","unstructured":"Hesp, C., Smith, R., Parr, T., Allen, M., Friston, K. J., & Ramstead, M. J. D. (2021). Deeply felt affect: the emergence of valence in deep active inference. Neural Computation, 33(2), 398\u2013446.","journal-title":"Neural Computation"},{"issue":"6","key":"9787_CR60","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1016\/j.brainresbull.2008.02.024","volume":"76","author":"S Hill","year":"2008","unstructured":"Hill, S., Tononi, G., & Ghilardi, M. F. (2008). Sleep improves the variability of motor performance. Brain research bulletin, 76(6), 605\u2013611.","journal-title":"Brain research bulletin"},{"issue":"5214","key":"9787_CR61","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1126\/science.7761831","volume":"268","author":"GE Hinton","year":"1995","unstructured":"Hinton, G. E., Dayan, P., Frey, B. J., & Neal, R. M. (1995). The wake-sleep algorithm for unsupervised neural networks. Science, 268(5214), 1158\u20131161.","journal-title":"Science"},{"issue":"1","key":"9787_CR62","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.pneurobio.2012.05.003","volume":"98","author":"JA Hobson","year":"2012","unstructured":"Hobson, J. A., & Friston, K. J. (2012). Waking and dreaming consciousness: neurobiological and functional considerations. Progress in neurobiology, 98(1), 82\u201398.","journal-title":"Progress in neurobiology"},{"issue":"1","key":"9787_CR63","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1176\/appi.prcp.20200023","volume":"3","author":"JA Hobson","year":"2021","unstructured":"Hobson, J. A., Gott, J. A., & Friston, K. J. (2021). Minds and brains, sleep and psychiatry. Psychiatric Research and Clinical Practice, 3(1), 12\u201328.","journal-title":"Psychiatric Research and Clinical Practice"},{"issue":"2","key":"9787_CR64","first-page":"259","volume":"50","author":"J Hohwy","year":"2016","unstructured":"Hohwy, J. (2016). The Self-Evidencing Brain No\u00fbs 50(2): 259\u2013285.","journal-title":"The Self-Evidencing Brain No\u00fbs"},{"key":"9787_CR65","doi-asserted-by":"crossref","unstructured":"Hu, S., Ma, Y., Liu, X., Wei, Y., & Bai, S. (2021). Stratified rule-aware network for abstract visual reasoning. In: Proceedings of the AAAI Conference on Artificial Intelligence.","DOI":"10.1609\/aaai.v35i2.16248"},{"issue":"6995","key":"9787_CR66","first-page":"78","volume":"430","author":"R Huber","year":"2004","unstructured":"Huber, R., Felice Ghilardi, M., Massimini, M., & Tononi, G. (2004). Local sleep and learning Nature 430(6995): 78\u201381.","journal-title":"Local sleep and learning Nature"},{"issue":"6","key":"9787_CR67","doi-asserted-by":"publisher","first-page":"4483","DOI":"10.1007\/s10462-021-10004-4","volume":"54","author":"M Huisman","year":"2021","unstructured":"Huisman, M., Van Rijn, J. N., & Plaat, A. (2021). A survey of deep meta-learning. Artificial Intelligence Review, 54(6), 4483\u20134541.","journal-title":"Artificial Intelligence Review"},{"key":"9787_CR68","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.neures.2021.12.003","volume":"175","author":"T Isomura","year":"2022","unstructured":"Isomura, T. (2022). Active inference leads to Bayesian neurophysiology. Neuroscience Research, 175, 38\u201345.","journal-title":"Neuroscience Research"},{"issue":"1","key":"9787_CR69","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/s42003-021-02994-2","volume":"5","author":"T Isomura","year":"2022","unstructured":"Isomura, T., Shimazaki, H., & Friston, K. J. (2022). Canonical neural networks perform active inference. Communications Biology, 5(1), 55.","journal-title":"Communications Biology"},{"key":"9787_CR70","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1007\/s00500-012-0966-6","volume":"17","author":"J Ji","year":"2013","unstructured":"Ji, J., Wei, H., & Liu, C. (2013). An artificial bee colony algorithm for learning Bayesian networks. Soft Computing, 17, 983\u2013994.","journal-title":"Soft Computing"},{"issue":"4","key":"9787_CR71","doi-asserted-by":"publisher","first-page":"e97","DOI":"10.1371\/journal.pbio.0020097","volume":"2","author":"M Jung-Beeman","year":"2004","unstructured":"Jung-Beeman, M., Bowden, E. M., Haberman, J., Frymiare, J. L., Arambel-Liu, S., Greenblatt, R., Reber, P. J., & Kounios, J. (2004). Neural activity when people solve verbal problems with insight. PLoS biology, 2(4), e97.","journal-title":"PLoS biology"},{"key":"9787_CR72","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1613\/jair.301","volume":"4","author":"LP Kaelbling","year":"1996","unstructured":"Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of artificial intelligence research, 4, 237\u2013285.","journal-title":"Journal