The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Role of the Medial Prefrontal Cortex in Impaired Decision Making in Juvenile Attention-Deficit/Hyperactivity Disorder
Open accessAbstract
IMPORTANCE: Attention-deficit/hyperactivity disorder (ADHD) has been associated with deficient decision making and learning. Models of ADHD have suggested that these deficits could be caused by impaired reward prediction errors (RPEs). Reward prediction errors are signals that indicate violations of expectations and are known to be encoded by the dopaminergic system. However, the precise learning and decision-making deficits and their neurobiological correlates in ADHD are not well known. OBJECTIVE: To determine the impaired decision-making and learning mechanisms in juvenile ADHD using advanced computational models, as well as the related neural RPE processes using multimodal neuroimaging. DESIGN, SETTING, AND PARTICIPANTS: Twenty adolescents with ADHD and 20 healthy adolescents serving as controls (aged 12-16 years) were examined using a probabilistic reversal learning task while simultaneous functional magnetic resonance imaging and electroencephalogram were recorded. MAIN OUTCOMES AND MEASURES: Learning and decision making were investigated by contrasting a hierarchical Bayesian model with an advanced reinforcement learning model and by comparing the model parameters. The neural correlates of RPEs were studied in functional magnetic resonance imaging and electroencephalogram. RESULTS: Adolescents with ADHD showed more simplistic learning as reflected by the reinforcement learning model (exceedance probability, Px = .92) and had increased exploratory behavior compared with healthy controls (mean [SD] decision steepness parameter β: ADHD, 4.83 [2.97]; controls, 6.04 [2.53]; P = .02). The functional magnetic resonance imaging analysis revealed impaired RPE processing in the medial prefrontal cortex during cue as well as during outcome presentation (P < .05, family-wise error correction). The outcome-related impairment in the medial prefrontal cortex could be attributed to deficient processing at 200 to 400 milliseconds after feedback presentation as reflected by reduced feedback-related negativity (ADHD, 0.61 [3.90] μV; controls, -1.68 [2.52] μV; P = .04). CONCLUSIONS AND RELEVANCE: The combination of computational modeling of behavior and multimodal neuroimaging revealed that impaired decision making and learning mechanisms in adolescents with ADHD are driven by impaired RPE processing in the medial prefrontal cortex. This novel, combined approach furthers the understanding of the pathomechanisms in ADHD and may advance treatment strategies.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“but the study did not separate positive from negative reward prediction errors.”
From Are prediction error modifications domain specific in autism but domain general in ADHD? (2025) · cites this paper as Hauser et al., 2014
In the citing paper
“Indeed, adolescents with ADHD showed reduced reward prediction error processing in the medial prefrontal cortex during a reversal learning paradigm compared with adolescents without ADHD (Hauser et al., 2014)”
From Are prediction error modifications domain specific in autism but domain general in ADHD? (2025) · cites this paper as Hauser et al., 2014
Citations
Cites
The assessment and analysis of handedness: The Edinburgh inventory
Oldfield, R. C. (1971) · Neuropsychologia · doi:10.1016/0028-3932(71)90067-4 · Cited by 35,900
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
