Reinforcement learning as an intermediate phenotype in psychosis? Deficits sensitive to illness stage but not associated with polygenic risk of schizophrenia in the general population
Open accessPublished version of a preprint
Abstract
BACKGROUND: Schizophrenia is a complex disorder in which the causal relations between risk genes and observed clinical symptoms are not well understood and the explanatory gap is too wide to be clarified without considering an intermediary level. Thus, we aimed to test the hypothesis of a pathway from molecular polygenic influence to clinical presentation occurring via deficits in reinforcement learning. METHODS: We administered a reinforcement learning task (Go/NoGo) that measures reinforcement learning and the effect of Pavlovian bias on decision making. We modelled the behavioural data with a hierarchical Bayesian approach (hBayesDM) to decompose task performance into its underlying learning mechanisms. Study 1 included controls (n = 29, F|M = 0.81), At Risk Mental State for psychosis (ARMS, n = 23, F|M = 0.35) and FEP (First-episode psychosis, n = 26, F|M = 0.18). Study 2 included healthy adolescents (n = 735, F|M = 1.06), 390 of whom had their polygenic risk scores for schizophrenia (PRSs) calculated. RESULTS: Patients with FEP showed significant impairments in overriding Pavlovian conflict, a lower learning rate and a lower sensitivity to both reward and punishment. Less widespread deficits were observed in ARMS. PRSs did not significantly predict performance on the task in the general population, which only partially correlated with measures of psychopathology. CONCLUSIONS: Reinforcement learning deficits are observed in first episode psychosis and, to some extent, in those at clinical risk for psychosis, and were not predicted by molecular genetic risk for schizophrenia in healthy individuals. The study does not support the role of reinforcement learning as an intermediate phenotype in psychosis.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Insensitivity to reward has been shown across all stages of schizophrenia and has been associated with negative symptoms, especially anhedonia, and cognitive deficits.
“Insensitivity to reward has been shown across all stages of schizophrenia and has been associated with negative symptoms, especially anhedonia, and cognitive deficits.55,61,119,120”
From Investigating disorder-specific and transdiagnostic alterations in model-based and model-free decision-making (2024) · cites this paper as [55]
Previous work indicated clear differences in the parameters when using group-specific versus group-general priors.
“Previous work indicated clear differences in the parameters when using group-specific versus group-general priors.55,130”
From Investigating disorder-specific and transdiagnostic alterations in model-based and model-free decision-making (2024) · cites this paper as [55]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
The Positive and Negative Syndrome Scale (PANSS) for schizophrenia.
Kay, S. R., Fiszbein, A., & Opler, L. A. (1987) · Schizophrenia Bulletin · doi:10.1093/schbul/13.2.261 · Cited by 21,378 · see citing works
Biological insights from 108 schizophrenia-associated genetic loci
Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) · Nature · doi:10.1038/nature13595 · Cited by 7,577 · see citing works
Self-citationCommon polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821 · see citing works
Self-citationMapping the Onset of Psychosis: The Comprehensive Assessment of At-Risk Mental States
