Reading List

*Note that some articles contain studies using neuroimaging. You are welcome to read these portions for fun, but we will mostly focus on the psychological/behavioral aspects of these studies.

Introduction to Modeling

  • Guest, O., & Martin, A. E. (2021). How computational modeling can force theory building in psychological science. Perspectives on Psychological Science, 1–14.

  • Jolly, E., & Chang, L. J. (2017). The flatland fallacy: Moving beyond low dimensional thinking. Topics in Cognitive Science, 1–30.

  • Wilson, R. C., & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. eLife.

  • Jones, B. A., & Rachlin, H. (2006). Social discounting. Psychological Science, 17(4), 283–286.


Cushman, F., & Gershman, S. (2019). Editors’ introduction: Computational approaches to social cognition. Topics in Cognitive Science, 11(2), 281–298.

Reinforcement Learning


  • Chase, H. W., Kumar, P., Eickhoff, S. B., & Dombrovski, A. Y. (2015). Reinforcement learning models and their neural correlates: An activation likelihood estimation meta-analysis. Cognitive, Affective and Behavioral Neuroscience, 15(2), 435–459.

  • Rangel, A., Camerer, C., & Montague, P. R. (2008). A framework for studying the neurobiology of value-based decision making. Nature Reviews Neuroscience, 9(7), 545–556.

Modeling Social Behavior

  • Lockwood, P. L., Apps, M. A. J., & Chang, S. W. C. (2020). Is there a “social” brain? Implementations and algorithms. Trends in Cognitive Sciences, 1–12.

  • Zhang, L., Lengersdorff, L., Mikus, N., Gläscher, J., & Lamm, C. (2020). Using reinforcement learning models in social neuroscience: Frameworks, pitfalls and suggestions of best practices. Social Cognitive and Affective Neuroscience, 15(6), 695-707.

  • Suzuki, S., & O’Doherty, J. P. (2020). Breaking human social decision making into multiple components and then putting them together again. Cortex, 127, 221–230.


  • Charpentier, C. J., & O’Doherty, J. P. (2018). The application of computational models to social neuroscience: promises and pitfalls. Social Neuroscience, 13(6), 637-647.

  • Heyes, C. (2012). What’s social about social learning? Journal of Comparative Psychology, 126(2), 193–202.

  • Joiner, J., Piva, M., Turrin, C., & Chang, S. W. C. (2017). Social learning through prediction error in the brain. Science of Learning, 2(1), 8.

  • Lockwood, P. L., & Klein-Flügge, M. (2020). Computational modelling of social cognition and behaviour—a reinforcement learning primer. Social Cognitive and Affective Neuroscience, 1–11.

Learning from and for others

Threat Learning (Example)

  • Lindström, B., Golkar, A., Jangard, S., Tobler, P. N., & Olsson, A. (2019). Social threat learning transfers to decision making in humans. Proceedings of the National Academy of Sciences, 116(10), 4732-4737.

Prosocial Learning (Example)

  • Lockwood, P. L., Apps, M. A. J., Valton, V., Viding, E., & Roiser, J. P. (2016). Neurocomputational mechanisms of prosocial learning and links to empathy. Proceedings of the National Academy of Sciences, 113(35), 9763–9768.

  • Lengersdorff, A. L. L., Wagner, I. C., & Lamm, C. (2020). When implicit prosociality trumps selfishness: the neural valuation system underpins more optimal choices when learning to avoid harm to others than to oneself. Journal of Neuroscience, 40(38), 7286–7299.


  • Aquino, T. G., Minxha, J., Dunne, S., Ross, I. B., Mamelak, A. N., Rutishauser, U., & Doherty, J. P. O. (2020). Value-related neuronal responses in the human amygdala during observational learning. The Journal of Neuroscience, 40(24), 4761–4772.

  • Burke, C. J., Tobler, P. N., Baddeley, M., & Schultz, W. (2010). Neural mechanisms of observational learning. Proceedings of the National Academy of Sciences.

