Skip to main content Accessibility help

Computational Phenotyping: Using Models to Understand Individual Differences in Personality, Development, and Mental Illness

  • Edward H. Patzelt (a1), Catherine A. Hartley (a2) and Samuel J. Gershman (a1)


This paper reviews progress in the application of computational models to personality, developmental, and clinical neuroscience. We first describe the concept of a computational phenotype, a collection of parameters derived from computational models fit to behavioral and neural data. This approach represents individuals as points in a continuous parameter space, complementing traditional trait and symptom measures. One key advantage of this representation is that it is mechanistic: The parameters have interpretations in terms of cognitive processes, which can be translated into quantitative predictions about future behavior and brain activity. We illustrate with several examples how this approach has led to new scientific insights into individual differences, developmental trajectories, and psychopathology. We then survey some of the challenges that lay ahead.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the or variations. ‘’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Computational Phenotyping: Using Models to Understand Individual Differences in Personality, Development, and Mental Illness
      Available formats

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Computational Phenotyping: Using Models to Understand Individual Differences in Personality, Development, and Mental Illness
      Available formats

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Computational Phenotyping: Using Models to Understand Individual Differences in Personality, Development, and Mental Illness
      Available formats


This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.

Corresponding author

*Author for correspondence: Edward H. Patzelt, E-mail:


Hide All

Cite this article: Patzelt EH, Hartley CA, Gershman SJ. (2018) Computational Phenotyping: Using Models to Understand Individual Differences in Personality, Development, and Mental Illness. Personality Neuroscience. Vol 1: e18, XX: 1-10. doi:10.1017/pen.2018.14