Yung, A. R., Yung, A. R., Yuen, H. P., McGorry, P. D., Phillips, L., Kelly, D., Dell’Olio, M., Francey, S. M., Cosgrave, E., Killackey, E. J., et al. (2005) · Australian & New Zealand Journal of Psychiatry · doi:10.1080/j.1440-1614.2005.01714.x · Cited by 2,195 · see citing works
The SPQ: A Scale for the Assessment of Schizotypal Personality Based on DSM-III-R Criteria
Raine, A. (1991) · Schizophrenia Bulletin · doi:10.1093/schbul/17.4.555 · Cited by 2,164 · see citing works
Bayesian Cognitive Modeling: A Practical Course
Lee, M. D., & Wagenmakers, E.-J. (2014) · doi:10.1017/cbo9781139087759 · Cited by 1,795 · see citing works
A scale for the assessment of hedonic tone the Snaith-Hamilton pleasure scale
Snaith, R. P., Hamilton, M., Morley, S., Humayan, A., Hargreaves, D., & Trigwell, P. (1995) · British Journal of Psychiatry · doi:10.1192/bjp.167.1.99 · Cited by 1,778 · see citing works
Bayesian inference for psychology. Part II: Example applications with JASP
Wagenmakers, E., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Selker, R., Gronau, Q. F., Dropmann, D., boutin, B., et al. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1323-7 · Cited by 1,705 · see citing works
GERAD1 Consortium, Pardiñas, A. F., CRESTAR Consortium, Holmans, P., Pocklington, A. J., Escott-Price, V., Ripke, S., Carrera, N., Legge, S. E., Bishop, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0059-2 · Cited by 1,552 · see citing works
Self-citationThe Prodromal Phase of First-episode Psychosis: Past and Current Conceptualizations
Yung, A. R., & McGorry, P. D. (1996) · Schizophrenia Bulletin · doi:10.1093/schbul/22.2.353 · Cited by 1,168 · see citing works
Scales to Assess Child and Adolescent Depression: Checklists, Screens, and Nets
Costello, E. J., & Angold, A. C. (1988) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1097/00004583-198811000-00011 · Cited by 730 · see citing works
Measuring Delusional Ideation: The 21-Item Peters et al. Delusions Inventory (PDI)
Peters, E., Joseph, S., Day, S., & Garety, P. (2004) · Schizophrenia Bulletin · doi:10.1093/oxfordjournals.schbul.a007116 · Cited by 607 · see citing works
Schizophrenia: genes and environment
Tsuang, M. T. (2000) · Biological Psychiatry · doi:10.1016/s0006-3223(99)00289-9 · Cited by 598 · see citing works
Substantia nigra/ventral tegmental reward prediction error disruption in psychosis
Murray, G. K., Corlett, P. R., Clark, L., Pessiglione, M., Blackwell, A. D., Honey, G., Jones, P. B., Bullmore, E. T., Robbins, T. W., & Fletcher, P. C. (2008) · Molecular Psychiatry · doi:10.1038/sj.mp.4002058 · Cited by 532 · see citing works
Reward Processing in Schizophrenia: A Deficit in the Representation of Value
Gold, J. M., Waltz, J. A., Prentice, K. J., Morris, S. E., & Heerey, E. A. (2008) · Schizophrenia Bulletin · doi:10.1093/schbul/sbn068 · Cited by 521 · see citing works
Whitaker, K. J., Vértes, P. E., Romero-García, R., Váša, F., Moutoussis, M., Prabhu, G., Weiskopf, N., Callaghan, M. F., Wagstyl, K., Rittman, T., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1601745113 · Cited by 506 · see citing works
Self-citationGo and no-go learning in reward and punishment: Interactions between affect and effect
Guitart‐Masip, M., Huys, Q. J. M., Fuentemilla, L., Dayan, P., Düzel, E., & Dolan, R. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.04.024 · Cited by 418 · see citing works
Self-citationAhn, W., Haines, N., & Zhang, L. (2017) · Computational Psychiatry · doi:10.1162/cpsy_a_00002 · Cited by 354 · see citing works
Decision making in the ageing brain: changes in affective and motivational circuits
Samanez-Larkin, G. R., & Knutson, B. D. (2015) · Nature reviews. Neuroscience · doi:10.1038/nrn3917 · Cited by 333 · see citing works