  • Crockett, M. J., Kurth-Nelson, Z., Siegel, J. Z., Dayan, P., & Dolan, R. J. (2014). Harm to others outweighs harm to self in moral decision making. Proceedings of the National Academy of Sciences, 111(48), 17320–17325.

  • Hill, M. R., Boorman, E. D., & Fried, I. (2016). Observational learning computations in neurons of the human anterior cingulate cortex. Nature Communications, 7.

  • Lockwood, P. L., Hamonet, M., Zhang, S. H., Ratnavel, A., Salmony, F. U., Husain, M., & Apps, M. A. J. (2017). Prosocial apathy for helping others when effort is required. Nature Human Behaviour, 1(7), 1–10.

  • Lockwood, P., Klein-Flügge, M., Abdurahman, A., & Crockett, M. (2020). Neural signatures of model-free learning when avoiding harm to self and other. Proceedings of the National Academy of Sciences.

  • Olsson, A., McMahon, K., Papenberg, G., Zaki, J., Bolger, N., & Ochsner, K. N. (2016). Vicarious fear learning depends on empathic appraisals and trait empathy. Psychological Science, 27(1), 25–33.

Learning and updating beliefs about others

Theory of Mind (Example)

  • Camerer, C., Ho, T., & Chong, K. (2003). Models of thinking, learning, and teaching in games. American Economic Review, 93(2), 192-195.

  • Rusch, T., Steixner-Kumar, S., Doshi, P., Spezio, M., & Gläscher, J. (2020). Theory of mind and decision science: Towards a typology of tasks and computational models. Neuropsychologia, 146(April), 107488.

Trustworthiness (Example)

  • Chang, L. J., Doll, B. B., van ’t Wout, M., Frank, M. J., & Sanfey, A. G. (2010). Seeing is believing: Trustworthiness as a dynamic belief. Cognitive Psychology, 61(2), 87–105.

  • Fareri, D. S., Chang, L. J., & Delgado, M. R. (2015). Computational substrates of social value in interpersonal collaboration. Journal of Neuroscience, 35(21), 8170–8180.

Morality (Example)

  • Siegel, J. Z., Mathys, C., Rutledge, R. B., & Crockett, M. J. (2018). Beliefs about bad people are volatile. Nature Human Behaviour, 2(10), 750–756.


  • Anzellotti, S., & Young, L. L. (2019). The acquisition of person knowledge. Annual Review of Psychology, 1–32.

  • Baker, C. L., Jara-Ettinger, J., Saxe, R., & Tenenbaum, J. B. (2017). Rational quantitative attribution of beliefs, desires and percepts in human mentalizing. Nature Human Behaviour, 1(4), 1–10.

  • Boorman, E. D., O’Doherty, J. P., Adolphs, R., & Rangel, A. (2013). The behavioral and neural mechanisms underlying the tracking of expertise. Neuron, 80(6), 1558–1571. Crockett, M. J. (2016). How formal models can illuminate mechanisms of moral judgment and decision making. Current Directions in Psychological Science, 25(2), 85–90.

  • Devaine, M., Hollard, G., & Daunizeau, J. (2014). The social bayesian brain: Does mentalizing make a difference when we learn? PLoS Computational Biology, 10(12).

  • Devaine, M., & Daunizeau, J. (2017). Learning about and from others’ prudence, impatience or laziness: The computational bases of attitude alignment. PLoS Computational Biology, 13(3), 1–28.

  • Hackel, L. M., Doll, B. B., & Amodio, D. M. (2015). Instrumental learning of traits versus rewards: Dissociable neural correlates and effects on choice. Nature Neuroscience, 18(9), 1233–1235.

  • Jara-Ettinger, J. (2019). Theory of mind as inverse reinforcement learning. Current Opinion in Behavioral Sciences, 29, 105–110.