Inaugural Invited Paper



Hide All
Abram, S. V.DeYoung, C. G. (2017). Using personality neuroscience to study personality disorder. Personality Disorders: Theory, Research, and Treatment, 8, 213.
Adams, R. A., Huys, Q. J. M.Roiser, J. P. (2015). Computational psychiatry: Towards a mathematically informed understanding of mental illness. Journal of Neurology, Neurosurgery, and Psychiatry, 87, 5363.
Alloway, T. P.Alloway, R. G. (2010). Investigating the predictive roles of working memory and IQ in academic attainment. Journal of Experimental Child Psychology, 106, 2029.
Bellman, R. E. (1957). Dynamic programming. Dover, NJ: Princeton University Press.
Bogg, T.Roberts, B. W. (2013). The case for conscientiousness: Evidence and implications for a personality trait marker of health and longevity. Annals of Behavioral Medicine, 45, 278288.
Borsboom, D., Mellenbergh, G. J.van Heerden, J. (2004). The concept of validity. Psychological Review, 111, 10611071.
Borsboom, D., Rhemtulla, M., Cramer, A. O. J., van der Maas, H. L. J., Scheffer, M.Dolan, C. V. (2016). Kinds versus continua: A review of psychometric approaches to uncover the structure of psychiatric constructs. Psychological Medicine, 46, 15671579.
Brodersen, K. H., Deserno, L., Schlagenhauf, F., Lin, Z., Penny, W. D., Buhmann, J. M., & Stephan, K. E. (2014). Dissecting psychiatric spectrum disorders by generative embedding. NeuroImage: Clinical, 4, 98111.
Brodersen, K. H., Schofield, T. M., Leff, A. P., Ong, C. S., Lomakina, E. I., Buhmann, J. M., & Stephan, K. E. (2011). Generative embedding for model-based classification of fMRI data. PLoS Computational Biology, 7, e1002079.
Casey, B. J., Getz, S.Galvan, A. (2008). The adolescent brain. Developmental Review, 28, 6277.
Cassey, P. J., Gaut, G., Steyvers, M.Brown, S. D. (2016). A generative joint model for spike trains and saccades during perceptual decision-making. Psychonomic Bulletin & Review, 23, 17571778.
Christakou, A., Gershman, S. J., Niv, Y., Simmons, A., Brammer, M.Rubia, K. (2013). Neural and psychological maturation of decision-making in adolescence and young adulthood. Journal of Cognitive Neuroscience, 25, 18071823.
Culbreth, A. J., Westbrook, A., Daw, N. D., Botvinick, M.Barch, D. M. (2016). Reduced model-based decision-making in schizophrenia. Journal of Abnormal Psychology, 125, 777787.
Cuthbert, B. N.Insel, T. R. (2013). Toward the future of psychiatric diagnosis: The seven pillars of RDoC. BMC Medicine, 11, 126.
Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P.Dolan, R. J. (2011). Model-based influences on humans’ choices and striatal prediction errors. Neuron, 69, 12041215.
Deary, I. J., Penke, L.Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11, 201211.
Deary, I. J., Strand, S., Smith, P.Fernandes, C. (2007). Intelligence and educational achievement. Intelligence, 35, 1321.
Decker, J. H., Otto, A. R., Daw, N. D.Hartley, C. A. (2016). From creatures of habit to goal-directed learners: Tracking the developmental emergence of model-based reinforcement learning. Psychological Science, 27, 848858.
Deserno, L., Huys, Q. J. M., Boehme, R., Buchert, R., Heinze, H.-J., Grace, A. A., … Schlagenhauf, F. (2015). Ventral striatal dopamine reflects behavioral and neural signatures of model-based control during sequential decision making. Proceedings of the National Academy of Sciences of the United States of America, 112, 15951600.
Diaconescu, A. O., Mathys, C., Weber, L. A. E., Daunizeau, J., Kasper, L., Lomakina, E. I., … Stephan, K. E. (2014). Inferring on the intentions of others by hierarchical Bayesian learning. PLoS Computational Biology, 10, e1003810.
Dickinson, A. (1985). Actions and habits: The development of behavioural autonomy. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 308, 6778.
Doll, B. B., Bath, K. G., Daw, N. D.Frank, M. J. (2016). Variability in dopamine genes dissociates model-based and model-free reinforcement learning. Journal of Neuroscience, 36, 12111222.
Doll, B. B., Duncan, K. D., Simon, D. A., Shohamy, D.Daw, N. D. (2015). Model-based choices involve prospective neural activity. Nature Neuroscience, 18, 767772.
Eppinger, B., Walter, M., Heekeren, H. R.Li, S.-C. (2013). Of goals and habits: Age-related and individual differences in goal-directed decision-making. Frontiers in Neuroscience, 7, 253.
