Alba, J. W. & Hasher, L. Is memory schematic? Psychol. Bull. 93, 203–231 (1983).

Article  Google Scholar 

Ghosh, V. E. & Gilboa, A. What is a memory schema? A historical perspective on current neuroscience literature. Neuropsychologia 53, 104–114 (2014).

Article  PubMed  Google Scholar 

Schank, R. & Abelson, R. in Scripts Plans Goals and Understanding: an Inquiry into Human Knowledge Structures 36–68 (Lawrence Erlbaum, 1977). A classic paper offering a philosophical investigation into human knowledge organization, goals, and event schemas (‘scripts’).

Bartlett, F. C. Remembering: a Study in Experimental and Social Psychology (Cambridge Univ. Press, 1932). One of the earliest demonstrations not only that memories are reconstructed, biased and transformed by schemas, but also of the notion that memories are not fixed copies of the past but rather are malleable and are influenced by expectations and social norms.

Gilboa, A. & Marlatte, H. Neurobiology of schemas and schema-mediated memory. Trends Cogn. Sci. 21, 618–631 (2017). This article provides one of the only overviews of brain regions and networks involved in schema learning and instantiation and the influence of schemas on memory.

Article  PubMed  Google Scholar 

Sutton, R. S. & Barto, A. G. Reinforcement Learning: an Introduction (MIT, 2018).

Chan, S. C. Y., Niv, Y. & Norman, K. A. A probability distribution over latent causes, in the orbitofrontal cortex. J. Neurosci. 36, 7817–7828 (2016).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Klein-Flügge, M. C., Bongioanni, A. & Rushworth, M. F. S. Medial and orbital frontal cortex in decision-making and flexible behavior. Neuron https://doi.org/10.1016/j.neuron.2022.05.022 (2022).

Schuck, N. W. et al. Human orbitofrontal cortex represents a cognitive map of state space article human orbitofrontal cortex represents a cognitive map of state space. Neuron 91, 1402–1412 (2016). This study provides a compelling demonstration of a neural representation of complex task structure in the mOFC–vmPFC in a task with no rewards, thus obviating a variety of alternative explanations of mOFC–vmPFC activity.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wilson, R. C., Takahashi, Y. K., Schoenbaum, G. & Niv, Y. Orbitofrontal cortex as a cognitive map of task space. Neuron 81, 267–279 (2014).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhou, J. et al. Evolving schema representations in orbitofrontal ensembles during learning. Nature 590, 606–611 (2020). Using single-unit recordings during a sequential task, the authors show how neural representations of schemas evolve in the rodent OFC, including demonstration of dimensionality reduction, generalization of neural representations across instances and schema-dependent enhanced learning.

Article  PubMed  PubMed Central  Google Scholar 

Baldassano, C., Hasson, U. & Norman, K. A. Representation of real-world event schemas during narrative perception. J. Neurosci. 38, 9689–9699 (2018).This study uses sophisticated hidden Markov models to segment events in functional MRI data from participants watching dynamic movie stimuli and demonstrates, in the human mPFC, abstract representations of schemas that include information about the order of events.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bonasia, K. et al. Prior knowledge modulates the neural substrates of encoding and retrieving naturalistic events at short and long delays. Neurobiol. Learn. Mem. 153, 26–39 (2018).

Article  PubMed  Google Scholar 

Giuliano, A. E., Bonasia, K., Ghosh, V. E., Moscovitch, M. & Gilboa, A. Differential influence of ventromedial prefrontal cortex lesions on neural representations of schema and semantic category knowledge. J. Cogn. Neurosci. https://doi.org/10.1162/jocn_a_01746 (2021). A unique study showing different impairments in deploying schema versus category knowledge in individuals with mOFC–vmPFC lesions.

van Kesteren, M. T. R., Ruiter, D. J., Fernández, G. & Henson, R. N. How schema and novelty augment memory formation. Trends Neurosci. 35, 211–219 (2012).

Article  PubMed  Google Scholar 

Varga, N., Morton, N. & Preston, A. in The Oxford Handbook of Human Memory (eds Kahana, M. J. & Wagner, A. D.) (Oxford Univ. Press, 2022).

Preston, A. R. & Eichenbaum, H. Interplay of hippocampus and prefrontal cortex in memory. Curr. Biol. 23, R764–R773 (2013). A perspective on hippocampal and prefrontal involvement in memory encoding, consolidation, and retrieval as a function of schematization and memory integration.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Rumelhart, D. E. & Ortony, A. in Schooling and the Acquisition of Knowledge 99–135 https://doi.org/10.4324/9781315271644-10 (1977).

Piaget, J. The Origins of Intelligence in Children (International Univ. Press, 1952).

Zacks, J. M. Event perception and memory. Annu. Rev. Psychol. 71, 165–191 (2020).

Article  PubMed  PubMed Central  Google Scholar 

Elman, J. L. & McRae, K. A model of event knowledge. Psychol. Rev. 126, 252–291 (2019).