of artificial intelligence research"},{"issue":"23","key":"9787_CR73","doi-asserted-by":"publisher","first-page":"3952","DOI":"10.1016\/j.neuron.2022.09.001","volume":"110","author":"BJ Kagan","year":"2022","unstructured":"Kagan, B. J., Kitchen, A. C., Tran, N. T., Habibollahi, F., Khajehnejad, M., Parker, B. J., Bhat, A., Rollo, B., Razi, A., & Friston, K. J. (2022). In vitro neurons learn and exhibit sentience when embodied in a simulated game-world. Neuron, 110(23), 3952\u20133969. e3958.","journal-title":"Neuron"},{"issue":"4","key":"9787_CR74","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/s00422-018-0753-2","volume":"112","author":"R Kaplan","year":"2018","unstructured":"Kaplan, R., & Friston, K. J. (2018). Planning and navigation as active inference. Biological Cybernetics, 112(4), 323\u2013343.","journal-title":"Biological Cybernetics"},{"issue":"12","key":"9787_CR75","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.1162\/jocn_a_01182","volume":"29","author":"EA Karuza","year":"2017","unstructured":"Karuza, E. A., Emberson, L. L., Roser, M. E., Cole, D., Aslin, R. N., & Fiser, J. (2017). Neural Signatures of Spatial Statistical Learning: Characterizing the Extraction of Structure from Complex Visual Scenes. Journal Of Cognitive Neuroscience, 29(12), 1963\u20131976.","journal-title":"Journal Of Cognitive Neuroscience"},{"issue":"2","key":"9787_CR76","first-page":"165","volume":"114","author":"C Kemp","year":"2010","unstructured":"Kemp, C., Tenenbaum, J. B., Niyogi, S., & Griffiths, T. L. (2010). A probabilistic model of theory formation Cognition 114(2): 165\u2013196.","journal-title":"A probabilistic model of theory formation Cognition"},{"key":"9787_CR77","doi-asserted-by":"publisher","first-page":"80","DOI":"10.3389\/fnsys.2011.00080","volume":"5","author":"SJ Kiebel","year":"2011","unstructured":"Kiebel, S. J., & Friston, K. J. (2011). Free energy and dendritic self-organization. Frontiers in systems neuroscience, 5, 80.","journal-title":"Frontiers in systems neuroscience"},{"issue":"8","key":"9787_CR78","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-022-10351-w","volume":"56","author":"NK Kitson","year":"2023","unstructured":"Kitson, N. K., Constantinou, A. C., Guo, Z., Liu, Y., & Chobtham, K. (2023). A survey of Bayesian network structure learning. Artificial Intelligence Review, 56(8), 1\u201394.","journal-title":"Artificial Intelligence Review"},{"issue":"4","key":"9787_CR79","first-page":"454","volume":"111","author":"Z Kurth-Nelson","year":"2023","unstructured":"Kurth-Nelson, Z., Behrens, T., Wayne, G., Miller, K., Luettgau, L., Dolan, R., Liu, Y., & Schwartenbeck, P. (2023). Replay and compositional computation Neuron 111(4): 454\u2013469.","journal-title":"Replay and compositional computation Neuron"},{"issue":"6266","key":"9787_CR80","first-page":"1332","volume":"350","author":"BM Lake","year":"2015","unstructured":"Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction Science 350(6266): 1332\u20131338.","journal-title":"Human-level concept learning through probabilistic program induction Science"},{"key":"9787_CR81","doi-asserted-by":"publisher","first-page":"e253","DOI":"10.1017\/S0140525X16001837","volume":"40","author":"BM Lake","year":"2017","unstructured":"Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and brain sciences, 40, e253.","journal-title":"Behavioral and brain sciences"},{"issue":"4","key":"9787_CR82","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1109\/3468.508827","volume":"26","author":"P Larranaga","year":"1996","unstructured":"Larranaga, P., Kuijpers, C. M., Murga, R. H., & Yurramendi, Y. (1996a). Learning Bayesian network structures by searching for the best ordering with genetic algorithms. IEEE transactions on systems man and cybernetics-part A: systems and humans, 26(4), 487\u2013493.","journal-title":"IEEE transactions on systems man and cybernetics-part A: systems and humans"},{"issue":"9","key":"9787_CR83","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1109\/34.537345","volume":"18","author":"P Larranaga","year":"1996","unstructured":"Larranaga, P., Poza, M., Yurramendi, Y., Murga, R. H., & Kuijpers, C. M. H. (1996b). Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters. IEEE transactions on pattern analysis and machine intelligence, 18(9), 912\u2013926.","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"9787_CR84","doi-asserted-by":"crossref","unstructured":"Lauritzen, S. L. (1996). Graphical Models. Oxford, Oxford University Press.","DOI":"10.1093\/oso\/9780198522195.001.0001"},{"issue":"6544","key":"9787_CR85","doi-asserted-by":"publisher","first-page":"eabf1357","DOI":"10.1126\/science.abf1357","volume":"372","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Mattar, M. G., Behrens, T. E., Daw, N. D., & Dolan, R. J. (2021). Experience replay is associated with efficient nonlocal learning. Science, 372(6544), eabf1357.","journal-title":"Science"},{"issue":"2","key":"9787_CR86","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1037\/0033-295X.111.2.309","volume":"111","author":"BC Love","year":"2004","unstructured":"Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111(2), 309\u2013332.","journal-title":"Psychological Review"},{"issue":"1","key":"9787_CR87","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1038\/s41467-019-13930-8","volume":"11","author":"ML Mack","year":"2020","unstructured":"Mack, M. L., Preston, A. R., & Love, B. C. (2020). Ventromedial prefrontal cortex compression during concept learning. Nature Communications, 11(1), 46.","journal-title":"Nature Communications"},{"key":"9787_CR88","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.neucom.2019.05.083","volume":"359","author":"D Maisto","year":"2019","unstructured":"Maisto, D., Friston, K., & Pezzulo, G. (2019). Caching mechanisms for habit formation in active inference. Neurocomputing, 359, 298\u2013314.","journal-title":"Neurocomputing"},{"key":"9787_CR89","doi-asserted-by":"publisher","first-page":"1099253","DOI":"10.3389\/fevo.2022.1099253","volume":"10","author":"G Matassi","year":"2023","unstructured":"Matassi, G., & Martinez, P. (2023). The brain-computer analogy\u2014A special issue. Frontiers in Ecology and Evolution, 10, 1099253.","journal-title":"Frontiers in Ecology and Evolution"},{"key":"9787_CR92","doi-asserted-by":"publisher","first-page":"56","DOI":"10.3389\/fncom.2016.00056","volume":"10","author":"MB Mirza","year":"2016","unstructured":"Mirza, M. B., Adams, R. A., Mathys, C. D., & Friston, K. J. (2016). Scene Construction, Visual Foraging, and Active Inference. Frontiers in computational neuroscience, 10, 56\u201356.","journal-title":"Frontiers in computational neuroscience"},{"issue":"1","key":"9787_CR91","doi-asserted-by":"publisher","first-page":"e0190429","DOI":"10.1371\/journal.pone.0190429","volume":"13","author":"MB Mirza","year":"2018","unstructured":"Mirza, M. B., Adams, R. A., Mathys, C., & Friston, K. J. (2018). Human visual exploration reduces uncertainty about the sensed world. PloS one, 13(1), e0190429.","journal-title":"PloS one"},{"issue":"1","key":"9787_CR90","doi-asserted-by":"publisher","first-page":"13915","DOI":"10.1038\/s41598-019-50138-8","volume":"9","author":"MB Mirza","year":"2019","unstructured":"Mirza, M. B., Adams, R. A., Friston, K., & Parr, T. (2019). Introducing a Bayesian model of selective attention based on active inference. Scientific Reports, 9(1), 13915.","journal-title":"Scientific Reports"},{"issue":"1","key":"9787_CR93","first-page":"16223","volume":"11","author":"MB Mirza","year":"2021","unstructured":"Mirza, M. B., Cullen, M., Parr, T., Shergill, S., & Moran, R. J. (2021). Contextual perception under active inference Scientific reports 11(1): 16223.","journal-title":"Contextual perception under active inference Scientific reports"},{"key":"9787_CR94","first-page":"189","volume":"234","author":"N Mizuguchi","year":"2017","unstructured":"Mizuguchi, N., Kanosue, K., Wilson, M. R., Walsh, V., & Parkin, B. (2017). Elsevier 234: 189\u2013204.","journal-title":"Elsevier"},{"key":"9787_CR95","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.neuropsychologia.2017.05.007","volume":"126","author":"CD Monroy","year":"2019","unstructured":"Monroy, C. D., Gerson, S. A., Dom\u00ednguez-Mart\u00ednez, E., Kaduk, K., Hunnius, S., & Reid, V. (2019a). Sensitivity to structure in action sequences: An infant event-related potential study. Neuropsychologia, 126, 92\u2013101.","journal-title":"Neuropsychologia"},{"issue":"1","key":"9787_CR98","first-page":"272","volume":"42","author":"RJ Moran","year":"2008","unstructured":"Moran, R. J., Stephan, K. E., Kiebel, S. J., Rombach, N., O\u2019Connor, W. T., Murphy, K., Reilly, R. B., & Friston, K. J. (2008). Bayesian estimation of synaptic physiology from the spectral responses of neural masses Neuroimage 42(1): 272\u2013284.","journal-title":"Bayesian estimation of synaptic physiology from the spectral responses of neural masses Neuroimage"},{"issue":"4","key":"9787_CR97","doi-asserted-by":"publisher","first-page":"1694","DOI":"10.1016\/j.neuroimage.2011.01.012","volume":"55","author":"RJ Moran","year":"2011","unstructured":"Moran, R. J., Stephan, K. E., Dolan, R. J., & Friston, K. J. (2011). Consistent spectral predictors for dynamic causal models of steady-state