Everitt, B. J.Robbins, T. W. (2005). Neural systems of reinforcement for drug addiction: From actions to habits to compulsion. Nature Neuroscience, 8, 14811489.
Fernandes, B. S., Williams, L. M., Steiner, J., Leboyer, M., Carvalho, A. F.Berk, M. (2017). The new field of “precision psychiatry”. BMC Medicine, 15, 80.
Forstmann, B. U.Wagenmakers, E. J. (2015). An introduction to model-based cognitive neuroscience. New York, NY: Springer.
Frank, M. J. (2005). Dynamic dopamine modulation in the basal ganglia: A neurocomputational account of cognitive deficits in medicated and nonmedicated Parkinsonism. Journal of Cognitive Neuroscience, 17, 5172.
Frank, M. J., Moustafa, A. A., Haughey, H. M., Curran, T.Hutchison, K. E. (2007). Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning. Proceedings of the National Academy of Sciences of the United States of America, 104, 1631116316.
Friston, K. J., Redish, A. D.Gordon, J. A. (2017). Computational nosology and precision psychiatry. Computational Psychiatry, 1, 223.
Friston, K. J., Stephan, K. E., Montague, P. R.Dolan, R. J. (2014). Computational psychiatry: The brain as a phantastic organ. The Lancet Psychiatry, 1, 148–158.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A.Rubin, D. B. (2013). Bayesian data analysis (3rd ed.), Boca Raton, FL: CRC Press.
Gershman, S. J. (2016). Empirical priors for reinforcement learning models. Journal of Mathematical Psychology, 71, 16.
Gershman, S. J.Hartley, C. A. (2015). Individual differences in learning predict the return of fear. Learning & Behavior, 43, 243250.
Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., … Rapoport, J. L. (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuroscience, 2, 861863.
Gillan, C. M., Kosinski, M., Whelan, R., Phelps, E. A.Daw, N. D. (2016). Characterizing a psychiatric symptom dimension related to deficits in goal-directed control. eLife, 5, e11305.
Glimcher, P. W. (2011). Understanding dopamine and reinforcement learning: The dopamine reward prediction error hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 108(Suppl. 3), 1564715654.
Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., … Thompson, P. M. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences of the United States of America, 101, 81748179.
Gold, J. (2012). Cognitive neuroscience test reliability and clinical applications for schizophrenia. Schizophrenia Bulletin, 38, 103.
Gold, J. M., Barch, D. M., Carter, C. S., Dakin, S., Luck, S. J., MacDonald, A. W. III, … Strauss, M. (2012). Clinical, functional, and intertask correlations of measures developed by the cognitive neuroscience test reliability and clinical applications for schizophrenia consortium. Schizophrenia Bulletin, 38, 144152.
Hartley, C. A.Somerville, L. H. (2015). The neuroscience of adolescent decision-making. Current Opinion in Behavioral Sciences, 5, 108115.
Hunt, E. (2011). Human intelligence. New York, NY: Cambridge University Press.
Huys, Q. J. M., Beck, A., Dayan, P.Heinz, A. (2014). Neurobiology and computational structure of decision-making in addiction. In A. L. Mishara, P. R. Corlett, P. C. Fletcher, A. Kranjec, & M. A. Schwartz (Eds.), Phenomenological neuropsychiatry: Bridging the clinic and clinical neuroscience. New York, NY: Springer.
Huys, Q. J. M., Maia, T. VFrank, M. J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19, 404413.
Huys, Q. J. M., Moutoussis, M.Williams, J. (2011). Are computational models of any use to psychiatry? Neural Networks, 24, 544551.
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., … Wang, P. (2010). Research domain criteria (RDoC): Toward a new classification framework for research on mental disorders. The American Journal of Psychiatry, 167, 748751.
Jackson, J. J., Wood, D., Bogg, T., Walton, K. E., Harms, P. D.Roberts, B. W. (2010). What do conscientious people do? Development and validation of the Behavioral Indicators of Conscientiousness (BIC). Journal of Research in Personality, 44, 501511.
Kool, W., Gershman, S. J.Cushman, F. A. (2017). Cost-benefit arbitration between multiple reinforcement learning systems. Psychological Science, 28, 13211333.
Kurth-Nelson, Z.Redish, A. D. (2012). Modeling decision-making systems in addiction. In B. S. Gutkin, & S. Ahmed (Eds.), Computational neuroscience of drug addiction (pp. 163–188). New York, NY: Springer.
