Terenzi, D.; Liu, L.; Bellucci, G.; Park, S.: Determinants and modulators of human social decisions. Neuroscience and Biobehavioral Reviews 128, pp. 383 - 393 (2021)

Mancinelli, F.; Roiser, J.; Dayan, P.: Internality and the internalisation of failure: Evidence from a novel task. PLoS Computational Biology 17 (7), pp. 1 - 25 (2021)

Moran, R.; Dayan, P.; Dolan, R.: Efficiency and prioritization of inference-based credit assignment. Current Biology 31 (13), pp. 2747 - 2756 (2021)

Kóbor, A.; Kardos, Z.; Takács, Á.; Éltetö, N.; Janacsek, K.; Tóth-Fáber, E.; Csépe, V.; Nemeth, D.: Adaptation to recent outcomes attenuates the lasting effect of initial experience on risky decisions. Scientific Reports 11 (1), 10132, pp. 1 - 20 (2021)

Brielmann, A.; Pelli, D.: The pleasure of multiple images. Attention, Perception & Psychophysics 83 (3), pp. 1179 - 1188 (2021)

Brielmann, A.; Pelli, D.: Correction to: The pleasure of multiple images. Attention, Perception & Psychophysics 83 (3), p. 1189 (2021)

Dayan, P.: When will's wont wants wanting. Behavioral and Brain Sciences 44, e35 (2021)

Sargent, K.; Chavez-Baldini, U.; Master, S.; Verweij, K.; Lok, A.; Sutterland, A.; Vulink, N.; Denys, D.; Smit, D.; Nieman, D.: Resting-state brain oscillations predict cognitive function in psychiatric disorders: A transdiagnostic machine learning approach. NeuroImage: Clinical 30, 102617, pp. 1 - 9 (2021)

Akam, T.; Rodrigues-Vaz, I.; Marcelo , I.; Zhang, X.; Pereira, M.; Freire Oliveira , R.; Dayan, P.; Costa, R.: The Anterior Cingulate Cortex Predicts Future States to Mediate Model-Based Action Selection. Neuron 109 (1), pp. 149 - 163 (2021)

Moran, R.; Dayan, P.; Dolan, R.: Human subjects exploit a cognitive map for credit assignment. Proceedings of the National Academy of Sciences of the United States of America 118 (4), pp. 1 - 12 (2021)

Neville, V.; Dayan, P.; Gilchrist, I.; Paul, E.; Mendl, M.: Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational approach. PLoS Computational Biology 17 (1), pp. 1 - 27 (2021)

Gagne, C.; Zika, O.; Dayan, P.; Bishop, S.: Impaired adaptation of learning to contingency volatility in internalizing psychopathology. eLife 9, pp. 1 - 51 (2020)

Schulz, E.; Dayan, P.: Computational psychiatry for computers. iScience 23 (12), 101772 (2020)

Schulz, L.; Rollwage, M.; Dolan, R.; Fleming, S.: Dogmatism manifests in lowered information search under uncertainty. Proceedings of the National Academy of Sciences of the United States of America 117 (49), pp. 31527 - 31534 (2020)

Bellucci, G.; Camilleri, J.; Eickhoff, S.; Krueger, F.: Neural signatures of prosocial behaviors. Neuroscience and Biobehavioral Reviews 118, pp. 186 - 195 (2020)

Dayan, P.: Learning rules. Current Biology 30 (21), pp. R1289 - R1290 (2020)

Dezfouli, A.; Nock, R.; Dayan, P.: Adversarial vulnerabilities of human decision-making. Proceedings of the National Academy of Sciences of the United States of America 117 (46), 202016921, pp. 29221 - 29228 (2020)

Bellucci, G.: Positive attitudes and negative expectations in lonely individuals. Scientific Reports 10 (1), 18595, pp. 1 - 9 (2020)