responses. Neuroimage, 55(4), 1694\u20131708.","journal-title":"Neuroimage"},{"issue":"2","key":"9787_CR99","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.gaitpost.2008.08.012","volume":"29","author":"AP Mulavara","year":"2009","unstructured":"Mulavara, A. P., Cohen, H. S., & Bloomberg, J. J. (2009). Critical features of training that facilitate adaptive generalization of over ground locomotion. Gait & Posture, 29(2), 242\u2013248.","journal-title":"Gait & Posture"},{"issue":"21","key":"9787_CR100","doi-asserted-by":"publisher","first-page":"9497","DOI":"10.1523\/JNEUROSCI.19-21-09497.1999","volume":"19","author":"Z N\u00e1dasdy","year":"1999","unstructured":"N\u00e1dasdy, Z., Hirase, H., Czurk\u00f3, A., Csicsvari, J., & Buzs\u00e1ki, G. (1999). Replay and time compression of recurring spike sequences in the hippocampus. Journal of Neuroscience, 19(21), 9497\u20139507.","journal-title":"Journal of Neuroscience"},{"key":"9787_CR101","doi-asserted-by":"publisher","first-page":"802396","DOI":"10.3389\/fnins.2022.802396","volume":"16","author":"V Neacsu","year":"2022","unstructured":"Neacsu, V., Convertino, L., & Friston, K. J. (2022). Synthetic spatial foraging with active inference in a geocaching task. Frontiers in Neuroscience, 16, 802396.","journal-title":"Frontiers in Neuroscience"},{"issue":"11","key":"9787_CR102","doi-asserted-by":"publisher","first-page":"e0277199","DOI":"10.1371\/journal.pone.0277199","volume":"17","author":"V Neacsu","year":"2022","unstructured":"Neacsu, V., Mirza, M. B., Adams, R. A., & Friston, K. J. (2022b). Structure learning enhances concept formation in synthetic Active Inference agents. PLoS One, 17(11), e0277199.","journal-title":"PLoS One"},{"issue":"8","key":"9787_CR103","doi-asserted-by":"publisher","first-page":"e129","DOI":"10.1371\/journal.pcbi.0030129","volume":"3","author":"CJ Needham","year":"2007","unstructured":"Needham, C. J., Bradford, J. R., Bulpitt, A. J., & Westhead, D. R. (2007). A primer on learning in Bayesian networks for computational biology. PLoS computational biology, 3(8), e129.","journal-title":"PLoS computational biology"},{"key":"9787_CR104","unstructured":"Ngo, H., Luciw, M., Forster, A., & Schmidhuber, J. (2012). Learning skills from play: artificial curiosity on a katana robot arm. The 2012 international joint conference on neural networks (IJCNN), IEEE."},{"issue":"10","key":"9787_CR105","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.1038\/s41593-019-0470-8","volume":"22","author":"Y Niv","year":"2019","unstructured":"Niv, Y. (2019). Learning task-state representations. Nature Neuroscience, 22(10), 1544\u20131553.","journal-title":"Nature Neuroscience"},{"key":"9787_CR106","doi-asserted-by":"publisher","first-page":"e36395","DOI":"10.7554\/eLife.36395","volume":"7","author":"SR O\u2019Bryan","year":"2018","unstructured":"O\u2019Bryan, S. R., Worthy, D. A., Livesey, E. J., & Davis, T. (2018). Model-based fMRI reveals dissimilarity processes underlying base rate neglect. ELife. 7: e36395.","journal-title":"ELife"},{"issue":"136","key":"9787_CR107","doi-asserted-by":"publisher","first-page":"20170376","DOI":"10.1098\/rsif.2017.0376","volume":"14","author":"T Parr","year":"2017","unstructured":"Parr, T., & Friston, K. J. (2017). Uncertainty, epistemics and active inference. Journal of The Royal Society Interface, 14(136), 20170376.","journal-title":"Journal of The Royal Society Interface"},{"key":"9787_CR108","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.neuropsychologia.2018.01.041","volume":"111","author":"T Parr","year":"2018","unstructured":"Parr, T., & Friston, K. J. (2018). Active inference and the anatomy of oculomotion. Neuropsychologia, 111, 334\u2013343.","journal-title":"Neuropsychologia"},{"key":"9787_CR109","doi-asserted-by":"publisher","first-page":"772641","DOI":"10.3389\/fnsys.2021.772641","volume":"15","author":"T Parr","year":"2021","unstructured":"Parr, T., & Pezzulo, G. (2021). Understanding, explanation, and active inference. Frontiers in Systems Neuroscience, 15, 772641.","journal-title":"Frontiers in Systems Neuroscience"},{"key":"9787_CR110","doi-asserted-by":"crossref","unstructured":"Parr, T., Pezzulo, G., & Friston, K. J. (2022). Active inference: the free energy principle in mind, brain, and behavior. MIT Press.","DOI":"10.7551\/mitpress\/12441.001.0001"},{"key":"9787_CR111","unstructured":"Pearl, J. (2000). Models, reasoning and inference. CambridgeUniversityPress 19."},{"key":"9787_CR112","unstructured":"Pearl, J. (2014). Probabilistic reasoning in intelligent systems: networks of plausible inference. Elsevier."