Morrison, A. P., French, P., Stewart, S. L. K., Birchwood, M., Fowler, D., Gumley, A. I., Jones, P. B., Bentall, R. P., Lewis, S. W., Murray, G. K., et al. (2012) · BMJ · doi:10.1136/bmj.e2233 · Cited by 332 · see citing works
Self-citationNegative Symptoms and the Failure to Represent the Expected Reward Value of Actions
Gold, J. M. (2012) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2011.1269 · Cited by 319 · see citing works
Jones, H. J., Stergiakouli, E., Tansey, K. E., Hubbard, L., Heron, J., Cannon, M. D., Holmans, P., Lewis, G., Linden, D. E. J., Jones, P. B., et al. (2016) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.3058 · Cited by 234 · see citing works
Self-citationWorking Memory Contributions to Reinforcement Learning Impairments in Schizophrenia
Collins, A. G. E., Brown, J. K., Gold, J. M., Waltz, J. A., & Frank, M. J. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0989-14.2014 · Cited by 214 · see citing works
Striatal Response to Reward Anticipation
Grimm, O., Heinz, A., Walter, H., Kirsch, P., Erk, S., Haddad, L., Plichta, M. M., Romanczuk‐Seiferth, N., Pöhland, L., Mohnke, S., et al. (2014) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.9 · Cited by 108 · see citing works
Polygenic risk score increases schizophrenia liability through cognition-relevant pathways
Toulopoulou, T., Zhang, X., Cherny, S. S., Dickinson, D., Berman, K. F., Straub, R. E., Sham, P. C., & Weinberger, D. R. (2018) · Brain · doi:10.1093/brain/awy279 · Cited by 98 · see citing works
Ermakova, A. O., Knolle, F., Justicia, A., Bullmore, E. T., Jones, P. B., Robbins, T. W., Fletcher, P. C., & Murray, G. K. (2018) · Neuropsychopharmacology · doi:10.1038/s41386-018-0056-2 · Cited by 91 · see citing works
Self-citationDeserno, L., Boehme, R., Heinz, A., & Schlagenhauf, F. (2013) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2013.00172 · Cited by 89 · see citing works
Kiddle, B., Inkster, B., Prabhu, G., Moutoussis, M., Whitaker, K. J., Bullmore, E. T., Dolan, R. J., Fonagy, P., Goodyer, I. M., & Jones, P. B. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyx117 · Cited by 87 · see citing works
Self-citationMapping the onset of psychosis: the comprehensive assessment of at risk mental states (CAARMS)
Yung, A. R., Yuen, H. P., Phillips, L. J., Francey, S., & McGorry, P. D. (2003) · Schizophrenia Research · doi:10.1016/s0920-9964(03)80090-7 · Cited by 84 · see citing works
Moutoussis, M., Bullmore, E. T., Goodyer, I. M., Fonagy, P., Jones, P. B., Dolan, R. J., & Dayan, P. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006679 · Cited by 79 · see citing works
Self-citationO’Callaghan, C., Hall, J. M., Tomassini, A., Muller, A. J., Walpola, I. C., Moustafa, A. A., Shine, J. M., & Lewis, S. J. G. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.04.007 · Cited by 77 · see citing works
Zammit, S., Horwood, J., Thompson, A., Thomas, K., Menezes, P., Gunnell, D., Hollis, C., Wolke, D., Lewis, G., & Harrison, G. (2008) · Schizophrenia Research · doi:10.1016/j.schres.2008.04.036 · Cited by 76 · see citing works
(2016) · The MIT Press eBooks · doi:10.7551/mitpress/9780262035422.001.0001 · Cited by 72 · see citing works
Ahn, W., Haines, N., & Zhang, L. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/064287 · Cited by 67 · see citing works
The promises and pitfalls of applying computational models to neurological and psychiatric disorders
Teufel, C., & Fletcher, P. C. (2016) · Brain · doi:10.1093/brain/aww209 · Cited by 64 · see citing works
Self-citationApproach-Induced Biases in Human Information Sampling