Lubinski, D. (2004). Introduction to the special section on cognitive abilities: 100 years after Spearman’s (1904) “‘General intelligence,’ objectively determined and measured”. Journal of Personality and Social Psychology, 86, 96111.
Maia, T. V.Frank, M. J. (2011). From reinforcement learning models to psychiatric and neurological disorders. Nature Neuroscience, 14, 154162.
Maia, T. V.Frank, M. J. (2017). An integrative perspective on the role of dopamine in schizophrenia. Biological Psychiatry, 81, 5266.
Montague, P. R., Dolan, R. J., Friston, K. J.Dayan, P. (2012). Computational psychiatry. Trends in Cognitive Sciences, 16, 7280.
Nebe, S., Kroemer, N. B., Schad, D. J., Bernhardt, N., Sebold, M., Müller, D. K., … Smolka, M. N. (2018). No association of goal-directed and habitual control with alcohol consumption in young adults. Addiction Biology, 23, 379393.
Nilsson, H., Rieskamp, J.Wagenmakers, E.-J. (2011). Hierarchical Bayesian parameter estimation for cumulative prospect theory. Journal of Mathematical Psychology, 55, 8493.
O’Doherty, J., Dayan, P., Schultz, J., Deichmann, R., Friston, K.Dolan, R. J. (2004). Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science, 304, 452454.
Otto, A. R., Raio, C. M., Chiang, A., Phelps, E. A.Daw, N. D. (2013). Working-memory capacity protects model-based learning from stress. Proceedings of the National Academy of Sciences of the United States of America, 110, 2094120946.
Palminteri, S., Kilford, E. J., Coricelli, G.Blakemore, S.-J. (2016). The computational development of reinforcement learning during adolescence. PLoS Computational Biology, 12, e1004953.
Paulus, M. P., Huys, Q. J. M.Maia, T. V. (2016). A roadmap for the development of applied computational psychiatry. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1, 386392.
Paunonen, S. V. (2003). Big Five factors of personality and replicated predictions of behavior. Journal of Personality and Social Psychology, 84, 411424.
Pessiglione, M., Seymour, B., Flandin, G., Dolan, R. J.Frith, C. D. (2006). Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans. Nature, 442, 10421045.
Petzschner, F. H., Weber, L. A. E., Gard, T.Stephan, K. E. (2017). Computational psychosomatics and computational psychiatry: Toward a joint framework for differential diagnosis. Biological Psychiatry, 82, 421430.
Potter, T. C. S., Bryce, N. VHartley, C. A. (2017). Cognitive components underpinning the development of model-based learning. Developmental Cognitive Neuroscience, 25, 272280.
Ree, M. J., Earles, J. A.Teachout, M. S. (1994). Predicting job performance: Not much more than g. The Journal of Applied Psychology, 79, 518524.
Rescorla, R. A.Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black, & W. F. Prokasy (Eds.), Classical conditioning II: Current research and theory (pp. 6499). New York, NY: Appleton-Century-Crofts.
Rigoux, L.Daunizeau, J. (2015). Dynamic causal modelling of brain-behaviour relationships. NeuroImage, 117, 202221.
Robinson, E. B., Koenen, K. C., McCormick, M. C., Munir, K., Hallett, V., Happé, F., … Ronald, A. (2011). Evidence that autistic traits show the same etiology in the general population and at the quantitative extremes (5%, 2.5%, and 1%). Archives of General Psychiatry, 68, 11131121.
Schlagenhauf, F., Rapp, M. A., Huys, Q. J. M., Beck, A., Wüstenberg, T., Deserno, L., … Heinz, A. (2013). Ventral striatal prediction error signaling is associated with dopamine synthesis capacity and fluid intelligence. Human Brain Mapping, 34, 14901499.
Schmidt, F. L.Hunter, J. (2004). General mental ability in the world of work: Occupational attainment and job performance. Journal of Personality and Social Psychology, 86, 162173.
Schönberg, T., Daw, N. D., Joel, D.O’Doherty, J. P. (2007). Reinforcement learning signals in the human striatum distinguish learners from nonlearners during reward-based decision making. The Journal of Neuroscience, 27, 1286012867.
Schultz, W., Dayan, P.Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275, 15931599.
Schwartenbeck, P.Friston, K. J. (2016). Computational phenotyping in psychiatry: A worked example. ENeuro, 3(4), ENEURO.0049-16.2016.
Sebold, M., Deserno, L., Nebe, S., Schad, D. J., Garbusow, M., Hägele, C., … Huys, Q. J. M. (2014). Model-based and model-free decisions in alcohol dependence. Neuropsychobiology, 70, 122131.