},{"key":"9787_CR113","unstructured":"Peters, J., Janzing, D., & Sch\u00f6lkopf, B. (2017). Elements of causal inference: foundations and learning algorithms. The MIT Press."},{"key":"9787_CR114","doi-asserted-by":"crossref","unstructured":"Pezzulo, G., Zorzi, M., & Corbetta, M. (2021). The secret life of predictive brains: what\u2019s spontaneous activity for? Trends in Cognitive Sciences.","DOI":"10.31234\/osf.io\/qus3h"},{"key":"9787_CR115","unstructured":"Poincar\u00e9, H. (2022). The foundations of science: Science and hypothesis, the value of science, science and method, DigiCat."},{"key":"9787_CR116","unstructured":"Rebane, G., & Pearl, J. (2013). The recovery of causal poly-trees from statistical data. arXiv preprint arXiv:1304.2736."},{"issue":"1","key":"9787_CR117","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1037\/0096-3445.133.1.63","volume":"133","author":"JN Rouder","year":"2004","unstructured":"Rouder, J. N., & Ratcliff, R. (2004). Comparing categorization models. Journal of experimental psychology General, 133(1), 63\u201382.","journal-title":"Journal of experimental psychology General"},{"key":"9787_CR118","unstructured":"Rutar, D. (2023). Developing higher cognition through predictive processing. Journal of Contemporary Educational Studies\/Sodobna Pedagogika 74(3)."},{"issue":"2","key":"9787_CR119","doi-asserted-by":"publisher","first-page":"41","DOI":"10.3390\/biology8020041","volume":"8","author":"F Sartor","year":"2019","unstructured":"Sartor, F., Eelderink-Chen, Z., Aronson, B., Bosman, J., Hibbert, L. E., Dodd, A. N., Kov\u00e1cs, \u00c1., T., & Merrow, M. (2019). Are there circadian clocks in non-photosynthetic bacteria? Biology, 8(2):41.","journal-title":"Biology"},{"key":"9787_CR120","doi-asserted-by":"crossref","unstructured":"Schillaci, G., Pico Villalpando, A., Hafner, V. V., Hanappe, P., Colliaux, D., & Wintz, T. (2020). Intrinsic motivation and episodic memories for robot exploration of high-dimensional sensory spaces. Adaptive Behavior: 1059712320922916.","DOI":"10.1177\/1059712320922916"},{"issue":"2","key":"9787_CR121","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1080\/09540090600768658","volume":"18","author":"J Schmidhuber","year":"2006","unstructured":"Schmidhuber, J. (2006). Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connection Science, 18(2), 173\u2013187.","journal-title":"Connection Science"},{"key":"9787_CR123","doi-asserted-by":"crossref","unstructured":"Schwartenbeck, P., & Friston, K. (2016). Computational phenotyping in psychiatry: a worked example. eneuro 3(4).","DOI":"10.1523\/ENEURO.0049-16.2016"},{"issue":"10","key":"9787_CR122","doi-asserted-by":"publisher","first-page":"3434","DOI":"10.1093\/cercor\/bhu159","volume":"25","author":"P Schwartenbeck","year":"2014","unstructured":"Schwartenbeck, P., FitzGerald, T. H. B., Mathys, C., Dolan, R., & Friston, K. (2014). The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes. Cerebral Cortex, 25(10), 3434\u20133445.","journal-title":"Cerebral Cortex"},{"issue":"1708","key":"9787_CR124","doi-asserted-by":"publisher","first-page":"20160007","DOI":"10.1098\/rstb.2016.0007","volume":"371","author":"AK Seth","year":"2016","unstructured":"Seth, A. K., & Friston, K. J. (2016). Active interoceptive inference and the emotional brain. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1708), 20160007.","journal-title":"Philosophical Transactions of the Royal Society B: Biological Sciences"},{"issue":"11","key":"9787_CR125","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1016\/j.tics.2018.08.008","volume":"22","author":"AK Seth","year":"2018","unstructured":"Seth, A. K., & Tsakiris, M. (2018). Being a beast machine: The somatic basis of selfhood. Trends in cognitive sciences, 22(11), 969\u2013981.","journal-title":"Trends in cognitive sciences"},{"issue":"5","key":"9787_CR126","doi-asserted-by":"publisher","first-page":"3208","DOI":"10.1523\/JNEUROSCI.14-05-03208.1994","volume":"14","author":"R Shadmehr","year":"1994","unstructured":"Shadmehr, R., & Mussa-Ivaldi, F. A. (1994). Adaptive representation of dynamics during learning of a motor task. Journal of neuroscience, 14(5), 3208\u20133224.","journal-title":"Journal of neuroscience"},{"key":"9787_CR127","unstructured":"Shin, H., Lee, J. K., Kim, J., & Kim, J. (2017). Continual learning with deep generative replay. Advances in neural information processing systems 30."