The DOI matches its Crossref record; 97 of the 97 references it cites were found in a registry or an index.
Testing Computational Models of Dopamine and Noradrenaline Dysfunction in Attention Deficit/Hyperactivity Disorder
Abstract
We test our neurocomputational model of fronto-striatal dopamine (DA) and noradrenaline (NA) function for understanding cognitive and motivational deficits in attention deficit/hyperactivity disorder (ADHD). Our model predicts that low striatal DA levels in ADHD should lead to deficits in 'Go' learning from positive reinforcement, which should be alleviated by stimulant medications, as observed with DA manipulations in other populations. Indeed, while nonmedicated adult ADHD participants were impaired at both positive (Go) and negative (NoGo) reinforcement learning, only the former deficits were ameliorated by medication. We also found evidence for our model's extension of the same striatal DA mechanisms to working memory, via interactions with prefrontal cortex. In a modified AX-continuous performance task, ADHD participants showed reduced sensitivity to working memory contextual information, despite no global performance deficits, and were more susceptible to the influence of distractor stimuli presented during the delay. These effects were reversed with stimulant medications. Moreover, the tendency for medications to improve Go relative to NoGo reinforcement learning was predictive of their improvement in working memory in distracting conditions, suggestive of common DA mechanisms and supporting a unified account of DA function in ADHD. However, other ADHD effects such as erratic trial-to-trial switching and reaction time variability are not accounted for by model DA mechanisms, and are instead consistent with cortical noradrenergic dysfunction and associated computational models. Accordingly, putative NA deficits were correlated with each other and independent of putative DA-related deficits. Taken together, our results demonstrate the usefulness of computational approaches for understanding cognitive deficits in ADHD.
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
“Ziegler et al. (2016) propose that the Basal ganglia model (Frank et al., 2007) predicts decreased positive but increased negative reward prediction errors”
From Are prediction error modifications domain specific in autism but domain general in ADHD? (2025) · cites this paper as Frank et al., 2007
In the citing paper
“There have been computational studies performed that also show low striatal dopamine levels in ADHD patients which leads to deficits in goal-oriented learning with positive reinforcements, which is often enhanced exogenously with stimulant medications [19].”
From The Impact of Social Media & Technology on Child and Adolescent Mental Health (2025) · cites this paper as [19]
Citations
Cites
Diagnostic and Statistical Manual of Mental Disorders
Williams, J. B. W., & First, M. B. (2013) · Oxford University Press eBooks · doi:10.1093/acrefore/9780199975839.013.104 · Cited by 109,842
Parallel organization of functionally segregated circuits linking basal ganglia and cortex
Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.09.030186.002041 · Cited by 8,527
Aston-Jones, G., & Cohen, J. D. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135709 · Cited by 4,472
Willcutt, E. G., Doyle, A. E., Nigg, J. T., Faraone, S. V., & Pennington, B. F. (2005) · Biological Psychiatry · doi:10.1016/j.biopsych.2005.02.006 · Cited by 3,568
Separate neural systems value immediate and delayed monetary rewards
McClure, S. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2004) · Science · doi:10.1126/science.1100907 · Cited by 3,089
Getting formal with dopamine and reward
Schultz, W. (2002) · Neuron · doi:10.1016/s0896-6273(02)00967-4 · Cited by 2,574
Molecular genetics of attention-deficit/hyperactivity disorder
Faraone, S. V., Perlis, R. H., Doyle, A. E., Smoller, J. W., Goralnick, J. J., Holmgren, M. A., & Sklar, P. (2005) · Biological Psychiatry · doi:10.1016/j.biopsych.2004.11.024 · Cited by 2,357
By Carrot or by Stick: Cognitive Reinforcement Learning in Parkinsonism
Frank, M. J., Seeberger, L. C., & O'Reilly, R. C. (2004) · Science · doi:10.1126/science.1102941 · Cited by 1,964
MARTINUSSEN, R., HAYDEN, J., HOGG-JOHNSON, S., & TANNOCK, R. (2005) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1097/01.chi.0000153228.72591.73 · Cited by 1,297 · cited 1×
“…memory manipulationFwhich requires selective updating of some information while working on a subset of a problemFis typically more impaired in ADHD than maintenance (Martinussen et al, 2005); similar updating/manipulation-specific deficits are observed in Parkinson's disease (see Cools, 2006).”