Neville, V.; King, J.; Gilchrist, I.; Dayan, P.; Paul, E.; Mendl, M.: Author Correction: Reward and punisher experience alter rodent decision-making in a judgement bias task. Scientific Reports 11 (1), 21387 (2020)

Kastner, D.; Gillespie, A.; Dayan, P.; Frank, L.: Memory Alone Does Not Account for the Way Rats Learn a Simple Spatial Alternation Task. The Journal of Neuroscience 40 (38), pp. 7311 - 7317 (2020)

Neville, V.; King, J.; Gilchrist, I.; Dayan, P.; Paul, E.; Mendl, M.: Reward and punisher experience alter rodent decision-making in a judgement bias task. Scientific Reports 10 (1), 11839, pp. 1 - 14 (2020)

Bellucci, G.; Camilleri, J.; Iyengar, V.; Eickhoff, S.; Krueger, F.: The Emerging Neuroscience of Social Punishment: Meta-Analytic Evidence. Neuroscience and Biobehavioral Reviews 113, pp. 426 - 439 (2020)

Bellucci, G.; Münte, T.; Park , S.: Effects of a dopamine agonist on trusting behaviors in females. Psychopharmacology 237 (6), pp. 1671 - 1680 (2020)

Dayan, P.: Representation, abstraction, and simple-minded sophisticates. Behavioral and Brain Sciences 43, e126 (2020)

Eldar, E.; Lièvre, G.; Dayan, P.; Dolan, R.: The roles of online and offline replay in planning. eLife 9, e56911, pp. 1 - 23 (2020)

Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dayan, P.; Dolan , R.: The Value of What's to Come: Neural Mechanisms Coupling Prediction Error and the Utility of Anticipation. Science Advances 6 (25), eaba3828 (2020)

Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dayan, P.; Dolan, R.: The value of what’s to come: Neural mechanisms coupling prediction error and the utility of anticipation. Science Advances 6, 25 (2020)

Krueger, F.; Bellucci, G.; Xu, P.; Feng, C.: The Critical Role of the Right Dorsal and Ventral Anterior Insula in Reciprocity: Evidence From the Trust and Ultimatum Games. Frontiers in Human Neuroscience 14, 176, pp. 1 - 6 (2020)

Mobbs, D.; Headley, D.; Ding, W.; Dayan, P.: Space, Time, and Fear: Survival Computations along Defensive Circuits. Trends in Cognitive Sciences 24 (3), pp. 228 - 241 (2020)

Schad, D.; Rapp, M.; Garbusow, M.; Nebe, S.; Sebold, M.; Obst, E.; Sommer, C.; Deserno, L.; Rabovsky, M.; Friedel, E. et al.; Romanczuk-Seiferth , N.; Wittchen, H.-U.; Zimmermann, U.; Walter, H.; Sterzer, P.; Smolka, M.; Schlagenhauf, F.; Heinz, A.; Dayan, P.; Huys, Q.: Dissociating neural learning signals in human sign- and goal-trackers. Nature Human Behaviour 4 (2), pp. 201 - 214 (2020)

Stojić, H.; Orquin, J.; Dayan, P.; Dolan, R.; Speekenbrink, M.: Uncertainty in learning, choice and visual fixation. Proceedings of the National Academy of Sciences of the United States of America 117 (6), pp. 3291 - 3300 (2020)

Lloyd, K.; Sanborn, A.; Leslie, D.; Lewandowsky, S.: Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation. Cognitive Science 43 (12), e12805 (2019)

Wise, T.; Michely, J.; Dayan, P.; Dolan, R.: A computational account of threat-related attentional bias. PLoS Computational Biology 15 (10), pp. 1 - 21 (2019)

Zhao, S.; Chait, M.; Dick, F.; Dayan, P.; Furukawa, S.; Liao, H.: Pupil-linked phasic arousal evoked by violation but not emergence of regularity within rapid sound sequences. Nature Communications 10, 4030, pp. 1 - 16 (2019)