},{"issue":"5257","key":"9787_CR128","doi-asserted-by":"publisher","first-page":"1870","DOI":"10.1126\/science.271.5257.1870","volume":"271","author":"WE Skaggs","year":"1996","unstructured":"Skaggs, W. E., & McNaughton, B. L. (1996). Replay of neuronal firing sequences in rat hippocampus during sleep following spatial experience. Science, 271(5257), 1870\u20131873.","journal-title":"Science"},{"key":"9787_CR133","doi-asserted-by":"publisher","first-page":"2844","DOI":"10.3389\/fpsyg.2019.02844","volume":"10","author":"R Smith","year":"2019","unstructured":"Smith, R., Parr, T., & Friston, K. J. (2019). Simulating emotions: An active inference model of emotional state inference and emotion concept learning. Frontiers in psychology, 10, 2844.","journal-title":"Frontiers in psychology"},{"key":"9787_CR132","doi-asserted-by":"crossref","unstructured":"Smith, R., Kuplicki, R., Teed, A., Upshaw, V., & Khalsa, S. S. (2020a). Confirmatory evidence that healthy individuals can adaptively adjust prior expectations and interoceptive precision estimates. Active Inference: First International Workshop, IWAI 2020, Co-located with ECML\/PKDD 2020, Ghent, Belgium, September 14, 2020, Proceedings 1, Springer.","DOI":"10.1101\/2020.08.31.275594"},{"key":"9787_CR135","doi-asserted-by":"publisher","first-page":"41","DOI":"10.3389\/fncom.2020.00041","volume":"14","author":"R Smith","year":"2020","unstructured":"Smith, R., Schwartenbeck, P., Parr, T., & Friston, K. J. (2020b). An active inference approach to modeling structure learning: concept learning as an example case. Frontiers in Computational Neuroscience, 14, 41.","journal-title":"Frontiers in Computational Neuroscience"},{"issue":"1","key":"9787_CR130","doi-asserted-by":"publisher","first-page":"E74","DOI":"10.1503\/jpn.200032","volume":"46","author":"R Smith","year":"2021","unstructured":"Smith, R., Kirlic, N., Stewart, J. L., Touthang, J., Kuplicki, R., Khalsa, S. S., Feinstein, J., Paulus, M. P., & Aupperle, R. L. (2021a). Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach. Journal of Psychiatry and Neuroscience, 46(1), E74\u2013E87.","journal-title":"Journal of Psychiatry and Neuroscience"},{"issue":"1","key":"9787_CR131","doi-asserted-by":"publisher","first-page":"11783","DOI":"10.1038\/s41598-021-91308-x","volume":"11","author":"R Smith","year":"2021","unstructured":"Smith, R., Kirlic, N., Stewart, J. L., Touthang, J., Kuplicki, R., McDermott, T. J., Taylor, S., Khalsa, S. S., Paulus, M. P., & Aupperle, R. L. (2021b). Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample. Scientific reports, 11(1), 11783.","journal-title":"Scientific reports"},{"key":"9787_CR129","doi-asserted-by":"publisher","first-page":"102632","DOI":"10.1016\/j.jmp.2021.102632","volume":"107","author":"R Smith","year":"2022","unstructured":"Smith, R., Friston, K. J., & Whyte, C. J. (2022). A step-by-step tutorial on active inference and its application to empirical data. Journal of Mathematical Psychology, 107, 102632.","journal-title":"Journal of Mathematical Psychology"},{"issue":"2","key":"9787_CR134","first-page":"81","volume":"200","author":"R Smith","year":"2022","unstructured":"Smith, R., Ramstead, M. J., & Kiefer, A. (2022b). Active inference models do not contradict folk psychology Synthese 200(2): 81.","journal-title":"Active inference models do not contradict folk psychology Synthese"},{"issue":"6230","key":"9787_CR136","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1126\/science.aaa3799","volume":"348","author":"AE Stahl","year":"2015","unstructured":"Stahl, A. E., & Feigenson, L. (2015). Cognitive development. Observing the unexpected enhances infants\u2019 learning and exploration. Science(New York N Y), 348(6230), 91\u201394.","journal-title":"Science(New York N Y)"},{"issue":"4","key":"9787_CR137","first-page":"1004","volume":"46","author":"KE Stephan","year":"2009","unstructured":"Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009). Bayesian model selection for group studies NeuroImage 46(4): 1004\u20131017.","journal-title":"Bayesian model selection for group studies NeuroImage"},{"issue":"3","key":"9787_CR138","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1207\/s15516709cog2703_6","volume":"27","author":"M Steyvers","year":"2003","unstructured":"Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive science, 27(3), 453\u2013489.","journal-title":"Cognitive science"},{"key":"9787_CR139","doi-asserted-by":"publisher","first-page":"102329","DOI":"10.1016\/j.pneurobio.2022.102329","volume":"217","author":"I Stoianov","year":"2022","unstructured":"Stoianov, I., Maisto, D., & Pezzulo, G. (2022). The hippocampal formation as a hierarchical generative model supporting generative replay and continual learning. Progress in Neurobiology, 217, 102329.","journal-title":"Progress in Neurobiology"},{"issue":"1","key":"9787_CR140","doi-asserted-by":"publisher","first-page":"e1002803","DOI":"10.1371\/journal.pcbi.1002803","volume":"9","author":"M