Under the hood: Using computational psychiatry to make psychological therapies more mechanism-focused
Open accessAbstract
Psychological therapies, such as CBT, are an important part of the treatment of a range of psychiatric disorders such as depression and anxiety. There is a growing desire to understand the mechanisms by which such therapies effect change so as to improve treatment outcomes. Here we argue that adopting a computational framework may be one such approach. Computational psychiatry aims to provide a theoretical framework for moving between higher-level psychological states (like emotions, decisions and beliefs) to neural circuits, by modeling these constructs mathematically. These models are explicit hypotheses that contain quantifiable variables and parameters derived from each individual's behavior. This approach has two advantages. Firstly, some of the variables described by these models appears to reflect the neural activity of specific brain regions. Secondly, the parameters estimated by these models may offer a unique description of a patient's symptoms which can be used to both tailor therapy and track its effect. In doing so this approach may offer some additional granularity in understanding how psychological therapies, such as CBT, are working. Although this field shows significant promise, we also highlight several of the key hurdles that must first be overcome before clinical translation of computational insights can be realized.
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?
Exposure therapy can be seen as attempts to recalibrate prediction weights by repeated safe exposures, encouraging the brain to assign greater weight to the 'I am safe' prediction relative to the 'something bad will happen' prediction error.
“Treatments like exposure therapy can be seen as attempts to recalibrate these prediction weights–by repeated safe exposures, the patient’s brain is encouraged to assign greater weight to the “I am safe” prediction relative to the “something bad will happen” prediction error (66).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [66]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
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
Rush, A. J., Trivedi, M. H., Wisniewski, S. R., Nierenberg, A. A., Stewart, J. W., Warden, D., Niederehe, G., Thase, M. E., Lavori, P. W., Lebowitz, B. D., et al. (2006) · American Journal of Psychiatry · doi:10.1176/ajp.2006.163.11.1905 · Cited by 5,643 · see citing works
Resting-state connectivity biomarkers define neurophysiological subtypes of depression
Drysdale, A. T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R. N., Zebley, B., Oathes, D. J., Etkin, A., et al. (2017) · Nature Medicine · doi:10.1038/nm.4246 · Cited by 2,170 · see citing works
Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans
Pessiglione, M., Seymour, B., Flandin, G., Dolan, R. J., & Frith, C. D. (2006) · Nature · doi:10.1038/nature05051 · Cited by 1,584 · see citing works
Reconsidering anhedonia in depression: Lessons from translational neuroscience
Treadway, M. T., & Zald, D. H. (2011) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2010.06.006 · Cited by 1,579 · see citing works
Active inference: a process theory
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2017) · Neural Computation · doi:10.1162/neco_a_00912 · Cited by 1,156 · see citing works
Trial of Solanezumab for Mild Dementia Due to Alzheimer’s Disease
Honig, L. S., Vellas, B., Woodward, M., Boada, M., Bullock, R., Borrie, M., Hager, K., Andreasen, N., Scarpini, E., Liu-Seifert, H., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1705971 · Cited by 1,049 · cited 1× · see citing works
“This is analogous to the use of biomarkers in clinical trials being used as secondary outcomes— for example, changes in amyloid-beta measures in clinical trials for Alzheimer dementia may suggest possible treatment efficacy even if no change is seen in cognitive scores (18).”
MethodComputational psychiatry as a bridge from neuroscience to clinical applications
Huys, Q. J. M., Maia, T. V., & Frank, M. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4238 · Cited by 1,002 · see citing works
Digital Phenotyping: Technology for a New Science of Behavior
Insel, T. R. (2017) · JAMA · doi:10.1001/jama.2017.11295 · Cited by 833 · see citing works
The Current State of Cognitive Therapy: A 40-Year Retrospective
Beck, A. T. (2005) · Archives of General Psychiatry · doi:10.1001/archpsyc.62.9.953 · Cited by 816 · see citing works
The Origins and Current Status of Behavioral Activation Treatments for Depression
Dimidjian, S., Barrera, M., Martell, C., Muñoz, R. F., & Lewinsohn, P. M. (2011) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032210-104535 · Cited by 785 · cited 2× · see citing works
“Here, a possible psychological mechanism maintaining their depression would be behavioral avoidance (14).”