KAUFMAN, J., BIRMAHER, B., BRENT, D., RAO, U., FLYNN, C., MORECI, P., WILLIAMSON, D., & RYAN, N. (1997) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1097/00004583-199707000-00021 · Cited by 10,148
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
Holroyd, C. B., & Coles, M. G. H. (2002) · Psychological Review · doi:10.1037/0033-295x.109.4.679 · Cited by 4,027
Getting formal with dopamine and reward
Schultz, W. (2002) · Neuron · doi:10.1016/s0896-6273(02)00967-4 · Cited by 2,574
Dopamine in motivational control: rewarding, aversive, and alerting
Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010) · Neuron · doi:10.1016/j.neuron.2010.11.022 · Cited by 2,396
Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation
Shaw, P., Eckstrand, K., Sharp, W., Blumenthal, J., Lerch, J. P., Greenstein, D., Clasen, L., Evans, A., Giedd, J., & Rapoport, J. L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0707741104 · Cited by 1,819
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
Flagged first, then by citations
Cited by
Sorted by citations
ADHD: Current Concepts and Treatments in Children and Adolescents
Drechsler, R., Brem, S., Brandeis, D., Grünblatt, E., Berger, G. E., & Walitza, S. (2020) · Neuropediatrics · doi:10.1055/s-0040-1701658 · Cited by 521
Self-citationThe PhysIO Toolbox for Modeling Physiological Noise in fMRI Data
Kasper, L., Bollmann, S., Diaconescu, A. O., Hutton, C., Heinzle, J., Iglesias, S., Hauser, T. U., Sebold, M., Manjaly, Z., Pruessmann, K. P., et al. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.10.019 · Cited by 427
Self-citationThe Cerebellum and Psychiatric Disorders
Phillips, J. R., Hewedi, D. H., Eissa, A. M., & Moustafa, A. A. (2015) · Frontiers in Public Health · doi:10.3389/fpubh.2015.00066 · Cited by 357
Computational psychiatry: towards a mathematically informed understanding of mental illness
Adams, R. A., Huys, Q. J. M., & Roiser, J. P. (2015) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2015-310737 · Cited by 285
Silverman, M. H., Jedd, K., & Luciana, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.083 · Cited by 277
Chiu, Y.-H. M., Hsu, H. L., Coull, B. A., Bellinger, D. C., Kloog, I., Schwartz, J. D., Wright, R. O., & Wright, R. J. (2015) · Environment International · doi:10.1016/j.envint.2015.11.010 · Cited by 267
From promise to practice: towards the realisation of AI-informed mental health care
Koutsouleris, N., Hauser, T. U., Skvortsova, V., & De Choudhury, M. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00153-4 · Cited by 251
Self-citationHauser, T. U., Iannaccone, R., Walitza, S., Brandeis, D., & Brem, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.018 · Cited by 203
Self-citationConflict monitoring and error processing: New insights from simultaneous EEG–fMRI
Iannaccone, R., Hauser, T. U., Staempfli, P., Walitza, S., Brandeis, D., & Brem, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.028 · Cited by 210
Self-citationThe research domain criteria framework: The case for anterior cingulate cortex
Holroyd, C. B., & Umemoto, A. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.09.021 · Cited by 187
Sonuga-Barke, E. J. S., Cortese, S., Fairchild, G. F., & Stringaris, A. K. (2015) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12496 · Cited by 177
Pharmacological Fingerprints of Contextual Uncertainty
Marshall, L., Mathys, C., Ruge, D., de Berker, A. O., Dayan, P., Stephan, K. E., & Bestmann, S. (2016) · PLOS Biology · doi:10.1371/journal.pbio.1002575 · Cited by 158
Self-citationHierarchical prediction errors in midbrain and septum during social learning
Diaconescu, A. O., Mathys, C. D., Weber, L. A. E., Kasper, L., Mauer, J., & Stephan, K. E. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw171 · Cited by 138