  • Kim, M., Park, B. K., & Young, L. (2020). The psychology of motivated versus rational impression updating. Trends in Cognitive Sciences, 24(2), 101–111.

  • Lockwood, P. L., Wittmann, M. K., Apps, M. A. J., Klein-Flügge, M. C., Crockett, M. J., Humphreys, G. W., & Rushworth, M. F. S. (2018). Neural mechanisms for learning self and other ownership. Nature Communications, 9(1), 4747.

  • Rosenblau, G., Korn, C. W. & Pelphrey, K. A. (2018). A computational account of optimizing social predictions reveals that adolescents are conservative learners in social contexts. Journal of Neuroscience, 38, 974–988.

  • Rosenblau, G., Korn, C. W., Dutton, A., Lee, D., & Pelphrey, K. A. (2021). Neurocognitive mechanisms of social inferences in typical and autistic adolescents. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(8), 782–791.

  • Rosenthal, I. A., Hutcherson, C. A., Adolphs, R., & Stanley, D. A. (2019). Deconstructing theory-of-mind impairment in high-functioning adults with autism. Current Biology, 29(3), 513-519.

  • Todorov, A. (2018). Flexible updating of beliefs in order to forgive. Nature Human Behaviour, 2(10), 722–723.

Social influence

Integrating Personal and Social Information (Example)

  • Tump, A. N., Pleskac, T., & Kurvers, R. H. J. M. (2020). Wise or mad crowds? The cognitive mechanisms underlying information cascades. Science Advances, 6, 1–12.

Self-Esteem (Example)

  • Will, G.-J., Rutledge, R. B., Moutoussis, M., & Dolan, R. J. (2017). Neural and computational processes underlying dynamic changes in self-esteem. ELife, 6, 1–21.


  • Zhang, L., & Gläscher, J. (2019). A brain network supporting social influences in human decision-making. Science Advances, 6(34), 1–19.

  • Xiang, T., Lohrenz, T., & Montague, P. R. (2013). Computational substrates of norms and their violations during social exchange. Journal of Neuroscience, 33(3), 1099–1108.

Current and Future Directions

Individual Differences

  • Patzelt, E. H., Hartley, C. A., & Gershman, S. J. (2018). Computational phenotyping: Using models to understand individual differences in personality, development, and mental illness. Personality Neuroscience, 1.

Altrustic Choice

  • Hutcherson, C. A., Bushong, B., & Rangel, A. (2015). A neurocomputational model of altruistic choice and its implications. Neuron, 87(2), 451–463.


  • Shaw, D. J., Czekóová, K., Staněk, R., Mareček, R., Urbánek, T., Špalek, J., Kopečková, L., Řezáč, J., & Brázdil, M. (2018). A dual-fMRI investigation of the iterated Ultimatum Game reveals that reciprocal behaviour is associated with neural alignment. Scientific Reports, 8(1), 1–13.

Inequity Aversion

  • Sáez, I., Zhu, L., Set, E., Kayser, A., & Hsu, M. (2015). Dopamine modulates egalitarian behavior in humans. Current Biology, 25(7), 912–919.

Social Interaction

  • Dumas, G., de Guzman, G. C., Tognoli, E., & Kelso, J. A. S. (2014). The human dynamic clamp as a paradigm for social interaction. Proceedings of the National Academy of Sciences, 111(35), 3726–3734.


  • Huys, Q. J. M., Browning, M., Paulus, M., & Frank, M. J. (2020). Advances in the computational understanding of mental illness. Neuropsychopharmacology, May, 1–17.

Rigor & Reproducibility

  • Poldrack, R. A., Feingold, F., Frank, M. J., Gleeson, P., de Hollander, G., Huys, Q. J. M., Love, B. C., Markiewicz, C. J., Moran, R., Ritter, P., Turner, B. M., Yarkoni, T., Zhan, M., & Cohen, J. D. (2019). The importance of standards for sharing of computational models and data. Computational Brain & Behavior, 2, 229–232.