Javadi, A.; Patai, E.; Marin-Garcia, E.; Margolis, A.; Tan, H.-R.; Kumaran, D.; Nardini, M.; Duzel, E.; Dayan, P.; Spiers, H.: Prefrontal Dynamics Associated with Efficient Detours and Shortcuts: A Combined Functional Magnetic Resonance Imaging and Magnetoencenphalography Study. Journal of Cognitive Neuroscience 31 (8), pp. 1227 - 1247 (2019)

Javadi, A.-H.; Patai, E.; Marin-Garcia, E.; Margois, A.; Tan, H.-R.; Kumaran, D.; Nardini, M.; Penny, W.; Duzel, E.; Dayan, P. et al.; Spiers, H.: Backtracking during navigation is correlated with enhanced anterior cingulate activity and suppression of alpha oscillations and the "default-mode" network. Proceedings of the Royal Society B: Biological Sciences 286 (1908), 20191016, pp. 1 - 9 (2019)

Ahilan, S.; Solomon, R.; Breton, A.-Y.; Conover, K.; Niyogi, R.; Shizgal, P.; Dayan, P.: Learning to use past evidence in a sophisticated world model. PLoS Computational Biology 15 (6), pp. 1 - 20 (2019)

Dezfouli , A.; Griffiths, K.; Ramos, F.; Dayan, P.; Balleine, B.: Models that learn how humans learn: The case of decision-making and its disorders. PLoS Computational Biology 16 (6), pp. 1 - 33 (2019)

Rouault, M.; Dayan, P.; Fleming, S.: Forming global estimates of self-performance from local confidence. Nature Communications 10 (1), 1141, pp. 1 - 11 (2019)

Moran, R.; Keramati, M.; Dayan, P.; Dolan, R.: Retrospective model-based inference guides model-free credit assignment. Nature Communications 10, 750, pp. 1 - 14 (2019)

Moutoussis, M.; Bullmore, E.; Goodyer, I.; Fonagy, P.; Jones, P.; Dolan, R.; Dayan, P.: Change, stability, and instability in the Pavlovian guidance of behaviour from adolescence to young adulthood. PLoS Computational Biology 14 (12), pp. 1 - 26 (2018)

Brielmann, A.; Dayan, P.: Aesthetic boredom. In: The Routledge International Handbook of Boredom (Eds. Bieleke, M.; Wolff, W.; Martarelli, C.). Routledge, London, UK (2024)

Brielmann, A.: Top-down processes in art experience. In: The Routledge International Handbook of Neuroaesthetics, 25, pp. 461 - 474 (Eds. Skov, M.; Nadal, M.). Routledge, London, UK (2022)

Bellucci, G.; Dreher, J.-C.: Trust and Learning. In: The Neurobiology of Trust, 8, pp. 185 - 220 (Ed. Krueger, F.). Cambridge University Press, Cambridge, UK (2022)

Brielmann, A.: Empirical Aesthetics. In: Internet Encyclopedia of Philosophy (Eds. Fieser, J.; Dowden, B.) (2021)

Dayan, P.; Roiser, J.; Viding, E.: The first steps on long marches: The costs of active observation. In: Psychiatry Reborn: Biopsychosocial psychiatry in modern medicine, 14, pp. 213 - 228 (Eds. Davies, W.; Savulescu, J.; Roache, R.; Loebel, J.). Oxford University Press, Oxford, UK (2020)

Gouw, M.; Alvarado-Valverde, J.; Čalyševa, J.; Diella, F.; Kumar, M.; Michael, S.; Van Roey, K.; Dinkel, H.; Gibson, T.: How to Annotate and Submit a Short Linear Motif to the Eukaryotic Linear Motif Resource. In: Intrinsically Disordered Proteins: Methods and Protocols, pp. 73 - 102 (Eds. Kragelund, B.; Skriver, K.). Humana, New York, NY, USA (2020)

Dayan, P.; Nakahara, H.: Models and Methods for Reinforcement Learning. In: Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, Vol. 5: Methodology, 4. Ed., pp. 507 - 546 (Eds. Wixted, J.; Wagenmakers, E.-J.). Wiley, Hoboken, NJ, USA (2018)

Buckley, C.; Cialfi, D.; Lanillos, P.; Pitliya, R.; Sajid, N.; Shimazaki, H.; Verbelen, T.; Wisse, M. (Eds.): Active Inference: 5th International Workshop, IWAI 2024 (Communications in Computer and Information Science, 2193). IWAI 2024 International Workshop on Active Inference, Oxford, UK, September 09, 2024 - September 11, 2024. Springer, Cham, Switzerland (2024)

Whole brain dynamics: Modeling and applications. Satellite Workshops of the Bernstein Conference 2023: Whole brain dynamics: Modeling and applications, Berlin, Germany, September 26, 2023. (2023)

Multi-modal understanding of brain and behavior. Satellite Workshops of the Bernstein Conference 2023: Multi-modal understanding of brain and behavior, Berlin, Germany, September 26, 2023. (2023)

Rachum, R.; Nakar, Y.; Tomlinson, B.; Alon, N.; Mirsky, R.: Emergent Dominance Hierarchies in Reinforcement Learning Agents. In: Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XVII: International Workshop, COINE 2024, Auckland, New Zealand, May 7, 2024, pp. 41 - 56 (Eds. Cranefield, S.; Nardin, L.; Lloyd, N.). International Workshop Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XVII (COINE 2024) , Auckland, New Zealand, May 07, 2024. Springer, Cham, Switzerland (2025)

Chen, J.; Teng, T.; Shao, Y.; Zhang, H.: Why some simple probabilistic rules are difficult to learn: A hypothesis diffusion model. In: 2024 Conference on Cognitive Computational Neuroscience, C85. Conference on Cognitive Computational Neuroscience (CCN 2024), Boston, MA, USA, August 06, 2024 - August 09, 2024. (2024)

Lu, H.; Teng, T.; Zhang, H.: The Development of Superstitions in Uncontrollable Environment. In: 2024 Conference on Cognitive Computational Neuroscience, A8. Conference on Cognitive Computational Neuroscience (CCN 2024), Boston, MA, USA, August 06, 2024 - August 09, 2024. (2024)

Satti, M.; Wille, K.; Nassar, M.; Cichy, R.; Schuck, N.; Dayan, P.; Bruckner, R.: Absence of systematic effects of trait anxiety on learning under uncertainty. In: 7th Annual Conference on Cognitive Computational Neuroscience (CCN 2024). 7th Annual Conference on Cognitive Computational Neuroscience (CCN 2024), Boston, MA, USA, August 06, 2024 - August 09, 2024. (2024)

Satti, M.; Wille, K.; Nassar, M.; Cichy, R.; Schuck, N.; Dayan, P.; Bruckner, R.: Absence of Systematic Effects of Trait Anxiety on Learning Under Uncertainty. In: 2024 Conference on Cognitive Computational Neuroscience, B27. Conference on Cognitive Computational Neuroscience (CCN 2024), Boston, MA, USA, August 06, 2024 - August 09, 2024. (2024)

Sui, X.; Dayan, P.; Lloyd, K.: Exploring Optimal Risk-Sensitive Behavior in the Balloon Analogue Risk Task (BART). In: 2024 Conference on Cognitive Computational Neuroscience, A30. Conference on Cognitive Computational Neuroscience (CCN 2024), Boston, MA, USA, August 06, 2024 - August 09, 2024. (2024)

Chebolu, S.; Dayan, P.: Optimal and sub-optimal temporal decisions can explain procrastination in a real-world task. In: 46th Annual Meeting of the Cognitive Science Society (CogSci 2024), pp. 3102 - 3108. 46th Annual Meeting of the Cognitive Science Society (CogSci 2024) , Rotterdam, The Netherlands, July 24, 2024 - July 27, 2024. (2024)

Nazemorroaya, A.; Bang, D.; Dayan, P.: State-Independent and State-Dependent Learning in a Motivational Go/NoGo task. In: 46th Annual Conference of the Cognitive Science Society (CogSci 2024), pp. 2171 - 2178. 46th Annual Conference of the Cognitive Science Society (CogSci 2024), Rotterdam, The Netherlands, July 24, 2024 - July 27, 2024. (2024)

Shen, T.; Nath, S.; Brielmann, A.; Dayan, P.: Simplicity in Complexity: Explaining Visual Complexity using Deep Segmentation Models. In: 46th Annual Conference of the Cognitive Science Society (CogSci 2024), pp. 2017 - 2024. 46th Annual Conference of the Cognitive Science Society (CogSci 2024), Rotterdam, The Netherlands, July 24, 2024 - July 27, 2024. (2024)

Zhou, H.; Nagy, D.; Wu, C.: Harmonizing Program Induction with Rate-Distortion Theory. In: 46th Annual Meeting of the Cognitive Science Society (CogSci 2024), pp. 2511 - 2518. 46th Annual Meeting of the Cognitive Science Society (CogSci 2024), Rotterdam, the Netherlands, July 24, 2024 - July 27, 2024. (2024)

Nath, S.; Dayan, P.; Stevenson, C.: Characterising the Creative Process in Humans and Large Language Models. In: 15th International Conference on Computational Creativity: ICCC'24, 179. 15th International Conference on Computational Creativity (ICCC 2024), Jönköping, Sweden, June 17, 2024 - June 21, 2024. (2024)

Nath, S.; Shen, K.; Brielmann, A.; Dayan, P.: Simplicity in Complexity. In: ICLR 2024 Workshop on Representational Alignment (Re-Align). ICLR 2024 Workshop on Representational Alignment (Re-Align), Wien, Austria, May 11, 2024. (2024)

Saanum, T.; Éltetö, N.; Dayan, P.; Binz, M.; Schulz, E.: Reinforcement Learning with Simple Sequence Priors. In: Advances in Neural Information Processing Systems 36: 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 2710, pp. 61985 - 62005 (Eds. Oh, A.; Naumann, T.; Globerson, A.; Saenko, K.; Hardt, M. et al.). Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, LA, USA, December 10, 2023 - December 16, 2023. Curran, Red Hook, NY, USA (2024)

Antonov, G.; Dayan, P.: Exploring Uncertainty in Distributional Reinforcement Learning. Reinforcement Learning Conference (RLC 2024), Amherst, MA, USA, August 09, 2024 - August 12, 2024. Reinforcement Learning Journal 2, pp. 961 - 978 (2024)

Bucher, S.; Dayan, P.: Cognitive Information Filters: Algorithmic Choice Architecture for Boundedly Rational Consumers. In: NeurIPS 2023 workshop: Information-Theoretic Principles in Cognitive Systems: InfoCog@NeurIPS2023. NeurIPS 2023 workshop: Information-Theoretic Principles in Cognitive Systems: InfoCog@NeurIPS2023, New Orleans, LA, USA, December 15, 2023. (2023)

Safavi, S.: Methods for cross-scale analysis of neural data. In: Satellite Workshops of the Bernstein Conference 2023: Multi-modal understanding of brain and behavior. Satellite Workshops of the Bernstein Conference 2023: Multi-modal understanding of brain and behavior, Berlin, Germany, September 26, 2023 - September 27, 2023. (2023)

Bruijns, S.; Dayan, P.; The International Brain Laborartory: Understanding Learning Trajectories With Infinite Hidden Markov Models. In: 2023 Conference on Cognitive Computational Neuroscience, P-2.97, pp. 770 - 772. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, August 24, 2023 - August 27, 2023. (2023)

Ershadmanesh, S.; Gholamzadeh, A.; Desender, K.; Dayan, P.: Meta-cognitive Efficiency in Learned Value-based Choice. In: 2023 Conference on Cognitive Computational Neuroscience, P-1A.9, pp. 29 - 32. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, August 24, 2023 - August 27, 2023. (2023)

Hamidi, M.; Bányai, M.; Wu, C.: One bottleneck is not enough. In: 2023 Conference on Cognitive Computational Neuroscience, P-2B.44, pp. 795 - 798. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, August 24, 2023 - August 27, 2023. (2023)

Renz, F.; Grossman, S.; Schuck, N.; Dayan, P.; Doeller, C.: Learning and adapting cognitive maps for flexible decision-making. In: 2023 Conference on Cognitive Computational Neuroscience, P-3.35, pp. 999 - 1001. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, August 24, 2023 - August 27, 2023. (2023)

Rubino, V.; Dayan, P.; Wu, C.: Biases towards compositionally simpler hypotheses are robust and unaffected by learning. In: 2023 Conference on Cognitive Computational Neuroscience, P-2B.50, pp. 817 - 820. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, August 24, 2023 - August 27, 2023. (2023)

Safavi, S.; Dayan, P.: A decision-theoretic model of perceptual multistability: perceptual switches as internal actions. In: 2023 Conference on Cognitive Computational Neuroscience, P-3.41, pp. 1022 - 1024. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, August 24, 2023 - August 27, 2023. (2023)

Shen, T.; Dayan, P.: Risking your Tail: Curiosity, Danger & Exploration. In: 2023 Conference on Cognitive Computational Neuroscience, P-3.67, pp. 1113 - 1116. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, August 24, 2023 - August 27, 2023. (2023)

Shen, T.; Dayan, P.; Bányai, M.: Meta-cognitive planning for learning representations. In: 2023 Conference on Cognitive Computational Neuroscience, P-2.1, pp. 425 - 428. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, August 24, 2023 - August 27, 2023. (2023)

Xiong, Y.; Wu, C.; Moneta, N.; Banyai, M.: Selective memory for reward-relevant features is modulated by expertise during reward learning. In: 2023 Conference on Cognitive Computational Neuroscience, P-2.87, pp. 733 - 736. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, August 24, 2023 - August 27, 2023. (2023)

Alon, N.; Schulz, L.; Dayan, P.; Barnby, J.: Between prudence and paranoia: Theory of Mind gone right, and wrong. In: ICML 2023: First Workshop on Theory of Mind in Communicating Agents (ToM 2023). ICML 2023: First Workshop on Theory of Mind in Communicating Agents (ToM 2023), Honolulu, HI,USA, July 28, 2023. (2023)

Éltetö, N.; Dayan, P.: Habits of Mind: Reusing Action Sequences for Efficient Planning. 45th Annual Meeting of the Cognitive Science Society (CogSci 2023): Workshop "Compositionality in minds, brains and machines: a unifying goal that cuts across cognitive sciences", Sydney, Australia, July 26, 2023 - July 29, 2023. Proceedings of the Annual Meeting of the Cognitive Science Society 45, pp. 195 - 201 (2023)

Kurtz-David, V.; Alladi, V.; Bucher, S.; Brandenburger, A.; Louie, K.; Glimcher, P.; Tymula, A.: Choosers Adapt Value Coding to the Environment, But Do Not Attain Efficiency. 45th Annual Meeting of the Cognitive Science Society (CogSci 2023), Sydney, Australia, July 26, 2023 - July 29, 2023. Proceedings of the Annual Meeting of the Cognitive Science Society 45, pp. 2237 - 2244 (2023)

Rubino, V.; Hamidi, M.; Dayan, P.; Wu, C.: Compositionality under time pressure. 45th Annual Meeting of the Cognitive Science Society (CogSci 2023), Sydney, Australia, July 26, 2023 - July 29, 2023. Proceedings of the Annual Meeting of the Cognitive Science Society 45, pp. 678 - 685 (2023)

Nath, S.: Inside the Grid, yet Outside the Box: Computational Investigations of Human Creativity using Pixel Patterns. In: C&C '23: Proceedings of the 15th Conference on Creativity and Cognition, pp. 28 - 34. 15th Conference on Creativity and Cognition (C&C 2023), June 19, 2023 - June 21, 2023. Association for Computing Machinery, New York, NY, USA (2023)

Schulz, L.; Alon, N.; Rosenschein, J.; Dayan, P.: Emergent deception and skepticism via theory of mind. In: ICML 2023: First Workshop on Theory of Mind in Communicating Agents (ToM 2023). ICML 2023: First Workshop on Theory of Mind in Communicating Agents (ToM 2023), Honolulu, HI,USA, July 28, 2023. (submitted)

Wu, S.; Éltetö, N.; Dasgupta, I.; Schulz, E.: Learning Structure from the Ground up: Hierarchical Representation Learning by Chunking. In: Advances in Neural Information Processing Systems 35: 36th Conference on Neural Information Processing Systems (NeurIPS 2022), pp. 36706 - 36721 (Eds. Koyejo, S.; Mohamed, S.). Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, USA, November 28, 2022 - December 09, 2022. Curran, Red Hook, NY, USA (2023)

Khajehabdollahi, S.; Giannakakis, E.; Buendia, V.; Martius, G.; Levina, A.: Locally adaptive cellular automata for goal-oriented self-organization. In: ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference, pp. 410 - 419 (Eds. Iizuka, H.; Suzuki, K.; Uno, R.; Damiano, L.; Spychala, N. et al.). ALIFE 2023: Ghost in the Machine: The 2021 Conference on Artificial Life, Sapporo, Japan, July 24, 2023 - July 28, 2023. MIT Press (2023)

Alon, N.; Schulz, L.; Rosenschein, J.; Dayan, P.: A (dis-)information theory of revealed and unrevealed preferences. In: Information-Theoretic Principles in Cognitive Systems: Workshop at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022). NeurIPS 2022 Workshop on Information-Theoretic Principles in Cognitive Systems, New Orleans, LA, USA, December 03, 2022. (2022)

Bruijns, S.; The International Brain Laboratory; Dayan, P.: Understanding Learning Trajectories With Infinite Hidden Markov Models. In: 2022 Conference on Cognitive Computational Neuroscience, P-1.19, pp. 64 - 66. Conference on Cognitive Computational Neuroscience (CCN 2022), San Francisco, CA, USA, August 25, 2022 - August 28, 2022. (2022)

Khajehnejad, M.; Habibollahi, F.; Nock, R.; Arabzadeh, E.; Dayan, P.; Dezfouli, A.: Neural Network Poisson Models for Behavioural and Neural Spike Train Data. In: International Conference on Machine Learning, 17-23 July 2022, Baltimore, Maryland, USA, pp. 10974 - 10996 (Eds. Chaudhuri, K.; Jegelka, S.; Song, L.; Szepesvari, C.; Niu, G. et al.). Thirty-ninth International Conference on Machine Learning (ICML 2022), Baltimore, MD, USA, July 17, 2022 - July 23, 2022. (2022)

Renz, F.; Grossman, S.; Dayan, P.; Doeller, C.; Schuck, N.: Representation learning facilitates different levels of generalization. In: 2022 Conference on Cognitive Computational Neuroscience, P-2.66, pp. 460 - 462. Conference on Cognitive Computational Neuroscience (CCN 2022), San Francisco, CA, USA, August 25, 2022 - August 28, 2022. (2022)

Bröker, F.; Roads, B.; Dayan, P.; Love, B.: Teaching categories to human semi-supervised learners. In: 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2022), 2.8, pp. 122 - 125. 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2022), Providence, RI, USA, June 08, 2022 - June 11, 2022. (2022)

Dayan, P.; Gagne, C.: Two steps to risk sensitivity. In: Advances in Neural Information Processing Systems 34: 35th Conference on Neural Information Processing Systems (NeurIPS 2021), pp. 22209 - 22220 (Eds. Ranzato, M.; Beygelzimer, A.; Liang, P.; Vaughan, J.; Dauphin, Y.). Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), December 06, 2021 - December 14, 2021. Curran, Red Hook, NY, USA (2022)

Wise, T.; Charpentier, C.; Dayan, P.; Mobbs, D.: Modeling the mind of a predator: Interactive cognitive maps enable avoidance of dynamic threats. In: 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2022), 1.49, pp. 32 - 33. 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2022), Providence, RI, USA, June 08, 2022 - June 11, 2022. (2022)

Wu, S.; Éltetö, N.; Dasgupta, I.; Schulz, E.: Learning Structure from the Ground up: Hierarchical Representation Learning by Chunking. In: Tenth International Conference on Learning Representations (ICLR 2022). Tenth International Conference on Learning Representations (ICLR 2022), April 25, 2022 - April 29, 2022. (2022)

Gagne, C.; Dayan, P.: Catastrophe, Compounding & Consistency in Choice. In: Workshop on Human and Machine Decisions @ NeurIPS 2021 (WHMD 2021). Workshop on Human and Machine Decisions @ NeurIPS 2021 (WHMD 2021), December 14, 2021. (2021)

Khajehabdollahi, S.; Giannakakis, E.; Prosi, J.; Levina, A.: Reservoir computing with self-organizing neural oscillators. In: Artificial Life Conference Proceedings, Vol. 2021, 78. ALIFE 2021: The 2021 Conference on Artificial Life, Praha, Czech Republic, July 19, 2021 - July 23, 2021. MIT Press (2021)

Prosi, J.; Khajehabdollahi, S.; Giannakakis, E.; Martius, G.; Levina, A.: The dynamical regime and its importance for evolvability, task performance and generalization. In: Artificial Life Conference Proceedings, Vol. 2021, 79. ALIFE 2021: The 2021 Conference on Artificial Life, Praha, Czech Republic, July 19, 2021 - July 23, 2021. MIT Press (2021)

Tano, P.; Dayan, P.; Pouget, A.: A local temporal difference code for distributional reinforcement learning. In: Advances in Neural Information Processing Systems 33, pp. 13662 - 13673 (Eds. Larochelle, H.; Ranzato, M.; Hadsell, R.; Balcan, M.-F.; Lin, H.-T.). 34th Conference on Neural Information Processing Systems (NeurIPS 2020), December 06, 2020 - December 12, 2020. Curran, Red Hook, NY, USA (2021)

Ahilan, S.; Dayan, P.: Correcting Experience Replay for Multi-Agent Communication. In: Ninth International Conference on Learning Representations (ICLR 2021). Ninth International Conference on Learning Representations (ICLR 2021), Vienna, Austria, May 03, 2021 - May 07, 2021. (2021)

Xia, L.; Master, S.; Eckstein, M.; Wilbrecht, L.; Collins, A.: Learning under uncertainty changes during adolescence. In: 42nd Annual Meeting of the Cognitive Science Society (CogSci 2020): 5Developing a Mind: Learning in Humans, Animals, and Machines, pp. 716 - 722 (Eds. Denison, S.; Mack, M.; Xu, Y.; Armstrong, B.). 42nd Annual Virtual Meeting of the Cognitive Science Society (CogSci 2020), Toronto, Canada, July 29, 2020 - August 01, 2020. Curran, Red Hook, NY, USA (2020)

Sezener, E.; Dayan, P.: Static and Dynamic Values of Computation in MCTS. 36th Conference on Uncertainty in Artificial Intelligence (UAI 2020), August 03, 2020 - August 06, 2020. Proceedings of Machine Learning Research (PMLR) 124, 26, pp. 31 - 40 (2020)