BackgroundThe Lancet Psychiatry Commission on psychological treatments research in tomorrow's science
Holmes, E. A., Ghaderi, A., Harmer, C. J., Ramchandani, P. G., Cuijpers, P., Morrison, A. P., Roiser, J. P., Bockting, C. L. H., O'Connor, R. C., Shafran, R., et al. (2018) · The Lancet Psychiatry · doi:10.1016/s2215-0366(17)30513-8 · Cited by 624 · see citing works
The third wave of cognitive behavioral therapy and the rise of process-based care
Hayes, S. C., & Hofmann, S. G. (2017) · World Psychiatry · doi:10.1002/wps.20442 · Cited by 613 · see citing works
Large-scale analysis of test–retest reliabilities of self-regulation measures
Enkavi, A. Z., Eisenberg, I. W., Bissett, P. G., Mazza, G. L., MacKinnon, D. P., Marsch, L. A., & Poldrack, R. A. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1818430116 · Cited by 489 · see citing works
Uncovering the structure of self-regulation through data-driven ontology discovery
Eisenberg, I. W., Bissett, P. G., Enkavi, A. Z., Li, J. K., MacKinnon, D. P., Marsch, L. A., & Poldrack, R. A. (2019) · Nature Communications · doi:10.1038/s41467-019-10301-1 · Cited by 473 · see citing works
A computational and neural model of momentary subjective well-being
Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1407535111 · Cited by 444 · see citing works
Self-citationPrediction error in reinforcement learning: A meta-analysis of neuroimaging studies
Garrison, J. R., Erdeniz, B., & Done, J. (2013) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.03.023 · Cited by 430 · see citing works
Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms
Niv, Y., Daniel, R., Geana, A., Gershman, S. J., Leong, Y. C., Radulescu, A., & Wilson, R. C. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.2978-14.2015 · Cited by 362 · see citing works
Conceptual foundations of the transdiagnostic approach to CBT
Mansell, W., Harvey, A., Watkins, E., & Shafran, R. (2009) · Journal of Cognitive Psychotherapy · doi:10.1891/0889-8391.23.1.6 · Cited by 347 · see citing works
Klein-Flügge, M. C., Kennerley, S. W., Friston, K. J., & Bestmann, S. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.0292-16.2016 · Cited by 247 · see citing works
Chase, H. W., Kumar, P., Eickhoff, S. B., & Dombrovski, A. Y. (2015) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-015-0338-7 · Cited by 225 · see citing works
Association of neural and emotional impacts of reward prediction errors with major depression
Rutledge, R. B., Moutoussis, M., Smittenaar, P., Zeidman, P., Taylor, T., Hrynkiewicz, L., Lam, J., Skandali, N., Siegel, J. Z., Ousdal, O. T., et al. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.1713 · Cited by 209 · see citing works
Self-citationGodlewska, B. R., Browning, M., Norbury, R., Cowen, P. J., & Harmer, C. J. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.130 · Cited by 181 · see citing works
Altered learning under uncertainty in unmedicated mood and anxiety disorders
Aylward, J., Valton, V., Ahn, W., Bond, R. L., Dayan, P., Roiser, J. P., & Robinson, O. J. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0628-0 · Cited by 164 · see citing works
Separate mesocortical and mesolimbic pathways encode effort and reward learning signals
Hauser, T. U., Eldar, E., & Dolan, R. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1705643114 · Cited by 135 · see citing works
Role of Dopamine D2 Receptors in Human Reinforcement Learning
Eisenegger, C., Naef, M., Linssen, A., Clark, L., Gandamaneni, P. K., Müller, U., & Robbins, T. W. (2014) · Neuropsychopharmacology · doi:10.1038/npp.2014.84 · Cited by 134 · see citing works
Gelo, O. C. G., & Salvatore, S. (2016) · Journal of Counseling Psychology · doi:10.1037/cou0000150 · Cited by 131 · see citing works
Reinforcement Learning: An Introduction, by Sutton, R.S. and Barto, A.G.