Hunt, L. T., Rutledge, R. B., Malalasekera, W. M. N., Kennerley, S. W., & Dolan, R. J. (2016) · PLOS Biology · doi:10.1371/journal.pbio.2000638 · Cited by 60 · see citing works
Self-citationWallace, R. (2017) · Springer eBooks · doi:10.1007/978-3-319-53910-2 · Cited by 52 · see citing works
Lancaster, T. M., Linden, D. E., Tansey, K. E., Banaschewski, T., Bokde, A. L. W., Bromberg, U., Büchel, C., Cattrell, A., Conrod, P. J., Flor, H., et al. (2016) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2016.1135 · Cited by 49 · see citing works
Murray, G. K., Corlett, P. R., & Fletcher, P. C. (2010) · Schizophrenia Bulletin · doi:10.1093/schbul/sbq005 · Cited by 46 · see citing works
Self-citationSchizophrenia: A Computational Reinforcement Learning Perspective
Frank, M. J. (2008) · Schizophrenia Bulletin · doi:10.1093/schbul/sbn123 · Cited by 37 · see citing works
The impact of traumatic stress on Pavlovian biases
Ousdal, O. T., Huys, Q. J. M., Milde, A. M., Craven, A. R., Ersland, L., Endestad, T., Melinder, A. M. D., Hugdahl, K., & Dolan, R. J. (2017) · Psychological Medicine · doi:10.1017/s003329171700174x · Cited by 36 · see citing works
Self-citationLancaster, T. M., Dimitriadis, S. I., Tansey, K. E., Perry, G., Ihssen, N., Jones, D. K., Singh, K. D., Holmans, P., Pocklington, A. J., Smith, G. D., et al. (2018) · Schizophrenia Bulletin · doi:10.1093/schbul/sby037 · Cited by 36 · see citing works
Self-citationScherer, H., Bédard, M., Stip, É., Paquet, F., Richer, F., Bériault, M., Rodriguez, J.-P., & Mottard, J. (2004) · Cognitive and Behavioral Neurology · doi:10.1097/00146965-200403000-00004 · Cited by 35 · see citing works
Mild Reinforcement Learning Deficits in Patients With First-Episode Psychosis
Chang, W. C., Waltz, J. A., Gold, J. M., Chan, T. C. W., & Chen, E. Y. H. (2016) · Schizophrenia Bulletin · doi:10.1093/schbul/sbw060 · Cited by 35 · see citing works
The effects of aging on the interaction between reinforcement learning and attention.
Radulescu, A., Daniel, R., & Niv, Y. (2016) · Psychology and Aging · doi:10.1037/pag0000112 · Cited by 35 · see citing works
Reduction of Pavlovian Bias in Schizophrenia: Enhanced Effects in Clozapine-Administered Patients
Albrecht, M. A., Waltz, J. A., Cavanagh, J. F., Frank, M. J., & Gold, J. M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152781 · Cited by 31 · see citing works
Decision Neuroscience : An Integrative Perspective
Dreher, J. (2016) · Cited by 28 · see citing works
Kasanova, Z., Ceccarini, J., Frank, M. J., van Amelsvoort, T., Booij, J., van Duin, E. D. A., Steinhart, H., Vaessen, T., Heinzel, A., Mottaghy, F. M., et al. (2017) · Psychological Medicine · doi:10.1017/s0033291717003476 · Cited by 17 · see citing works
Intelligence moderates reinforcement learning: a mini-review of the neural evidence
Chen, C. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00600.2014 · Cited by 12 · see citing works
Can Models of Reinforcement Learning Help Us to Understand Symptoms of Schizophrenia?
Murray, G. K., Tudor-Sfetea, C., & Fletcher, P. C. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-805308-9.00021-x · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- conceptualization, methodology, supervision, writing (review and editing); NSPN Consor- tium: conceptualization, methodology, project administration. GKM, conceptualization, project administration, methodology, supervision, writing (original draft preparation, re- view and editing). References
- Davies, D., 2017. Psychotic Experiences beyond Psychotic Disorders in Young People: From Measurement to Computational Mechanisms. PhD. University of Cambridge.
- Peters et al. Delusions Inventory (PDI). Schizophr. Bull. 30 (4), 1005-1022.
Cited by
Sorted by citations
Millard, S. J., Bearden, C. E., Karlsgodt, K. H., & Sharpe, M. J. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01188-y · Cited by 79 · see citing works
Neural Circuitry of Salience and Reward Processing in Psychosis
Kesby, J. P., Murray, G. K., & Knolle, F. (2023) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2021.12.003 · Cited by 55 · see citing works
Self-citationGibbs-Dean, T., Katthagen, T., Tsenkova, I., Ali, R., Liang, X., Spencer, T. J., & Diederen, K. M. J. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105087 · Cited by 46 · see citing works
Negative symptoms, striatal dopamine and model-free reward decision-making in schizophrenia
Brandl, F., Knolle, F., Avram, M., Leucht, C., Yakushev, I., Priller, J., Leucht, S., Ziegler, S., Wunderlich, K., & Sorg, C. (2023) · Brain · doi:10.1093/brain/awac268 · Cited by 24 · see citing works
Self-citationAction selection in early stages of psychosis: an active inference approach
Knolle, F., Sterner, E., Moutoussis, M., Adams, R. A., Griffin, J. D., Haarsma, J., Taverne, H., Goodyer, I. M., Fletcher, P. C., & Murray, G. K. (2023) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.220141 · Cited by 16 · see citing works
Self-citationThe impact of social anxiety on feedback-based go and nogo learning
Peterburs, J., Albrecht, C., & Bellebaum, C. (2021) · Psychological Research · doi:10.1007/s00426-021-01479-5 · Cited by 14 · see citing works
Improving the reliability of the Pavlovian go/no-go task for computational psychiatry researchPreprint
Zorowitz, S., Karni, G., Paredes, N., Daw, N. D., & Niv, Y. (2023) · doi:10.31234/osf.io/eb697 · Cited by 14 · see citing works
Demler, V. F., Sterner, E., Wilson, M., Zimmer, C., & Knolle, F. (2023) · Scientific Reports · doi:10.1038/s41598-023-39881-1 · Cited by 9 · see citing works
Self-citationPredictive processing accounts of psychosis: bottom-up or top-down disruptions
Goodwin, I., Diederen, K. M. J., Hird, E., Weilnhammer, V. A., Garrido, M. I., & Knolle, F. (2026) · Nature Mental Health · doi:10.1038/s44220-025-00558-5 · Cited by 9 · see citing works
Self-citationFrydecka, D., Misiak, B., Piotrowski, P., Bielawski, T., Pawlak, E., Kłosińska, E., Krefft, M., Noaimy, K. A., Rymaszewska, J., Moustafa, A. A., et al. (2021) · Brain Sciences · doi:10.3390/brainsci12010007 · Cited by 8 · see citing works
Knolle, F., Sen, P., Culbreth, A., Koch, K., Schmitz-Koep, B., Gürsel, D. A., Wunderlich, K., Avram, M., Berberich, G., Sorg, C., et al. (2024) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.240020 · Cited by 9 · see citing works
Self-citationPavlovian impatience: The anticipation of immediate rewards increases approach behaviour
Burghoorn, F., Scheres, A., Monterosso, J. R., Guo, M., LUO, S. H., Karin Roelofs, A., & Figner, B. (2024) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-024-01236-2 · Cited by 11 · see citing works
Improving the Reliability of the Pavlovian Go/No-Go Task for Computational Psychiatry Research
Zorowitz, S., Karni, G., Paredes, N., Daw, N. D., & Niv, Y. (2025) · Computational Psychiatry · doi:10.5334/cpsy.127 · Cited by 5 · see citing works
Griffin, J. D., Diederen, K. M. J., Haarsma, J., Barnham, I. J., Cook, B., Fernández-Egea, E., Williamson, S., van Sprang, E. D., Raphaël Gaillard, B., Vinckier, F., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-68004-7 · Cited by 4 · see citing works