Self-citationComputational Phenotyping in Psychiatry: A Worked Example
Schwartenbeck, P., & Friston, K. J. (2016) · eNeuro · doi:10.1523/eneuro.0049-16.2016 · Cited by 134
Visual Mismatch and Predictive Coding: A Computational Single-Trial ERP Study
Stefanics, G., Heinzle, J., Horvath, A. A., & Stephan, K. E. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.3365-17.2018 · Cited by 131
Ziegler, G., Hauser, T. U., Moutoussis, M., Bullmore, E. T., Goodyer, I. M., Fonagy, P., Jones, P. B., Lindenberger, U., & Dolan, R. J. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0394-3 · Cited by 120
Self-citationComputational Psychiatry of ADHD: Neural Gain Impairments across Marrian Levels of Analysis
Hauser, T. U., Fiore, V. G., Moutoussis, M., & Dolan, R. J. (2016) · Trends in Neurosciences · doi:10.1016/j.tins.2015.12.009 · Cited by 125
Self-citationClassification and treatment of antisocial individuals: From behavior to biocognition
Brazil, I. A., van Dongen, J. D. M., Maes, J. H. R., Mars, R. B., & Baskin-sommers, A. R. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.10.010 · Cited by 135
Ziegler, S., Pedersen, M. L., Mowinckel, A. M., & Biele, G. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.09.002 · Cited by 110
Reiter, A. M. F., Heinze, H., Schlagenhauf, F., & Deserno, L. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.95 · Cited by 104
Aznar, S., & Hervig, M. E.-S. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.02.008 · Cited by 99
van der Oord, S., & Tripp, G. (2020) · Clinical Child and Family Psychology Review · doi:10.1007/s10567-020-00327-z · Cited by 91
Lawson, R. P., Bisby, J. A., Nord, C. L., Burgess, N., & Rees, G. E. (2020) · Current Biology · doi:10.1016/j.cub.2020.10.043 · Cited by 88
Hauser, T. U., Iannaccone, R., Dolan, R. J., Ball, J., Hättenschwiler, J., Drechsler, R., Rufer, M., Brandeis, D., Walitza, S., & Brem, S. (2017) · Psychological Medicine · doi:10.1017/s0033291716003305 · Cited by 86
Self-citationHenco, L., Diaconescu, A. O., Lahnakoski, J. M., Brandi, M., Hörmann, S., Hennings, J. M., Hasan, A., Papazova, I., Strube, W., Bolis, D., et al. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008162 · Cited by 82
Self-citationAttractor-like dynamics in belief updating in schizophrenia
Adams, R. A., Napier, G., Roiser, J. P., Mathys, C., & Gilleen, J. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.3163-17.2018 · Cited by 77
Self-citationHuman complex exploration strategies are enriched by noradrenaline-modulated heuristics
Dubois, M., Habicht, J., Michely, J., Moran, R., Dolan, R. J., & Hauser, T. U. (2021) · eLife · doi:10.7554/elife.59907 · Cited by 76
Self-citationCowell, W., Bellinger, D. C., Coull, B. A., Gennings, C., Wright, R. O., & Wright, R. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142492 · Cited by 75
Iannaccone, R., Hauser, T. U., Ball, J., Brandeis, D., Walitza, S., & Brem, S. (2015) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-015-0678-4 · Cited by 74
Self-citationAtypical processing of uncertainty in individuals at risk for psychosis
Cole, D. M., Diaconescu, A. O., Pfeiffer, U. J., Brodersen, K. H., Mathys, C. D., Julkowski, D., Ruhrmann, S., Schilbach, L., Tittgemeyer, M., Vogeley, K. T., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102239 · Cited by 72
Self-citationWaltmann, M., Schlagenhauf, F., & Deserno, L. (2022) · Behavior Research Methods · doi:10.3758/s13428-021-01739-7 · Cited by 69
Value-free random exploration is linked to impulsivity
Dubois, M., & Hauser, T. U. (2022) · Nature Communications · doi:10.1038/s41467-022-31918-9 · Cited by 76