Montague, P. R. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01331-5 · Cited by 120 · see citing works
Mood Instability and Reward Dysregulation—A Neurocomputational Model of Bipolar Disorder
Mason, L., Eldar, E., & Rutledge, R. B. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.3163 · Cited by 100 · see citing works
Self-citationAffective bias as a rational response to the statistics of rewards and punishments
Pulcu, E., & Browning, M. D. (2017) · eLife · doi:10.7554/elife.27879 · Cited by 88 · see citing works
Decodability of Reward Learning Signals Predicts Mood Fluctuations
Eldar, E., Roth, C., Dayan, P., & Dolan, R. J. (2018) · Current Biology · doi:10.1016/j.cub.2018.03.038 · Cited by 84 · see citing works
Metacognitive training for schizophrenia spectrum patients: a meta-analysis on outcome studies
van Oosterhout, B., Smit, F., A, L. K. M., Castelein, S., Staring, A. B. P., & van der Gaag, M. (2015) · Psychological Medicine · doi:10.1017/s0033291715001105 · Cited by 83 · see citing works
Moutoussis, M., Shahar, N., Hauser, T., & Dolan, R. (2017) · Computational Psychiatry · doi:10.1162/cpsy_a_00014 · Cited by 68 · see citing works
Mansell, W., & Huddy, V. C. (2018) · Review of General Psychology · doi:10.1037/gpr0000147 · Cited by 67 · see citing works
Learning and Choice in Mood Disorders: Searching for the Computational Parameters of Anhedonia
Robinson, O. J., & Chase, H. W. (2017) · Computational Psychiatry · doi:10.1162/cpsy_a_00009 · Cited by 66 · see citing works
Scholl, J., & Klein-Flügge, M. C. (2017) · Behavioural Brain Research · doi:10.1016/j.bbr.2017.09.050 · Cited by 62 · see citing works
A biopsychosocial model based on negative feedback and control
Carey, T. A., Mansell, W., & Tai, S. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00094 · Cited by 57 · see citing works
Queirazza, F., Fouragnan, E. F., Steele, J. D., Cavanagh, J., & Philiastides, M. G. (2019) · Science Advances · doi:10.1126/sciadv.aav4962 · Cited by 47 · see citing works
Cognitive Behavioral Therapy Normalizes Functional Connectivity for Social Threat in Psychosis
Mason, L., PETERS, E. R., Dima, D., Williams, S., & Kumari, V. (2015) · Schizophrenia Bulletin · doi:10.1093/schbul/sbv153 · Cited by 38 · see citing works
Self-citationSalvatore, S., Tschacher, W., Gelo, O. C. G., & Koch, S. C. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00914 · Cited by 34 · see citing works
Mason, L., PETERS, E. R., Williams, S., & Kumari, V. (2017) · Translational Psychiatry · doi:10.1038/tp.2016.263 · Cited by 33 · see citing works
Self-citationMason, L., PETERS, E. R., & Kumari, V. (2016) · Australian & New Zealand Journal of Psychiatry · doi:10.1177/0004867415624970 · Cited by 24 · see citing works
Self-citationUnderstanding Psychotherapy: Fifty years of Client-centred Theory and Practice
Patterson, C. H. (2014) · Medical Entomology and Zoology · Cited by 22 · see citing works
Smith, M. B. (1990) · Journal of Humanistic Psychology · doi:10.1177/002216789003000402 · Cited by 21 · see citing works
Smith, M. B. (1990) · Journal of Humanistic Psychology · doi:10.1177/0022167890301002 · Cited by 15 · see citing works
Oxford Guide to Behavioural Experiments in Cognitive Therapy
Rees, C. S. (2005) · Behaviour Change · doi:10.1375/bech.2005.22.3.195 · Cited by 11 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Individual differences in computational psychiatry: A review of current challenges
Karvelis, P., Paulus, M. P., & Diaconescu, A. O. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105137 · Cited by 113 · see citing works
Gibbs-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
Riva, G., Serino, S., Lernia, D. D., & Pagnini, F. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.749268 · Cited by 44 · see citing works