BackgroundCharacterizing cognition in ADHD: beyond executive dysfunction
Castellanos, F. X., Sonuga-Barke, E. J. S., Milham, M. P., & Tannock, R. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.01.011 · Cited by 1,170
Flagged first, then by citations
Research works printed but not linked: 1
- Santamaria A, O'Reilly R, Willcutt E (in preparation). Working memory deficits explain timing impairments in ADHD.
Cited by
Sorted by citations
Items 101–200 of 228
Véronneau-Veilleux, F., Robaey, P., Ursino, M., & Nekka, F. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.849323 · Cited by 32
Kofler, M. J., Groves, N. B., Chan, E. S. M., Marsh, C. L., Cole, A. M., Gaye, F., Cibrian, E., Tatsuki, M. O., & Singh, L. J. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1277583 · Cited by 35
Silvetti, M., Wiersema, J. R., Sonuga-Barke, E. J. S., & Verguts, T. (2013) · Neural Networks · doi:10.1016/j.neunet.2013.05.008 · Cited by 36
Nampoothiri, M., Gurram, P. C., Manandhar, S., Satarker, S., Mudgal, J., & Arora, D. S. (2022) · CNS & Neurological Disorders - Drug Targets · doi:10.2174/1871527321666220413090541 · Cited by 33
Seu, E., & Jentsch, J. D. (2009) · Neuropharmacology · doi:10.1016/j.neuropharm.2009.08.007 · Cited by 32
Broadway, J. M., Frank, M. J., & Cavanagh, J. F. (2018) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-018-0584-6 · Cited by 30
Goal-Driven Cognition in the Brain: A Computational FrameworkPreprint
O’Reilly, R. C., Hazy, T. E., Mollick, J., Mackie, P., & Herd, S. A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.7591 · Cited by 54
Reinforcement learning in young adults with developmental language impairment
Lee, J. C., & Tomblin, J. B. (2012) · Brain and Language · doi:10.1016/j.bandl.2012.07.009 · Cited by 30
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
The basal ganglia in reward and decision making
Doll, B. B., & Frank, M. J. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374620-7.00019-4 · Cited by 31
Attention deficits linked with proclivity to explore while foraging
Barack, D. L., Ludwig, V. U., Parodi, F., Ahmed, N., Brannon, E. M., Ramakrishnan, A., & Platt, M. L. (2024) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2022.2584 · Cited by 31
Luman, M., Goos, V., & Oosterlaan, J. (2014) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-014-9934-1 · Cited by 30
CNTRICS Imaging Biomarkers Final Task Selection: Long-Term Memory and Reinforcement Learning
Ragland, J. D., Cohen, N. J., Cools, R., Frank, M., Hannula, D. E., & Ranganath, C. (2011) · Schizophrenia Bulletin · doi:10.1093/schbul/sbr168 · Cited by 27
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
Hyperbilirubinemia: Subcortical Mechanisms of Cognitive and Behavioral Dysfunction
Koziol, L. F., Budding, D. E., & Chidekel, D. (2013) · Pediatric Neurology · doi:10.1016/j.pediatrneurol.2012.06.019 · Cited by 25
Kim, B.-N., Kim, J.-W., Cummins, T. D. R., Bellgrove, M. A., Hawi, Z., Hong, S.-B., Yang, Y.-H., Kim, H.-J., Shin, M., Cho, S.-C., et al. (2013) · Journal of Clinical Psychopharmacology · doi:10.1097/jcp.0b013e31828f9fc3 · Cited by 24
Linking Across Levels of Computation in Model-Based Cognitive Neuroscience
Frank, M. J. (2015) · Springer eBooks · doi:10.1007/978-1-4939-2236-9_8 · Cited by 24
Video games as rich environments to foster brain plasticity
Nahum, M., & Bavelier, D. (2020) · Handbook of clinical neurology · doi:10.1016/b978-0-444-63934-9.00010-x · Cited by 26
Pajkossy, P., Szöllősi, Á., Demeter, G., & Racsmány, M. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00506 · Cited by 21