Sunn\u00e5ker","year":"2013","unstructured":"Sunn\u00e5ker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., & Dessimoz, C. (2013). Approximate bayesian computation PLoS computational biology 9(1): e1002803.","journal-title":"Approximate bayesian computation PLoS computational biology"},{"issue":"10","key":"9787_CR141","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1016\/j.tics.2019.07.008","volume":"23","author":"A Tambini","year":"2019","unstructured":"Tambini, A., & Davachi, L. (2019). Awake reactivation of prior experiences consolidates memories and biases cognition. Trends in cognitive sciences, 23(10), 876\u2013890.","journal-title":"Trends in cognitive sciences"},{"issue":"4","key":"9787_CR142","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1017\/S0140525X01000061","volume":"24","author":"JB Tenenbaum","year":"2001","unstructured":"Tenenbaum, J. B., & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and brain sciences, 24(4), 629\u2013640.","journal-title":"Behavioral and brain sciences"},{"issue":"7","key":"9787_CR143","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.tics.2006.05.009","volume":"10","author":"JB Tenenbaum","year":"2006","unstructured":"Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10(7), 309\u2013318.","journal-title":"Trends in Cognitive Sciences"},{"issue":"6022","key":"9787_CR144","first-page":"1279","volume":"331","author":"JB Tenenbaum","year":"2011","unstructured":"Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: statistics. Structure and Abstraction Science, 331(6022), 1279\u20131285.","journal-title":"Structure and Abstraction Science"},{"issue":"4","key":"9787_CR145","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1037\/h0061626","volume":"55","author":"EC Tolman","year":"1948","unstructured":"Tolman, E. C. (1948). Cognitive maps in rats and men. Psychological review, 55(4), 189.","journal-title":"Psychological review"},{"issue":"2","key":"9787_CR146","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.brainresbull.2003.09.004","volume":"62","author":"G Tononi","year":"2003","unstructured":"Tononi, G., & Cirelli, C. (2003). Sleep and synaptic homeostasis: a hypothesis. Brain research bulletin, 62(2), 143\u2013150.","journal-title":"Brain research bulletin"},{"issue":"1","key":"9787_CR147","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.smrv.2005.05.002","volume":"10","author":"G Tononi","year":"2006","unstructured":"Tononi, G., & Cirelli, C. (2006). Sleep function and synaptic homeostasis. Sleep Medicine Reviews, 10(1), 49\u201362.","journal-title":"Sleep Medicine Reviews"},{"key":"9787_CR148","doi-asserted-by":"publisher","first-page":"101672","DOI":"10.1016\/j.psychsport.2020.101672","volume":"48","author":"AJ Toth","year":"2020","unstructured":"Toth, A. J., McNeill, E., Hayes, K., Moran, A. P., & Campbell, M. (2020). Does mental practice still enhance performance? A 24 Year follow-up and meta-analytic replication and extension. Psychology of Sport and Exercise, 48, 101672.","journal-title":"Psychology of Sport and Exercise"},{"issue":"3","key":"9787_CR149","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1109\/TCSVT.2018.2816960","volume":"29","author":"NA Tu","year":"2019","unstructured":"Tu, N. A., Huynh-The, T., Khan, K. U., & Lee, Y. (2019). ML-HDP: A Hierarchical Bayesian Nonparametric Model for Recognizing Human Actions in Video. IEEE Transactions on Circuits and Systems for Video Technology, 29(3), 800\u2013814.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"issue":"1","key":"9787_CR151","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1146\/annurev-devpsych-121318-084833","volume":"2","author":"TD Ullman","year":"2020","unstructured":"Ullman, T. D., & Tenenbaum, J. B. (2020). Bayesian models of conceptual development: Learning as building models of the world. Annual Review of Developmental Psychology, 2(1), 533\u2013558.","journal-title":"Annual Review of Developmental Psychology"},{"key":"9787_CR150","unstructured":"Ullman, T., Goodman, N., & Tenenbaum, J. (2010). Theory acquisition as stochastic search. In: Proceedings of the Annual Meeting of the Cognitive Science Society."},{"issue":"1","key":"9787_CR152","doi-asserted-by":"publisher","first-page":"4069","DOI":"10.1038\/s41467-020-17866-2","volume":"11","author":"GM Van de Ven","year":"2020","unstructured":"Van de Ven, G. M., Siegelmann, H. T., & Tolias, A. S. (2020). Brain-inspired replay for continual learning with artificial neural networks. Nature communications, 11(1), 4069.","journal-title":"Nature communications"},{"key":"9787_CR153","unstructured":"Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D., & Botvinick, M. (2016). Learning to reinforcement learn. arXiv preprint arXiv:1611.05763."