Reliability of Decision-Making and Reinforcement Learning Computational Parameters
Mkrtchian, A., Valton, V., & Roiser, J. P. (2023) · Computational Psychiatry · doi:10.5334/cpsy.86 · Cited by 41 · see citing works
Disordered Decision Making: A Cognitive Framework for Apathy and Impulsivity in Huntington’s Disease
Morris, L., O'Callaghan, C., & Le Heron, C. (2022) · Movement Disorders · doi:10.1002/mds.29013 · Cited by 36 · see citing works
Computational models of subjective feelings in psychiatryPreprint
Kao, C.-H., Feng, G., Hur, J. K., Jarvis, H., & Rutledge, R. B. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.105008 · Cited by 46 · see citing works
Self-citationSmith, R. S., Taylor, S., Stewart, J. L., Guinjoan, S. M., Ironside, M., Kirlić, N., Ekhtiari, H., White, E. J., Zheng, H., Kuplicki, R., et al. (2022) · Computational Psychiatry · doi:10.5334/cpsy.85 · Cited by 27 · see citing works
Yip, S. W., Barch, D. M., Chase, H. W., Flagel, S. B., Huys, Q. J. M., Konova, A. B., Montague, P. R., & Paulus, M. P. (2022) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2022.03.011 · Cited by 24 · see citing works
Pitliya, R. J., Nelson, B. D., Hajcak, G., & Jin, J. (2022) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-022-00936-y · Cited by 21 · see citing works
Computational reinforcement learning, reward (and punishment), and dopamine in psychiatric disorders
Liebenow, B., Jones, R., DiMarco, E. K., Trattner, J. D., Humphries, J. B., Sands, L. P., Spry, K. P., Johnson, C. K., Farkas, E. B., Jiang, A., et al. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.886297 · Cited by 19 · see citing works
Acute stress impairs reward learning in men
Carvalheiro, J., Conceição, V. A., Mesquita, A. R., & Seara‐Cardoso, A. (2020) · Brain and Cognition · doi:10.1016/j.bandc.2020.105657 · Cited by 18 · see citing works
A Computational Model of Hopelessness and Active-Escape Bias in Suicidality
Karvelis, P., & Diaconescu, A. O. (2022) · Computational Psychiatry · doi:10.5334/cpsy.80 · Cited by 21 · see citing works
Acute stress blunts prediction error signals in the dorsal striatum during reinforcement learning
Carvalheiro, J., Conceição, V. A., Mesquita, A. R., & Seara‐Cardoso, A. (2021) · Neurobiology of Stress · doi:10.1016/j.ynstr.2021.100412 · Cited by 16 · see citing works
Spatiotemporal Precision of Neuroimaging in Psychiatry
McFadyen, J., & Dolan, R. J. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.08.016 · Cited by 15 · see citing works
The influence of anxiety on exploration: A review of computational modeling studies
Chou, K.-P., Wilson, R. C., & Smith, R. S. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105940 · Cited by 15 · see citing works
Artificial Intelligence in Emotion Quantification : A Prospective Overview
Liu, F. (2024) · CAAI Artificial Intelligence Research · doi:10.26599/air.2024.9150040 · Cited by 18 · see citing works
Reliability of Decision-Making and Reinforcement Learning Computational ParametersPreprint
Mkrtchian, A., Valton, V., & Roiser, J. P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.30.450026 · Cited by 12 · see citing works
Reward and punishment learning deficits among bipolar disorder subtypes
Pouchon, A., Vinckier, F., Dondé, C., Gueguen, M. C. M., Polosan, M., & Bastin, J. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.08.075 · Cited by 10 · see citing works
Shaw, A. D., Sumner, R. L., & Berndt, L. C. S. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1713833 · Cited by 9 · see citing works
“Treatments like exposure therapy can be seen as attempts to recalibrate these prediction weights – by repeated safe exposures, the patient ’ s brain is encouraged to assign greater weight to the “ I am safe ” prediction relative to the “ something bad will happen ” prediction error (66).”