Sebold, M., Nebe, S., Garbusow, M., Guggenmos, M., Schad, D. J., Beck, A., … Heinz, A. (2017). When habits are dangerous: Alcohol expectancies and habitual decision making predict relapse in alcohol dependence. Biological Psychiatry, 82, 847856.
Sevgi, M., Diaconescu, A. O., Tittgemeyer, M.Schilbach, L. (2016). Social Bayes: Using Bayesian modeling to study autistic trait–related differences in social cognition. Biological Psychiatry, 80, 112119.
Sharp, M. E., Foerde, K., Daw, N. D.Shohamy, D. (2015). Dopamine selectively remediates “model-based” reward learning: A computational approach. Brain, 139, 355364.
Smittenaar, P., FitzGerald, T. H. B., Romei, V., Wright, N. D.Dolan, R. J. (2013). Disruption of dorsolateral prefrontal cortex decreases model-based in favor of model-free control in humans. Neuron, 80, 914919.
Spearman, C. (1904). “General Intelligence,” objectively determined and measured. The American Journal of Psychology, 15, 201292.
Stephan, K. E., Iglesias, S., Heinzle, J.Diaconescu, A. O. (2015). Translational perspectives for computational neuroimaging. Neuron, 87, 716732.
Stephan, K. E.Mathys, C. (2014). Computational approaches to psychiatry. Current Opinion in Neurobiology, 25, 8592.
Stephan, K. E., Schlagenhauf, F., Huys, Q. J. M., Raman, S., Aponte, E. A., Brodersen, K. H., … Heinz, A. (2017). Computational neuroimaging strategies for single patient predictions. Neuroimage, 145(Pt B), 180199.
Sutton, R. S.Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
Thorndike, E. L. (1911). Animal intelligence: Experimental studies. Abingdon: Routledge.
Tolman, E. C. (1948). Cognitive maps in rats and men. Psychological Review, 55, 189208.
Tupes, E. C.Christal, R. E. (1992). Recurrent personality factors based on trait ratings. Journal of Personality, 60, 225251.
Turner, B. M., Forstmann, B. U., Wagenmakers, E.-J., Brown, S. D., Sederberg, P. B.Steyvers, M. (2013). A Bayesian framework for simultaneously modeling neural and behavioral data. NeuroImage, 72, 193206.
Turner, B. M., Rodriguez, C. A., Norcia, T. M., McClure, S. M.Steyvers, M. (2016). Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data. NeuroImage, 128, 96115.
Turner, B. M., van Maanen, L.Forstmann, B. U. (2015). Informing cognitive abstractions through neuroimaging: The neural drift diffusion model. Psychological Review, 122, 312336.
Turner, B. M., Wang, T.Merkle, E. C. (2017). Factor analysis linking functions for simultaneously modeling neural and behavioral data. Neuroimage, 153, 2848.
van den Bos, W., Cohen, M. X., Kahnt, T.Crone, E. A. (2012). Striatum-medial prefrontal cortex connectivity predicts developmental changes in reinforcement learning. Cerebral Cortex, 22, 12471255.
van Leeuwen, T. M., den Ouden, H. E. M.Hagoort, P. (2011). Effective connectivity determines the nature of subjective experience in grapheme-color synesthesia. Journal of Neuroscience, 31, 98799884.
Vandekerckhove, J. (2014). A cognitive latent variable model for the simultaneous analysis of behavioral and personality data. Journal of Mathematical Psychology, 60, 5871.
Voon, V., Derbyshire, K., Rück, C., Irvine, M. A., Worbe, Y., Enander, J., … Bullmore, E. T. (2015). Disorders of compulsivity: A common bias towards learning habits. Molecular Psychiatry, 20, 345352.
Voon, V., Reiter, A., Sebold, M.Groman, S. (2017). Model-based control in dimensional psychiatry. Biological Psychiatry, 82, 391400.
Wang, X.-J.Krystal, J. H. (2014). Computational psychiatry. Neuron, 84, 638654.
Wiecki, T. V., Poland, J.Frank, M. J. (2015). Model-based cognitive neuroscience approaches to computational psychiatry: Clustering and classification. Clinical Psychological Science, 3, 378399.
Wiecki, T. V., Sofer, I.Frank, M. J. (2013). HDDM: Hierarchical Bayesian estimation of the drift-diffusion model in Python. Frontiers in Neuroinformatics, 7, 14.
Wunderlich, K., Smittenaar, P.Dolan, R. J. (2012). Dopamine enhances model-based over model-free choice behavior. Neuron, 75, 418424.
MathJax is a JavaScript display engine for mathematics. For more information see


Computational Phenotyping: Using Models to Understand Individual Differences in Personality, Development, and Mental Illness

  • Edward H. Patzelt (a1), Catherine A. Hartley (a2) and Samuel J. Gershman (a1)


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed