_{Publications:
Department for Computational Neuroscience}

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)

Hall-McMaster, S.; Dayan, P.; Schuck, N.: Control over patch encounters changes foraging behaviour. iScience Epub ahead (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)

Brielmann, A.; Pelli, D.: Correction to: The pleasure of multiple images. Attention, Perception & Psychophysics Epub ahead (2020)

Bröker, F.; Ramscar, M.: Representing absence of evidence: why algorithms and representations matter in models of language and cognition. Language, Cognition and Neuroscience Epub ahead (2020)

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, pp. 1 - 9 (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)

Brielmann, A.; Pelli, D.: The pleasure of multiple images. Attention, Perception & Psychophysics Epub ahead (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)

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, pp. 1 - 16 (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), pp. 1 - 43 (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)

Na, S.; Chung, D.; Jung, J.; Hula, A.; Fiore, V.; Dayan, P.; Gu, X.: Humans Use Forward Thinking to Exert Social Control. Neuron (accepted)

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)

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. 1 - 40 (Eds. Wixted, J.; Wagenmakers, E.-J.). Wiley, Hoboken, NJ, USA (2018)

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)

Tano, P.; Dayan, P.; Pouget, A.: A local temporal difference code for distributional reinforcement learning. In: Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020). Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020), December 07, 2020 - December 12, 2020. (accepted)

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 04, 2021 - May 08, 2021. (submitted)

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)

Browning, M.; Carter, C.; Chatham, C.; Den Ouden, H.; Gillan, C.; Baker, J.; Chekroud, A.; Cools, R.; Dayan, P.; Gold, J. et al.; Goldstein, R.; Hartley, C.; Kepecs, A.; Lawson, R.; Mourao-Miranda, J.; Phillips, M.; Pizzagalli, D.; Powers, A.; Rindskopf, D.; Roiser, J.; Schmack, K.; Schiller, D.; Sebold, M.; Stephan, K.; Frank, M.; Huys, Q.; Paulus, M.: Realizing the Clinical Potential of Computational Psychiatry: Report from the Banbury Center Meeting, February 2019. Banbury Center Meeting 2019, Huntington, NY, USA, 2019-02. Biological Psychiatry 88 (2), pp. e5 - e10 (2020)

Dezfouli, A.; Ashtiani, H.; Ghattas, O.; Nock, R.; Dayan, P.; Ong, C.: Disentangled behavioural representations. In: Advances in Neural Information Processing Systems 32, pp. 2243 - 2252 (Eds. Wallach, H.; Larochelle, H.; Beygelzimer , A.; d'Alché-Buc, F.; Fox, E. et al.). Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, December 09, 2019 - December 13, 2019. Curran, Red Hook, NY, USA (2020)

Jain, Y.; Gupta, S.; Rakesh, V.; Dayan, P.; Callaway, F.; Lieder, F.: How do people learn how to plan? In: Conference on Cognitive Computational Neuroscience (CCN 2019), PS-2A.70, pp. 826 - 829. Conference on Cognitive Computational Neuroscience (CCN 2019), Berlin, Germany, September 13, 2019 - September 16, 2019. (2019)

Ahilan, S.; Dayan, P.: Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning. In: Annual Conference of the American Library Association (ALA 2019), 5, pp. 1 - 5. Annual Conference of the American Library Association (ALA 2019) , Washington, DC, USA, June 20, 2019 - June 25, 2019. (2019)

Ahilan, S.; Dayan, P.: Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning. In: Workshop on Structure & Priors in Reinforcement Learning (SPiRL 2019) at ICLR 2019, pp. 1 - 11. Workshop on Structure & Priors in Reinforcement Learning (SPiRL 2019) at ICLR 2019, New Orleans, LA, USA, May 06, 2019. (2019)

Dayan, P.: Impact beyond neuroscience (translational non-clinical). In FENS 2020 Virtual Forum, 1522. FENS 2020 Virtual Forum, Glasgow, UK, July 11, 2020 - July 15, 2020. (2020)

Rouault, C.; Dayan, P.; Fleming, S.: Forming global estimates of self-performance from local confidence. In 61st Conference of Experimental Psychologists: 61. TeaP 2019 (Tagung Experimentell Arbeitender PsychologInnen) (Ed. Lange-Küttner, C.). 61st Conference of Experimental Psychologists (TEAP 2019), London, UK, April 15, 2019 - April 17, 2019. Pabst, Lengerich, Germany (2020)

Dayan, P.; Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dolan, R.: Anticipation, imagination and information seeking via mid-brain, hippocampal, and prefrontal interactions. In 49th Annual Meeting of the Society for Neuroscience (Neuroscience 2019), 018.12, pp. 103 - 104. 49th Annual Meeting of the Society for Neuroscience (Neuroscience 2019), Chicago, IL, USA, October 19, 2019 - October 23, 2019. (2019)

Mendl, M.; Jones, S.; Neville, V.; Higgs, L.; Robinson, E.; Dayan, P.; Paul, E.: An automated and self-initiated judgement bias task based on natural investigative behaviour. In Applied Ethology 2019: Animal lives worth living: 53rd Congress of the International Society of Applied Ethology (ISAE 2019), p. 126 (Eds. Newberry, R.; Braastad, B.). 53rd Congress of the International Society of Applied Ethology (ISAE 2019), Bergen, Norway, August 05, 2019 - August 09, 2019. Wageningen Academic Publishers, Wageningen, The Netherlands (2019)

Neville, V.; Paul, L.; Dayan, P.; Gilchrist, I.; Mendl, M.: Investigating animal affect and welfare using computational modelling. In Applied Ethology 2019: Animal lives worth living: 53rd Congress of the International Society of Applied Ethology (ISAE 2019), p. 127 (Eds. Newberry, R.; Braastad, B.). 53rd Congress of the International Society of Applied Ethology (ISAE 2019), Bergen, Norway, August 05, 2019 - August 09, 2019. Wageningen Academic Publishers, Wageningen, The Netherlands (2019)

Vilares, I.; Nolte, T.; Hula, A.; Cui, Z.; Fonagy, P.; Zhu, L.; Chiu, P.; King-Casas, B.; Lohrenz, T.; Dayan, P. et al.; Montague, R.: Computational Phenotyping in Borderline Personality Using a Role-Based Social Hierarchy Probe. In Biological Psychiatry, 85 (10 Supplement), O8, p. S108. 74th Annual Meeting of the Society of Biological Psychiatry, Chicago, IL, USA, May 16, 2019 - May 18, 2019. Elsevier, New York (2019)

Dayan, P.: Peril, Prudence and Planning as Risk, Avoidance and Worry. Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands (2021)

Dayan, P.; Gagne, C.: Peril, Prudence and Planning as Risk, Avoidance and Worry. The Fields Institute for Research in Mathematical Sciences: 2020-2021 Machine Learning Advances and Applications Seminar, Toronto, Canada (2021)

Dayan, P.: Replay and Preplay in Human Planning. Jülich Forschungszentrum: Institute of Neuroscience and Medicine, Computational and Systems Neuroscience (INM-6 / IAS-6), Jülich, Germany (2020)

Dinkel, H.: Version Control with Git & GitHub. EMBL Course: Computing Skills For Reproducible Research: Software Carpentry - Virtual (2020)

Dayan, P.: Replay in Offline Planning. Pattern Recognition in Neuroimaging Summer School 2020 (PRNI), Wien, Austria (2020)

Dayan, P.: Replay. 43th Annual Meeting of the Japan Neuroscience Society, Kobe, Japan (2020)

Dayan, P.: Modelling and Manipulating Behaviour Using Recurrent Networks. Machine Learning in Science: Virtual Cluster Conference, Tübingen, Germany (2020)

Laurent, G.; Yaksi, E.; Lüscher, C.; Ming, G.-L.; Roberts, A.; Dayan, P.; Bale, T.; Tracey, I.: The Brain Debate. FENS 2020 Virtual Forum, Glasgow, UK (2020)

Dayan, P.: Machine learning for Computational Neuroscience. Machine Learning Summer School (MLSS 2020), Tübingen, Germany (2020)

Dayan, P.: All Models Are Wronged. Gordon Research Conference: Functions of the Basal Ganglia from Thought to Action , Ventura, CA, USA (2020)

Dayan, P.; Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dolan, R.: Savouring and its Modulation by Prediction Errors. Departmental Colloquium Max Planck for Biological Cybernetics, Tübingen, Germany (2019)

Dinkel, H.: Introduction of Linear motifs. EMBO Practical Course: Computational analysis of protein-protein interactions in cell function and disease, Bangalore, India (2019)

Dinkel, H.; Gibson, T.; Kumar, M.: Linear motif biology and prediction, cooperativity in cellular signaling (I). EMBO Practical Course: Computational analysis of protein-protein interactions in cell function and disease, Bangalore, India (2019)

Kumar, M.; Dinkel, H.; Gibson, T.: Linear motif biology and prediction, cooperativity in cellular signaling (II). EMBO Practical Course: Computational analysis of protein-protein interactions in cell function and disease, Bangalore, India (2019)

Sharan, M.; Dinkel, H.: Using informal meetings for building local (bioinformatics) communities. EMBO Practical Course: Computational analysis of protein-protein interactions in cell function and disease, Bangalore, India (2019)

Dayan, P.: Replay and Preplay in Human Planning. Université de Genève: C. Lüscher Synapses, Circuits and Behaviour in Addiction and Related Disorders, Genève, Switzerland (2019)

Dayan, P.: Neural Reinforcement Learning. Humboldt Lecture Series at Tübingen University , Tübingen, Germany (2019)

Dayan, P.; Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dolan, R.: Savouring and its Modulation by Prediction Errors. Colloquium Cognitive Systems: Universität Ulm , Ulm, Germany (2019)

Dayan, P.: Savouring and its Modulation by Prediction Errors. 17th Annual Meeting of the Society for NeuroEconomics (SNE 2019), Dublin, Ireland (2019)

Dayan, P.: Theoretical Neuroscience: Decision Making and its Discontents. 49th Annual Meeting of the Society for Neuroscience (Neuroscience 2019) , Chicago, IL, USA (2019)

Sharan, M.; Dinkel, H.: Building Blocks of Python Programming. EMBL Course: Computing Skills For Reproducible Research: Software Carpentry, Heidelberg, Germany (2019)

Sharan, M.; Dinkel, H.; Alves, R.: Building programs with Python I. EMBL Course: Computing Skills For Reproducible Research: Software Carpentry, Heidelberg, Germany (2019)

Sharan, M.; Dinkel, H.; Alves, R.: Building programs with Python II. EMBL Course: Computing Skills For Reproducible Research: Software Carpentry, Heidelberg, Germany (2019)

Sharan, M.; Dinkel, H.; Alves, R.: Using commandline git for version control. EMBL Course: Computing Skills For Reproducible Research: Software Carpentry, Heidelberg, Germany (2019)

Dayan, P.: The cortical dynamics of integrative decision making. 6th ESI Systems Neuroscience Conference 2019: The recurrent cortex: feedback, dynamics, and dimensionality , Frankfurt a.M., Germany (2019)

Dayan, P.: Unsupervised Yearning. David Marr, 50 years on (Marr 2019) at Artificial & Biological Cognition 2019, Cambridge, UK (2019)

Dayan, P.: Dysfunctional decision-making. Tübingen International Summer School (TISS 2019), Kloster Heiligkreuztal (2019)