Pott, J., & Schilbach, L. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1010012 · Cited by 8 · see citing works
Idei, H., & Yamashita, Y. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.10.005 · Cited by 9 · see citing works
Self-esteem depends on beliefs about the rate of change of social approval
Low, A. A. Y., Hopper, W. J. T., Angelescu, I., Mason, L., Will, G., & Moutoussis, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-10260-6 · Cited by 9 · see citing works
Self-citationNiederlohmann, E. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1749364 · Cited by 8 · see citing works
Vassileva, J., Lee, J.-H., Psederska, E., & Ahn, W. (2023) · Neuromethods · doi:10.1007/978-1-0716-3230-7_14 · Cited by 6 · see citing works
Niederlohmann, E. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1817747 · Cited by 4 · see citing works
Sumedrea, A. G., Sumedrea, C. M., & Săvulescu, F. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8080088 · Cited by 2 · see citing works
Queirazza, F., & Philiastides, M. G. (2025) · npj Mental Health Research · doi:10.1038/s44184-025-00165-3 · Cited by 2 · see citing works
Beltzer, M., Daniel, K. E., Daros, A. R., & Teachman, B. A. (2023) · JMIR Formative Research · doi:10.2196/44888 · Cited by 1 · see citing works
Doing the Math in Exposure Therapy
Fullana, M. A., & Soriano‐Mas, C. (2021) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.07.007 · Cited by 1 · see citing works
Absence of Systematic Effects of Internalizing Psychopathology on Learning Under UncertaintyPreprint
Satti, M. H., Wille, K., Nassar, M. R., Cichy, R. M., Schuck, N. W., Dayan, P., & Bruckner, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.12.653409 · Cited by 1 · see citing works
Absence of Systematic Effects of Internalizing Psychopathology on Learning Under UncertaintyPreprint
Satti, M. H., Wille, K., Nassar, M. R., Cichy, R. M., Schuck, N. W., Dayan, P., & Bruckner, R. (2025) · eLife · doi:10.7554/elife.107611.1 · Cited by 1 · see citing works
Vassileva, J., Lee, J. H., Psederska, E., & Ahn, W. (2022) · doi:10.31234/osf.io/uyzeg
Young, J. H. (2023) · Wiley eBooks · doi:10.1002/9781119767398.furread
Absence of Systematic Effects of Internalizing Psychopathology on Learning Under UncertaintyPreprint
Satti, M. H., Wille, K., Nassar, M. R., Cichy, R. M., Schuck, N. W., Dayan, P., & Bruckner, R. (2025) · eLife · doi:10.7554/elife.107611
Hodson, R., Mehta, M., Taylor, S., Lavalley, C. A., Stewart, J. L., Guinjoan, S. M., Ironside, M., White, E. J., Kuplicki, R., Paulus, M. P., et al. (2025) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2025.112922
Value-Based Decision-Making in the Wild: Opportunities and Challenges
Kühnel, A., Thurn, L., Steward, T., & Kroemer, N. B. (2025) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-032-00880-0_10
Computational Approaches in Borderline Personality Disorder
Amjad, E., & Sokouti, B. (2025) · Springer eBooks · doi:10.1007/978-3-031-40858-8_571-1
- Authors
- Nair, A., Rutledge, R. B., & Mason, L.
- Affiliations
- UCL Queen Square Institute of Neurology (GB)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Mason, Liam
- Venue
- Frontiers in Psychiatry
- Publisher
- Frontiers Media SA
- Published
- 2020-03-18
- Volume
- 11
- Pages
- 140-140
- ISSN
- 1664-0640 (ISSN-L)
- PMC
- PMC7093344
- OpenAlex
- W3012224178
- Cited by
- 37
- References
- 45
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 3 · 2023: 7 · 2022: 12 · 2021: 4 · 2020: 1
- FWCI
- 5.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychiatry (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Medical Research Council
- Wellcome Trust
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- computational psychiatry, cognitive behavioral therapy, neural circuits, personalized treatment, treatment response monitoring, depression, anxiety, clinical translation
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