},{"key":"9787_CR154","doi-asserted-by":"publisher","first-page":"1424","DOI":"10.3389\/fpsyg.2016.01424","volume":"7","author":"ME Webb","year":"2016","unstructured":"Webb, M. E., Little, D. R., & Cropper, S. J. (2016). Insight is not in the problem: Investigating insight in problem solving across task types. Frontiers in psychology, 7, 1424.","journal-title":"Frontiers in psychology"},{"issue":"2","key":"9787_CR155","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1002\/1520-6696(199404)30:2<162::AID-JHBS2300300205>3.0.CO;2-M","volume":"30","author":"N Weidman","year":"1994","unstructured":"Weidman, N. (1994). Mental testing and machine intelligence: The Lashley-Hull debate. Journal of the History of the Behavioral Sciences, 30(2), 162\u2013180.","journal-title":"Journal of the History of the Behavioral Sciences"},{"issue":"1","key":"9787_CR156","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10400419.2013.752178","volume":"25","author":"RW Weisberg","year":"2013","unstructured":"Weisberg, R. W. (2013). On the demystification of insight: A critique of neuroimaging studies of insight. Creativity Research Journal, 25(1), 1\u201314.","journal-title":"Creativity Research Journal"},{"issue":"3","key":"9787_CR157","first-page":"729","volume":"12","author":"MA Wiering","year":"2012","unstructured":"Wiering, M. A., & Van Otterlo, M. (2012). Reinforcement learning. Adaptation learning and optimization, 12(3), 729.","journal-title":"Adaptation learning and optimization"},{"key":"9787_CR158","unstructured":"Wilson, A., Fern, A., & Tadepalli, P. (2012). Transfer learning in sequential decision problems: A hierarchical Bayesian approach. In: Proceedings of ICML Workshop on Unsupervised and Transfer Learning, JMLR Workshop and Conference Proceedings."},{"issue":"6","key":"9787_CR159","doi-asserted-by":"publisher","first-page":"e2205211120","DOI":"10.1073\/pnas.2205211120","volume":"120","author":"GE Wimmer","year":"2023","unstructured":"Wimmer, G. E., Liu, Y., McNamee, D. C., & Dolan, R. J. (2023). Distinct replay signatures for prospective decision-making and memory preservation. Proceedings of the National Academy of Sciences.120(6): e2205211120.","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"9787_CR160","unstructured":"Winn, J., Bishop, C. M., & Jaakkola, T. (2005). Variational message passing. Journal of Machine Learning Research 6(4)."},{"issue":"31","key":"9787_CR161","doi-asserted-by":"publisher","first-page":"eabf9616","DOI":"10.1126\/sciadv.abf9616","volume":"7","author":"T Wise","year":"2021","unstructured":"Wise, T., Liu, Y., Chowdhury, F., & Dolan, R. J. (2021). Model-based aversive learning in humans is supported by preferential task state reactivation. Science Advances, 7(31), eabf9616.","journal-title":"Science Advances"},{"key":"9787_CR162","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/j.neubiorev.2021.08.002","volume":"129","author":"L Wittkuhn","year":"2021","unstructured":"Wittkuhn, L., Chien, S., Hall-McMaster, S., & Schuck, N. W. (2021). Replay in minds and machines. Neuroscience & Biobehavioral Reviews, 129, 367\u2013388.","journal-title":"Neuroscience & Biobehavioral Reviews"},{"issue":"42","key":"9787_CR163","first-page":"8259","volume":"39","author":"D Zeithamova","year":"2019","unstructured":"Zeithamova, D., Mack, M. L., Braunlich, K., Davis, T., Seger, C. A., van Kesteren, M. T. R., & Wutz, A. (2019). Brain Mechanisms of Concept Learning J Neurosci 39(42): 8259\u20138266.","journal-title":"Brain Mechanisms of Concept Learning J Neurosci"},{"key":"9787_CR164","doi-asserted-by":"crossref","unstructured":"Zha, D., Lai, K. H., Zhou, K., & Hu, X. (2019). Experience replay optimization. arXiv preprint arXiv:1906.08387.","DOI":"10.24963\/ijcai.2019\/589"}],"container-title":["Minds and Machines"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11023-026-09787-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11023-026-09787-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11023-026-09787-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T05:03:00Z","timestamp":1781499780000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11023-026-09787-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,15]]},"references-count":164,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["9787"],"URL":"https:\/\/doi.org\/10.1007\/s11023-026-09787-8","relation":{},"ISSN":["1572-8641"],"issn-type":[{"value":"1572-8641","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,15]]},"assertion":[{"value":"16 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"33"}}