Gong, J., Yuan, J., Wang, S., Shi, L., Cui, X., & Luo, X. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099570 · Cited by 27
Paradoxical enhancement of choice reaction time performance in patients with major depression
Chase, H. W., Michael, A., Bullmore, E. T., Sahakian, B. J., & Robbins, T. W. (2009) · Journal of Psychopharmacology · doi:10.1177/0269881109104883 · Cited by 25
Larson, M. J., Clayson, P. E., Primosch, M., Leyton, M., & Steffensen, S. C. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140770 · Cited by 23
Cáceres, P., & Martín, R. S. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00204 · Cited by 22
Reinforcement Contingency Learning in Children with ADHD: Back to the Basics of Behavior Therapy
Meyer, H. D., Beckers, T., Tripp, G., & der Oord, S. V. (2019) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-019-00572-z · Cited by 26
Thoma, P., Edel, M., Boris Suchan, A., & Bellebaum, C. (2014) · Psychiatry Research · doi:10.1016/j.psychres.2014.11.006 · Cited by 27
Groen, Y., Tucha, O. M., Wijers, A. A., & Althaus, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059240 · Cited by 24
Hui, L., Zhang, X., Yu, Y. Q., Han, M., Huang, X., Chen, D. C., Wang, Z. R., Du, W. L., Kou, C. G., Yu, Q., et al. (2013) · Schizophrenia Research · doi:10.1016/j.schres.2013.04.035 · Cited by 21
Spontaneous brain activity underlying auditory hallucinations in the hearing-impaired
Marschall, T. M., Ćurĉić‐Blake, B., Brederoo, S., Renken, R. J., Linszen, M. M. J., Koops, S., & Sommer, I. E. (2021) · Cortex · doi:10.1016/j.cortex.2020.12.005 · 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
A Biased Bayesian Inference for Decision-Making and Cognitive Control
Matsumori, K., Koike, Y., & Matsumoto, K. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00734 · Cited by 19
Wiesner, C. D., Molzow, I., Prehn‐Kristensen, A., & Baving, L. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00167 · Cited by 18
Mueller, K. L., & Tomblin, J. B. (2012) · Topics in Language Disorders · doi:10.1097/tld.0b013e318261ffdd · Cited by 17
Feedback 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
Cascone, A., Calabro, F. J., Foran, W., Larsen, B., Nugiel, T., Parr, A. C., Tervo‐Clemmens, B., Luna, B., & Cohen, J. R. (2023) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2023.101274 · Cited by 18
Nissan, N., Hertz, U., Shahar, N., & Gabay, Y. (2023) · Behavioral and Brain Functions · doi:10.1186/s12993-023-00207-w · Cited by 18
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
Atypical Reinforcement Learning in Developmental Dyslexia
Massarwe, A. O., Nissan, N., & Gabay, Y. (2021) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617721000266 · Cited by 14
Zaehle, T., & Krauel, K. (2021) · Progress in brain research · doi:10.1016/bs.pbr.2021.03.001 · Cited by 17
Cockburn, J., & Holroyd, C. B. (2010) · Brain Research · doi:10.1016/j.brainres.2010.09.065 · Cited by 16
Gabay, Y., & Goldfarb, L. (2017) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2017.04.012 · Cited by 14
KTN1 variants and risk for attention deficit hyperactivity disorder
Luο, X., Guo, X., Tan, Y., Zhang, Y., García-Milian, R., Wang, Z., Shi, J., Yu, T. Y., Ji, J., Wang, X., et al. (2020) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32782 · Cited by 13
Temperament and Character Correlates of Neuropsychological Performance
Cassimjee, N., & Murphy, R. (2010) · South African Journal of Psychology · doi:10.1177/008124631004000202 · Cited by 15
Slama, H., Fery, P., Verheulpen, D., Vanzeveren, N., & Bogaert, P. V. (2014) · Journal of Child Neurology · doi:10.1177/0883073814550498 · Cited by 14