Article  PubMed  Google Scholar 

Franklin, N. T., Norman, K. A., Ranganath, C., Zacks, J. M. & Gershman, S. J. Structured event memory: a neuro-symbolic model of event cognition. Psychol. Rev. 127, 327–361 (2020). A computational model accounting for event segmentation findings in humans using latent cause inference and event schemas.

Article  PubMed  Google Scholar 

Collins, A. M. & Loftus, E. F. Spreading activation theory of semantic processing. Psychol. Rev. 82, 407–428 (1975).

Article  Google Scholar 

Collins, A. M. & Quillian, M. R. Retrieval time from semantic memory. J. Verbal Learn. Verbal Behav. 8, 240–247 (1969).

Article  Google Scholar 

Kenett, Y. N., Levi, E., Anaki, D. & Faust, M. The semantic distance task: quantifying semantic distance with semantic network path length. J. Exp. Psychol. Learn. Mem. Cogn. https://doi.org/10.1037/xlm0000391 (2017).

Article  PubMed  Google Scholar 

Kumar, A. A. Semantic memory: a review of methods, models, and current challenges. Psychon. Bull. Rev. 28, 40–80 (2021).

Love, B. C., Medin, D. L. & Gureckis, T. M. SUSTAIN: a network model of category learning. Psychol. Rev. 111, 309–332 (2004).

Article  PubMed  Google Scholar 

McClelland, J. L., McNaughton, B. L. & Oreilly, R. C. Why there are complementary learning-systems in the hippocampus and neocortex—insights from the success and failures of connectionist models of learning and memory. Psychol. Rev. 102, 419–457 (1995).

Article  PubMed  Google Scholar 

Murphy, G. L. The Big Book of Concepts (MIT, 2004).

Daw, N. D., Niv, Y. & Dayan, P. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nat. Neurosci. 8, 1704–1711 (2005).

Article  CAS  PubMed  Google Scholar 

Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P. & Dolan, R. J. Model-based influences on humans’ choices and striatal prediction errors. Neuron 69, 1204–1215 (2011).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Doya, K., Samejima, K., Katagiri, K. & Kawato, M. Multiple model-based reinforcement learning. Neural Comput. 1369, 1347–1369 (2002).

Niv, Y. & Schoenbaum, G. Dialogues on prediction errors. Trends Cogn. Sci. 12, 265–272 (2008).

Article  PubMed  Google Scholar 

Rescorla, R. A. Pavlovian conditioning. It’s not what you think it is. Am. Psychol. 43, 151–160 (1988).

Article  CAS  PubMed  Google Scholar 

Rescorla, R. A. & Wagner, A. R. in Classical Conditioning II: Current Research and Theory (eds A. H. Black & W. F. Prokasy), Ch. 3, 64–99 (Appleton-Century-Crofts 1972).

Kamin, L. J. Predictability, surprise, attention, and conditioning. Symposium on Punishment (Princeton University 1967).

Kamin, L. J. in Miami Symposium on the Prediction of Behavior, 1967: Aversive Stimulation (ed. Jones, M. R.) 9–31 (Univ. Miami Press, 1968). A seminal paper describing the discovery of ‘blocking’, which prompted the idea that associative learning is driven by prediction errors, not simple co-occurrence of events.

d’Acremont, M., Schultz, W. & Bossaerts, P. The human brain encodes event frequencies while forming subjective beliefs. J. Neurosci. 33, 10887–10897 (2013).

Article  PubMed  PubMed Central  Google Scholar 

Niv, Y. & Langdon, A. Reinforcement learning with Marr. Curr. Opin. Behav. Sci. 11, 67–73 (2016).

Article  PubMed  PubMed Central  Google Scholar 

Ergo, K., De Loof, E. & Verguts, T. Reward prediction error and declarative memory. Trends Cogn. Sci. 24, 388–397 (2020).

Article  PubMed  Google Scholar 

Greve, A., Cooper, E., Tibon, R. & Henson, R. N. Knowledge is power: prior knowledge aids memory for both congruent and incongruent events, but in different ways. J. Exp. Psychol. Gen. 148, 325–341 (2019).

Article  PubMed  Google Scholar 

Rouhani, N., Norman, K. A. & Niv, Y. Dissociable effects of surprising rewards on learning and memory. J. Exp. Psychol. Learn. Mem. Cogn. 44, 1430–1443 (2018).

Article  PubMed  PubMed Central  Google Scholar 

Rouhani, N. & Niv, Y. Signed and unsigned reward prediction errors dynamically enhance learning and memory. eLife 10, e61077 (2021).

Sharpe, M. J. et al. Dopamine transients are sufficient and necessary for acquisition of model-based associations. Nat. Neurosci. 20, 735–742 (2017).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sharpe, M. J. et al. Dopamine transients do not act as model-free prediction errors during associative learning. Nat. Commun. 11, 106 (2020). This study demonstrates in rats that dopaminergic prediction errors are sufficient and necessary for learning state transitions even in the absence of a reward, contrary to previous assumptions that dopamine represents reward prediction errors used only for learning the reward value of stimuli.

Article  CAS  PubMed