Self-citationEckstein, M. K., Master, S. L., Dahl, R. E., Wilbrecht, L., & Collins, A. G. E. (2022) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2022.101106 · Cited by 69
Altered Medial Frontal Feedback Learning Signals in Anorexia Nervosa
Bernardoni, F., Geisler, D., King, J. A., Javadi, A., Ritschel, F., Murr, J., Reiter, A. M. F., Roessner, V., Smolka, M. N., Kiebel, S. J., et al. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.07.024 · Cited by 65
Lenartowicz, A., Lu, S., Rodriguez, C., Lau, E. P., Walshaw, P. D., McCracken, J. T., Cohen, M. S., & Loo, S. K. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.01.023 · Cited by 65
Norman, L. J., Carlisi, C. O., Christakou, A., Murphy, C. M., Chantiluke, K. C., Giampietro, V., Simmons, A., Brammer, M. J., Mataix‐Cols, D., & Rubia, K. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.03.009 · Cited by 61
Demkow, U. A., & Wolańczyk, T. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.106 · Cited by 54
Annual Research Review: Developmental computational psychiatry
Hauser, T. U., Will, G., Dubois, M., & Dolan, R. J. (2018) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12964 · Cited by 55
Self-citationJeong, H., Yim, H. W., Lee, S., Lee, H. K., Potenza, M. N., Jo, S., Son, H. J., & Kim, G. (2020) · Journal of Behavioral Addictions · doi:10.1556/2006.2020.00039 · Cited by 51
Temporally Dissociable Contributions of Human Medial Prefrontal Subregions to Reward-Guided Learning
Hauser, T. U., Hunt, L. T., Iannaccone, R., Walitza, S., Brandeis, D., Brem, S., & Dolan, R. J. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0560-15.2015 · Cited by 46
Self-citationBoecker-Schlier, R., Holz, N. E., Buchmann, A. F., Blomeyer, D., Plichta, M. M., Jennen-Steinmetz, C., Wolf, I., Baumeister, S., Treutlein, J., Rietschel, M., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.006 · Cited by 42
Self-citationAttention-deficit/hyperactivity disorder and the explore/exploit trade-off
Addicott, M. A., Pearson, J. M., Schechter, J. C., Sapyta, J. J., Weiss, M. D., & Kollins, S. H. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-00881-8 · Cited by 43
Test–retest reliability of reinforcement learning parameters
Schaaf, J. V., Weidinger, L., Molleman, L., & van den Bos, W. (2023) · Behavior Research Methods · doi:10.3758/s13428-023-02203-4 · Cited by 47
Altered neural encoding of prediction errors in assault-related posttraumatic stress disorder
Ross, M. C., Lenow, J. K., Kilts, C. D., & Cisler, J. M. (2018) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2018.05.008 · Cited by 44
Waltmann, M., Herzog, N., Horstmann, A., & Deserno, L. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.07.016 · Cited by 40
Neuron Membrane Trafficking and Protein Kinases Involved in Autism and ADHD
Kitagishi, Y., Minami, A., Nakanishi, A., Ogura, Y., & Matsuda, S. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms16023095 · Cited by 37
Disrupted resting brain graph measures in individuals at high risk for alcoholism
Holla, B., Panda, R., Venkatasubramanian, G., Biswal, B. B., Bharath, R. D., & Benegal, V. (2017) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2017.05.002 · Cited by 34
Véronneau-Veilleux, F., Robaey, P., Ursino, M., & Nekka, F. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.849323 · Cited by 32
Social anxiety and dynamic social reinforcement learning in a volatile environment
Beltzer, M. L., Adams, S., Beling, P. A., & Teachman, B. A. (2019) · Clinical Psychological Science · doi:10.1177/2167702619858425 · Cited by 32
Neural arbitration between social and individual learning systems
Diaconescu, A. O., Stecy, M., Kasper, L., Burke, C. J., Nagy, Z., Mathys, C. D., & Tobler, P. N. (2020) · eLife · doi:10.7554/elife.54051 · Cited by 32
Self-citationLearning environment-specific learning rates
Simoens, J., Verguts, T., & Braem, S. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011978 · Cited by 38
Oldehinkel, M., Beckmann, C. F., Franke, B., Hartman, C. A., Hoekstra, P. J., Oosterlaan, J., Heslenfeld, D. J., Buitelaar, J. K., & Mennes, M. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.10.006 · Cited by 36
Failure to retreat: Blunted sensitivity to negative feedback supports risky behavior in adolescents
McCormick, E. M., & Telzer, E. H. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.041 · Cited by 29
Alsop, B., Furukawa, E., Sowerby, P., Jensen, S., Moffat, C., & Tripp, G. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12561 · Cited by 33
Shephard, E., Jackson, G. M., & Groom, M. J. (2016) · International Journal of Developmental Neuroscience · doi:10.1016/j.ijdevneu.2016.04.005 · Cited by 30
Reversal learning strategy in adolescence is associated with prefrontal cortex activation
Boehme, R., Lorenz, R. C., Gleich, T., Romund, L., Pelz, P., Golde, S., Flemming, E., Wold, A., Deserno, L., Behr, J., et al. (2016) · European Journal of Neuroscience · doi:10.1111/ejn.13401 · Cited by 28
Usui, N., Tian, X., Harigai, W., Togawa, S., Utsunomiya, R., Doi, T., Miyoshi, K., Shinoda, K., Tanaka, J., Shimada, S., et al. (2021) · Neurochemistry International · doi:10.1016/j.neuint.2021.105273 · Cited by 27
Chiu, Y.-H. M., Wilson, A., Hsu, H. L., Jamal, H., Mathews, N., Kloog, I., Schwartz, J. D., Bellinger, D. C., Xhani, N., Wright, R. O., et al. (2023) · Environmental Research · doi:10.1016/j.envres.2023.116394 · Cited by 30
Fortune telling addiction: Unfortunately a serious topic about a case report
Grall‐Bronnec, M., Bulteau, S., Victorri‐Vigneau, C., Challet‐Bouju, G., & Sauvaget, A. (2015) · Journal of Behavioral Addictions · doi:10.1556/jba.4.2015.1.7 · Cited by 28
Gu, X., FitzGerald, T. H. B., & Friston, K. J. (2019) · Psychopharmacology · doi:10.1007/s00213-019-05300-5 · Cited by 28
Natsheh, J. Y., & Shiflett, M. W. (2018) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2018.00045 · Cited by 29
Exploration heuristics decrease during youth
Dubois, M., Bowler, A., Moses-Payne, M. E., Habicht, J., Moran, R., Steinbeis, N., & Hauser, T. U. (2022) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-022-01009-9 · Cited by 29
Self-citationAcetylcholine and noradrenaline enhance foraging optimality in humans
Doren, N., Chung, H.-K., Grueschow, M., Quednow, B. B., Hayward-Koennecke, H. K., Jetter, A., & Tobler, P. N. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2305596120 · Cited by 23
Learning and forgetting using reinforced Bayesian change detection
Moens, V., & Zénon, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006713 · Cited by 25
Jeong, H., Kim, D., Song, M. J., Paik, S., & Jung, M. W. (2019) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2020.101773 · Cited by 24
Disrupted reinforcement learning during post-error slowing in ADHD
Chevrier, A., Bhaijiwala, M., Lipszyc, J., Cheyne, D., Graham, S., & Schachar, R. (2019) · PLOS ONE · doi:10.1371/journal.pone.0206780 · Cited by 21
Wetterling, F., McCarthy, H., Tozzi, L., Skokauskas, N., O'Doherty, J. P., Mulligan, A., Meaney, J., Fagan, A. J., Gill, M., & Frodl, T. (2015) · Human Brain Mapping · doi:10.1002/hbm.22944 · Cited by 20
Sidlauskaite, J., Dhar, M., Sonuga-Barke, E. J. S., & Wiersema, J. R. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107330 · Cited by 21
Rahman, F., Coull, B. A., Carroll, K. N., Wilson, A., Just, A. C., Kloog, I., Zhang, X., Wright, R. J., & Chiu, Y.-H. M. (2021) · Environmental Research · doi:10.1016/j.envres.2021.112583 · Cited by 18
Khan, A., de Jong, L. A. W., Kamenski, M. E., Higa, K. K., Lucero, J. D., Young, J. W., Behrens, M. M., & Powell, S. B. (2016) · Neuroscience · doi:10.1016/j.neuroscience.2016.11.022 · Cited by 19
Risky decision-making in children with and without ADHD: A prospective study
Humphreys, K. L., Tottenham, N., & Lee, S. S. (2016) · Child Neuropsychology · doi:10.1080/09297049.2016.1264578 · Cited by 19
A Low-Level Perceptual Correlate of Behavioral and Clinical Deficits in ADHD
Mihali, A., Young, A. G., Adler, L. A., Halassa, M. M., & Ma, W. J. (2018) · Computational Psychiatry · doi:10.1162/cpsy_a_00018 · Cited by 17
Eckstein, M. K., Master, S. L., Dahl, R. E., Wilbrecht, L., & Collins, A. G. E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.04.187971 · Cited by 21
Developmental Differences in Probabilistic Reversal Learning: A Computational Modeling Approach
Weiß, E. O., Kruppa, J. A., Fink, G. R., Herpertz‐Dahlmann, B., Konrad, K., & Schulte‐Rüther, M. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.536596 · Cited by 20
Systematic Review: Attention-Deficit/Hyperactivity Disorder and Instrumental Learning
Hulsbosch, A., Meyer, H. D., Beckers, T., Danckaerts, M., Liefferinge, D. V., Tripp, G., & der Oord, S. V. (2021) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2021.03.009 · Cited by 20
Keute, M., Stenner, M., Mueller, M.-K., Zaehle, T., & Krauel, K. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00381 · Cited by 16
Baumeister, S., Wolf, I., Hohmann, S., Holz, N. E., Boecker-Schlier, R., Banaschewski, T., & Brandeis, D. (2018) · ADHD Attention Deficit and Hyperactivity Disorders · doi:10.1007/s12402-018-0269-6 · Cited by 22
Self-citationFeedback Timing Modulates Probabilistic Learning in Adults with ADHD
Gabay, Y., Shahbari-Khateb, E., & Mendelsohn, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-33551-3 · Cited by 17
The Neurobiology of Impulsive Decision-Making and Reinforcement Learning in Nonhuman Animals
Groman, S. M. (2020) · Current topics in behavioral neurosciences · doi:10.1007/7854_2020_127 · Cited by 16
Nissan, N., Hertz, U., Shahar, N., & Gabay, Y. (2023) · Behavioral and Brain Functions · doi:10.1186/s12993-023-00207-w · Cited by 18
A review on exploration–exploitation trade-off in psychiatric disorders
Jami, A., Abbaszade, S., & Vahabie, A. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-06837-w · Cited by 20
Chen, D., Jia, T., Cheng, W., Cao, M., Banaschewski, T., Barker, G. J., Bokde, A. L. W., Bromberg, U., Büchel, C., Desrivières, S., et al. (2021) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2021.11.030 · Cited by 14
Oerlemans, J., Alejandro, R. J., Roost, D. V., Boon, P., Herdt, V. D., Meurs, A., & Holroyd, C. B. (2024) · Brain · doi:10.1093/brain/awae259 · Cited by 15
Probabilistic Learning in Children With Attention-Deficit/Hyperactivity Disorder
Luman, M., Janssen, T. W. P., Bink, M., van Mourik, R., Maras, A., & Oosterlaan, J. (2020) · Journal of Attention Disorders · doi:10.1177/1087054720905094 · Cited by 17
Zaehle, T., & Krauel, K. (2021) · Progress in brain research · doi:10.1016/bs.pbr.2021.03.001 · Cited by 17
Marković, D., Reiter, A. M. F., & Kiebel, S. J. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006707 · Cited by 13
Capuozzo, A., Rizzato, S., Grossi, G., & Strappini, F. (2024) · Brain Sciences · doi:10.3390/brainsci14111117 · Cited by 21
Keller, L., Dempfle, A., Dahmen, B., Schreiber, S., Adan, R. A. H., Andreani, N. A., Danner, U. N., Eisert, A., Fetissov, S. O., Fischmeister, F. P. S., et al. (2022) · Trials · doi:10.1186/s13063-022-06413-7 · Cited by 13
Vilca, L. W. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.928425 · Cited by 17
Eddy, M. C., & Green, J. T. (2017) · Behavioral Neuroscience · doi:10.1037/bne0000218 · Cited by 16
Richards, K. L., Karvelis, P., Lawrie, S. M., & Seriès, P. (2020) · PLoS ONE · doi:10.1371/journal.pone.0243100 · Cited by 12
Aster, H.-C., Waltmann, M., Busch, A., Romanos, M., Gamer, M., van Noort, B. M., Beck, A., Kappel, V., & Deserno, L. (2024) · NeuroImage Clinical · doi:10.1016/j.nicl.2024.103588 · Cited by 16
Kwon, M., Lee, S. H., & Ahn, W. (2022) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2022.12.003 · Cited by 14
Nickchen, K., Boehme, R., Amador, M. D. M., Hälbig, T. D., Dehnicke, K., Panneck, P., Behr, J., Prass, K., Heinz, A., Deserno, L., et al. (2016) · Brain Imaging and Behavior · doi:10.1007/s11682-016-9660-0 · Cited by 12
Tabula-rasa exploration decreases during youth and is linked to ADHD symptomsPreprint
Dubois, M., Bowler, A., Moses-Payne, M. E., Habicht, J., Steinbeis, N., & Hauser, T. U. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.11.146019 · Cited by 12
Self-citationHsu, H. L., Lane, J. M., Schnaas, L., Coull, B. A., Osorio-Valencia, E., Chiu, Y.-H. M., Wilson, A., Just, A. C., Kloog, I., Bellinger, D. C., et al. (2024) · Environmental Epidemiology · doi:10.1097/ee9.0000000000000291 · Cited by 10
Koithan, E. M., Demeter, D. V., Ali, S. A., Feigelis, M., & Greene, D. J. (2024) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2024.101429 · Cited by 11
Early-Life Adversities Are Associated With Lower Expected Value Signaling in the Adult Brain
Sacu, S., Dubois, M., Hezemans, F. H., Aggensteiner, P.-M., Monninger, M., Brandeis, D., Banaschewski, T., Hauser, T. U., & Holz, N. E. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.04.005 · Cited by 11
Self-citationSkvortsova, V., & Hauser, T. U. (2022) · Research Square · doi:10.21203/rs.3.rs-1280535/v1 · Cited by 9
Self-citationSensory and environmental uncertainty in perceptual decision-making
Fritsch, M., Weilnhammer, V. A., Thiele, P., Heinz, A., & Sterzer, P. (2023) · iScience · doi:10.1016/j.isci.2023.106412 · Cited by 7
Metadata
- Authors
- Hauser, T. U., Iannaccone, R., Ball, J., Mathys, C., Brandeis, D., Walitza, S., & Brem, S.
- Affiliations
- University of Zurich (CH)
- Wellcome Centre for Human Neuroimaging (GB)
- Venue
- JAMA Psychiatry
- Publisher
- American Medical Association (AMA)
- Published
- 2014-10-01
- Volume
- 71
- Issue
- 10
- Pages
- 1165
- ISSN
- 2168-622X (ISSN-L)
- OpenAlex
- W2065928720
- Cited by
- 168
- References
- 64
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 18 · 2023: 15 · 2022: 16 · 2021: 11 · 2020: 16 · 2019: 11 · 2018: 13 · 2017: 6 · 2016: 29 · 2015: 12 · 2014: 2
- FWCI
- 12.46
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- JAMA Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- SDL (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- attention deficit hyperactivity disorder, decision-making, reward prediction error, medial prefrontal cortex, reinforcement learning, probabilistic reversal learning, feedback-related negativity, dopaminergic system, computational modeling, adolescents
- MeSH terms
- Adolescent, Attention Deficit Disorder with Hyperactivity, Brain, Child, Decision Making, Electroencephalography, Female, Humans, Learning, Magnetic Resonance Imaging, Male, Case-Control Studies, Prefrontal Cortex, Functional Neuroimaging
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