Richards, K. L., Karvelis, P., Lawrie, S. M., & Seriès, P. (2020) · PLoS ONE · doi:10.1371/journal.pone.0243100 · Cited by 12
Developing Cognitive Control: The Costs and Benefits of Active, Abstract Representations
Munakata, Y., Snyder, H. R., & Chatham, C. H. (2013) · Wiley eBooks · doi:10.1002/9781118732373.ch3 · Cited by 13
Attention, Motivation, and Reading Coherence Failure: A Neuropsychological Perspective
Wasserman, T. H. (2012) · Applied Neuropsychology Adult · doi:10.1080/09084282.2011.643940 · Cited by 12
Lane, S. D. (2014) · Journal of Addiction Research & Therapy · doi:10.4172/2155-6105.1000176 · Cited by 11
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
Macoun, S. J., & Kerns, K. A. (2015) · Journal of Clinical and Experimental Neuropsychology · doi:10.1080/13803395.2015.1094028 · Cited by 10
Moschak, T. M., Stang, K. A., Phillips, T. J., & Mitchell, S. H. (2012) · Psychopharmacology · doi:10.1007/s00213-012-2650-z · Cited by 10
Large-Scale Brain Systems and Subcortical Relationships: Practical Applications
Koziol, L. F., Barker, L. A., Hrin, S., & Joyce, A. W. (2014) · Applied Neuropsychology Child · doi:10.1080/21622965.2014.946809 · Cited by 8
Ballan, R., Durrant, S. J., Stickgold, R., Morgan, A., Manoach, D. S., & Gabay, Y. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02239-8 · Cited by 9
Berger, C., Müller‐Godeffroy, J., Marx, I., Reis, O., Buchmann, J. A., & Dück, A. (2018) · Brain and Behavior · doi:10.1002/brb3.1155 · Cited by 9
Lee, J. C., Mueller, K. L., & Tomblin, J. B. (2015) · Language Cognition and Neuroscience · doi:10.1080/23273798.2015.1089359 · Cited by 8
Implicit Working Memory: Implications for Assessment and Treatment
Joyce, A. W. (2016) · Applied Neuropsychology Child · doi:10.1080/21622965.2016.1167497 · Cited by 9
Brinker, M., Cohen, J. G., Sharrette, J. A., & Hall, T. A. (2017) · Applied Neuropsychology Child · doi:10.1080/21622965.2017.1401479 · Cited by 8
Are deficits in cognition associated with psychotic-like experiences after cannabis?
Barkus, E., Morrison, P., Forti, M. D., & Murray, R. M. (2016) · Human Psychopharmacology Clinical and Experimental · doi:10.1002/hup.2556 · Cited by 8
Hashemian, F., Mohammadian, S., Riahi, F., Ghaeli, P., & Ghodsi, D. (2011) · Europe PMC (PubMed Central) · Cited by 8
Skvortsova, V., & Hauser, T. U. (2022) · Research Square · doi:10.21203/rs.3.rs-1280535/v1 · Cited by 9
The role of reinforcement learning in shaping the decision policy in methamphetamine use disorders
Ghaderi, S., Hemami, M., Khosrowabadi, R., & Rad, J. A. (2024) · Journal of Choice Modelling · doi:10.1016/j.jocm.2024.100469 · Cited by 7
Ghaderi, S., Rad, J. A., Hemami, M., & Khosrowabadi, R. (2024) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2024.108847 · Cited by 8
Darby, K., & Sederberg, P. B. (2022) · Psychology and Aging · doi:10.1037/pag0000665 · Cited by 9
Aster, H.-C., Romanos, M., Walitza, S., Gerlach, M., Mühlberger, A., Rizzo, A., Andreatta, M., Hasenauer, N., Hartrampf, P. E., Nerlich, K., et al. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.804730 · Cited by 8
Frank, M. J., Scheres, A., & Sherman, S. J. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511731525.019 · Cited by 8
Meyer, H. D., Beckers, T., Tripp, G., & der Oord, S. V. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8091381 · Cited by 9
Mechanisms of automaticity and anticipatory control in fluid intelligence
Joyce, A. W. (2017) · Applied Neuropsychology Child · doi:10.1080/21622965.2017.1317486 · Cited by 7
Dutta, C. N., Christov‐Moore, L., Ombao, H., & Douglas, P. K. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.938501 · Cited by 8
Neuronal and psychological underpinnings of pathological gambling