Self-citationBaker, A., Suetani, S., Cosgrove, P., Siskind, D. J., Murray, G. K., Scott, J. G., & Kesby, J. P. (2023) · Cognitive Neuropsychiatry · doi:10.1080/13546805.2023.2259019 · Cited by 4 · see citing works
Self-citationSterner, E., Greve, A., & Knolle, F. (2025) · Schizophrenia · doi:10.1038/s41537-025-00643-9 · Cited by 4 · see citing works
Self-citationJin, H., Nath, S. S., Schneider, S., Junghaenel, D. U., Wu, S., & Kaplan, C. D. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103913 · Cited by 4 · see citing works
Bracher, K. M., Wohlschlaeger, A. M., Koch, K., & Knolle, F. (2024) · Scientific Reports · doi:10.1038/s41598-024-71316-3 · Cited by 3 · see citing works
Self-citationConn, K., Das, J., Alexander, S., Burne, T. H. J., & Kesby, J. P. (2025) · Neuropharmacology · doi:10.1016/j.neuropharm.2025.110568 · Cited by 3 · see citing works
The Role of Pharmacogenomics in Precision Psychiatry
Manchia, M., & Murri, M. B. (2023) · Pharmacogenomics · doi:10.2217/pgs-2023-0112 · Cited by 3 · see citing works
Intertemporal Pavlovian biases and links with mental health in the general population
Burghoorn, F., Scheres, A., Roelofs, K., & Figner, B. (2025) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-025-01326-9 · Cited by 3 · see citing works
Clinical psychopathology-based early relapse prediction model using speech and language in psychosis
Dalal, T. C., Park, M. T. M., Silva, A. M., Iskhakova, S., Voppel, A., Brierley, N. J., Mackinley, M., Olarewaju, E., & Palaniyappan, L. K. (2025) · Schizophrenia Research Cognition · doi:10.1016/j.scog.2025.100392 · Cited by 2 · see citing works
Chu, R. S. T., Waltz, J. A., Gold, J. M., Wo, S. F., Wong, C. F., Hui, C. L. M., Chan, S. K. W., Lee, E. H. M., Lui, S. S. Y., Chen, E. Y. H., et al. (2026) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2026.112824 · Cited by 1 · see citing works
Charaf, K., Agoub, M., & Boussaoud, D. (2023) · Schizophrenia Research Cognition · doi:10.1016/j.scog.2023.100295 · Cited by 1 · see citing works
Sen, P., & Knolle, F. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103756 · Cited by 1 · see citing works
Self-citationDemler, V. F., Sterner, E., Wilson, M., Zimmer, C., & Knolle, F. (2023) · medRxiv · doi:10.1101/2023.02.01.23285183 · Cited by 1 · see citing works
Self-citation
- Authors
- Montagnese, M., Knolle, F., Haarsma, J., Griffin, J. D., Richards, A., Vertes, P. E., Kiddle, B., Fletcher, P. C., Jones, P. B., Owen, M. J., Fonagy, P., Bullmore, E. T., Dolan, R. J., Moutoussis, M., Goodyer, I. M., & Murray, G. K.
- Affiliations
- University of Cambridge (GB)
- National Health Service (GB)
- Cardiff University (GB)
- University College London (GB)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Murray, Graham K.
- ORCID
- Venue
- Schizophrenia Research
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 222
- Pages
- 389-396
- ISSN
- 0920-9964 (ISSN-L)
- PMC
- PMC7594641
- OpenAlex
- W3018331763
- Cited by
- 26
- References
- 50
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 4 · 2023: 8 · 2022: 1 · 2021: 5
- FWCI
- 2.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Schizophrenia Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Funders
- Wellcome Trust
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- reinforcement learning, endophenotypes
- MeSH terms
- Adolescent, Bayes Theorem, Humans, Phenotype, Psychotic Disorders, Schizophrenia, Multifactorial Inheritance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review