Dayan, P.: Functional decision-making. Tübingen International Summer School (TISS 2019), Kloster Heiligkreuztal (2019)

Dayan, P.: Reinforcement learning. 2019 Computational Psychiatry Summer Course, New York, NY, USA (2019)

Dayan, P.: Theoretical approaches to function and dysfunction. 2019 Computational Psychiatry Summer Course, New York, NY, USA (2019)

Dayan, P.: Slothful serial: perilous parallel processing. Workshop Heuristics, Hacks and Habits: 41st Annual Meeting of the Cognitive Science Society (COGSCI 2019), Montréal, Canada (2019)

Dayan, P.: Decision-making and some of its Discontents. 9th IMPRS NeuroCom Summer School in Cognitive Neuroscience , Leipzig, Germany (2019)

Dayan, P.: Savouring and its Modulation by Prediction Errors. NEUREX Workshop: Neuroeconomics: Studying Decision Making in an Economic Context, Strasbourg, France (2019)

Dayan, P.: Pavlovian-Instrumental Interactions in Active Avoidance: The Bark of Neutral Trials. Modulation of Neural Circuits and Behavior Gordon Research Conference: Neuromodulatory Mechanisms Underlying Flexible Brain Functions and Behavior , Les Diablerets, Switzerland (2019)

Dayan, P.: The Cortical Dynamics of Integrative Decision Making. University of Freiburg: Bernstein Seminar, Freiburg i.Br., Germany (2019)

Dayan, P.: Learning in Natural Neural Systems. Eberhard-Karls-Universität Tübingen: Studium Generale: Maschinelles Lernen, Tübingen, Germany (2019)

Dayan, P.: The long and the short of serotonergic stimulation: The learning rate for rewards. NeuroRetreat 2019: Max Planck Institute of Psychiatry, München, Germany (2019)

Dayan, P.: The Good, The Bad, and Something Inbetween: Dopamine in Active Avoidance. Centro Champalimaud, Lisboa, Portugal (2019)

Dayan, P.: The long and the short of 5-HT stimulation: optogenetic activation of dorsal raphe serotonergic neurons changes the learning rate for rewards. COSYNE 2019 Workshop: Advances and convergences in 5-HT research , Cascais, Portugal (2019)

Dayan, P.: Neural Reinforcement Learning 3: The Self and the Other. Winter School on Quantitative Systems Biology: Learning and Artificial Intelligence, Trieste, Italy (2018)

Dayan, P.: Neural Reinforcement Learning 2: Choice. Winter School on Quantitative Systems Biology: Learning and Artificial Intelligence, Trieste, Italy (2018)

Dayan, P.: Neural Reinforcement Learning 1: Prediction. Winter School on Quantitative Systems Biology: Learning and Artificial Intelligence, Trieste, Italy (2018)

Dayan, P.: The long and the short of serotonergic stimulation: Selective effects on the learning rate of rewards. InstitutsSeminar Neurobiologie: Institute of Neurobiology, Eberhard-Karls Universität Tübingen, Tübingen, Germany (2018)

Tano, P.; Dayan, P.; Pouget, A.: A Local Temporal Difference Code for Distributional Reinforcement Learning. Computational and Systems Neuroscience Meeting (COSYNE 2021) (2021)

Lekscha Sedlinská, T.; Brückner, L.; Hübner, M.; Rentsch, C.; Falke, D.; Mühle, C.; Suc, J.; Weinland, C.; Kornhuber, J.; Lenz, B. et al.; Dayan, P.: Temporal discounting and male depression. Kongress der Deutschen Gesellschaft für Psychiatrie und Psychotherapie, Psychosomatik und Nervenheilkunde e. V. (DGPPN 2020), Berlin, Germany (2020)

Bröker, F.; Love, B.; Dayan, P.: The varied effect of unsupervised information on human category learning. Bernstein Conference 2020 (2020)