Singer, B. F., Anselme, P., Robinson, M. J. F., & Vezina, P. (2014) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2014.00230 · Cited by 8
Furukawa, E., Alsop, B., Caparelli-Dáquer, E. M., Casella, E. B., da Costa, R. Q. M., Queiroz, P. D. M., Galvão, P. A., Benevides, L. R. D. S., Jucá-Vasconcelos, H. P., & Tripp, G. (2018) · ADHD Attention Deficit and Hyperactivity Disorders · doi:10.1007/s12402-018-0265-x · Cited by 7
Simulated Dopamine Modulation of a Neurorobotic Model of the Basal Ganglia
Prescott, T. J., Montes-González, F., Gurney, K. R., Humphries, M. D., & Redgrave, P. (2024) · Biomimetics · doi:10.3390/biomimetics9030139 · Cited by 5
Amirlatifi, H. S., Shokravi, S., Sateei, A., Golsefidi, M. A., & Mahmoudjanlo, M. (2018) · International Journal on Algae · doi:10.1615/interjalgae.v20.i2.80 · Cited by 5
Quantifying mechanisms of cognition with an experiment and modeling ecosystem
Weichart, E. R., Darby, K., Fenton, A. W., Jacques, B. G., Kirkpatrick, R. P., Turner, B. M., & Sederberg, P. B. (2021) · Behavior Research Methods · doi:10.3758/s13428-020-01534-w · Cited by 8
Beck, F. (2008) · German Journal of Exercise and Sport Research · doi:10.1007/bf03217046 · Cited by 5
Hale, J. B., Reddy, L. A., Wilcox, G., McLaughlin, A. E., Hain, L. A., Stern, A., Henzel, J. N., & Eusebio, E. (2009) · Wiley eBooks · doi:10.1002/9781118269855.ch11 · Cited by 5
Scholz, V., Waltmann, M., Herzog, N., Horstmann, A., & Deserno, L. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002877 · Cited by 4
Learning and the Basal Ganglia: Benefiting from Action and Reinforcement
Koziol, L. F., & Budding, D. E. (2008) · Springer eBooks · doi:10.1007/978-0-387-84868-6_4 · Cited by 4
The Basal Ganglia: Beyond the Motor System—From Movement to Thought
Koziol, L. F., & Budding, D. E. (2008) · Springer eBooks · doi:10.1007/978-0-387-84868-6_2 · Cited by 4
Are prediction error modifications domain specific in autism but domain general in ADHD?
Plank, I. S., Yurova, A., Pior, A., Nowak, J., Bierlich, A. M., Papazov, B., Shi, Z., & Falter-Wagner, C. M. (2025) · Imaging neuroscience · doi:10.1162/imag.a.942 · Cited by 3
“Ziegler et al. (2016) propose that the Basal ganglia model ( Frank et al., 2007) predicts decreased positive but increased negative reward prediction errors, while the dynamic developmental theory ( Sagvolden et al., 2005) predicts that both positive and negative reward prediction errors are…”
Katabi, G., & Shahar, N. (2024) · Translational Psychiatry · doi:10.1038/s41398-023-02717-7 · Cited by 4
Computational markers show specific deficits for dyslexia and ADHD in complex learning settings
Gabay, Y., Jacob, L., Mansour, A., & Hertz, U. (2025) · npj Science of Learning · doi:10.1038/s41539-025-00323-4 · Cited by 3
Hazy, T. E., Frank, M. J., & O’Reilly, R. C. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511731525.016 · Cited by 3
Coarse-Grained Neural Network Model of the Basal Ganglia to Simulate Reinforcement Learning Tasks
Drapała, J., & Frydecka, D. (2022) · Brain Sciences · doi:10.3390/brainsci12020262 · Cited by 2
Ging-Jehli, N. R., Arnold, L. E., & Zandt, T. V. (2023) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-023-01129-w · Cited by 2
The Relationship Between Mathematics Achievement and Working Memory Across Education Level
Iguchi, N. E. P. (2008) · doi:10.13021/mars/6198 · Cited by 2
Shephard, E. (2014) · Nottingham ePrints (University of Nottingham) · Cited by 2
Mueller, K. L., Murray, J. C., Duff, M., Pennington, B. F., Zubrowski, P., & Tomblin, J. B. (2012) · doi:10.17077/etd.dfynfxnz · Cited by 2
Category learning difficulties in ADHD across modalities and multiple learning systems
Roark, C. L., Anat, Y. B., & Gabay, Y. (2025) · Psychonomic Bulletin & Review · doi:10.3758/s13423-025-02743-0 · Cited by 2
Nawrocki, R. A., Shaalan, M., Shaheen, S. E., & Lorenzon, N. M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045581 · Cited by 1
The impact of ADHD symptom severity on reinforcement and punishment learning among adults
Morris, S. S. J., Raiker, J. S., Mattfeld, A. T., & Fosco, W. D. (2023) · Cognitive Neuropsychiatry · doi:10.1080/13546805.2023.2178398 · Cited by 3
Navakkode, S. (2024) · Springer eBooks · doi:10.1007/978-3-031-54864-2_15 · Cited by 1
Held, L. K., Lesage, E., Kool, W., & Braem, S. (2025) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-025-01350-9 · Cited by 3
Amirlatifi, H. S., Shokravi, S., Sateei, A., Golsefidi, M. A., & Mahmoudjanlo, M. (2018) · Algologia · doi:10.15407/alg28.02.182 · Cited by 1
Ferrier, M. R. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.4702 · Cited by 3
Neural Computations Underpinning Anxiety in Health and Disease
Walters, C. J. (2021) · University of Minnesota Digital Conservancy (University of Minnesota) · Cited by 1
Ghaderi, S., Amanirad, J., Hemami, M., & Khosrowabadi, R. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.08.515688 · Cited by 1
Rare Variant Analyses in Ancestrally Diverse Cohorts Reveal Novel ADHD Risk GenesPreprint
Jung, S., Caballero, M., Olfson, E., Newcorn, J. H., Fernandez, T. V., & Mahjani, B. (2025) · medRxiv · doi:10.1101/2025.01.14.25320294 · Cited by 1
Neurally Inspired Models of Psychological Processes
Mercado, E., & Henderson, C. M. (2012) · Wiley eBooks · doi:10.1002/9781118133880.hop203023
The relationship between temperament, character and executive functioning
Dennison, L. K. (2013) · UpSpace Institutional Repository (University of Pretoria)
Trampush, J. W. (2010) · CUNY Academic Works (City University of New York)
O desempenho de ratos em jogo estratégico e os efeitos da modulação dopaminérgica
Tassi, L. E. (2011) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.41.2011.tde-21092011-084317
Metadata
- Authors
- Frank, M. J., Santamaria, A., O'Reilly, R. C., & Willcutt, E.
- Affiliations
- University of Arizona (US)
- Alion Science and Technology (United States) (US)
- University of Colorado Boulder (US)
- Corresponding author
- Frank, Michael J
- Venue
- Neuropsychopharmacology
- Published
- 2006-12-13
- Volume
- 32
- Issue
- 7
- Pages
- 1583-1599
- ISSN
- 0893-133X (ISSN-L)
- 1740-634X
- OpenAlex
- W2114186661
- Cited by
- 227
- References
- 97
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 9 · 2023: 9 · 2022: 11 · 2021: 8 · 2020: 8 · 2019: 5 · 2018: 12 · 2017: 8 · 2016: 11 · 2015: 7 · 2014: 19 · 2013: 15 · 2012: 16
- FWCI
- 9.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Neuropsychopharmacology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (R01MH069597, MH 069597-01)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- attention deficit hyperactivity disorder, dopamine, norepinephrine, neurocomputational model, frontostriatal circuits, reinforcement learning, positive reinforcement, stimulant medication, working memory, prefrontal cortex, distractor interference, reaction time variability, cognitive impairment, motivational deficits
- MeSH terms
- Adolescent, Adult, Central Nervous System Stimulants, Attention Deficit Disorder with Hyperactivity, Brain, Brain Chemistry, Computer Simulation, Dopamine, Female, Humans, Learning, Male, Memory, Short-Term, Neural Pathways, Norepinephrine, Reinforcement, Psychology, Prefrontal Cortex
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