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26 - Computational Modeling in Psychiatry

from Part IV - Computational Modeling in Various Cognitive Fields

Published online by Cambridge University Press:  21 April 2023

By
Cody J. Walters ,
Sophia Vinogradov  and
A. David Redish
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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Summary

While psychiatry has made great strides in recent decades toward integrating our increasing understanding of the biological bases of cognition, it nonetheless continues to suffer from imprecise diagnostics and blunt treatment options. Recent advances in computational neuroscience have the potential to address these issues, with a range of neural and cognitive models offering the possibility of a more precise psychiatric nosology with more targeted therapeutics. Here we review a variety of these models, with a special emphasis on their application to addiction, psychosis, anxiety disorders, depression, obsessive-compulsive disorder, autism spectrum disorder, and attention-deficit hyperactivity disorder. We then close with a discussion of potential challenges in incorporating these insights and methods into a clinical setting.

Keywords

psychiatrycomputational neurosciencepsychotherapymathematical modelsneuroscienceDSMpsychopathology
Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 862 - 889
Publisher: Cambridge University Press
Print publication year: 2023

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References

Adams, C. D., & Dickinson, A. (1981). Instrumental responding following reinforcer devaluation. The Quarterly Journal of Experimental Psychology Section B, 33 (2b), 109–121.CrossRefGoogle Scholar
Adams, R. A., Stephan, K. E., Brown, H. R., Frith, C. D., & Friston, K. J. (2013). The computational anatomy of psychosis. Frontiers in Psychiatry, 4, 47.CrossRefGoogle ScholarPubMed
Bach, D. R. (2015). Anxiety-like behavioural inhibition is normative under environmental threat-reward correlations. PLoS Computational Biology, 11 (12), e1004646.CrossRefGoogle ScholarPubMed
Beck, A. T., Emery, G., & Greenberg, R. L. (2005). Anxiety Disorders and Phobias: A Cognitive Perspective. New York, NY: Basic Books.Google Scholar
Bergstrom, D., Carlson, J., Chase, T., Braun, A., et al. (1987). D1 dopamine receptor activation required for postsynaptic expression of d2 agonist effects. Science, 236 (4802), 719–722.Google Scholar
Berns, G. S., & Sejnowski, T. J. (1998). A computational model of how the basal ganglia produce sequences. Journal of Cognitive Neuroscience, 10 (1), 108–121.CrossRefGoogle ScholarPubMed
Blanchard, D. C., & Blanchard, R. J. (2008). Four defensive behaviors, fear, and anxiety. Handbook of Behavioral Neuroscience, 17, 63–79.Google Scholar
Borsboom, D., Cramer, A. O., & Kalis, A. (2019). Brain disorders? Not really: why network structures block reductionism in psychopathology research. Behavioral and Brain Sciences, 42, e2.Google Scholar
Bzdok, D., & Meyer-Lindenberg, A. (2018). Machine learning for precision psychiatry: opportunities and challenges. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3 (3), 223–230.Google ScholarPubMed
Chekroud, A. M. (2015). Unifying treatments for depression: an application of the free energy principle. Frontiers in Psychology, 6, 153.CrossRefGoogle ScholarPubMed
Clayton, N. S., Bussey, T. J., & Dickinson, A. (2003). Can animals recall the past and plan for the future? Nature Reviews Neuroscience, 4 (8), 685.Google Scholar
Conceicao, V. A., Dias, A., Farinha, A. C., & Maia, T. V. (2017). Premonitory urges and tics in Tourette syndrome: computational mechanisms and neural correlates. Current Opinion in Neurobiology, 46, 187–199.CrossRefGoogle ScholarPubMed
Contopoulos-Ioannidis, D. G., Alexiou, G. A., Gouvias, T. C., & Ioannidis, J. P. (2008). Life cycle of translational research for medical interventions. Science, 321 (5894), 1298–1299.Google Scholar
Daw, N. D., Kakade, S., & Dayan, P. (2002). Opponent interactions between serotonin and dopamine. Neural Networks, 15 (4–6), 603–616.CrossRefGoogle ScholarPubMed
Daw, N. D., O’Doherty, J. P., Dayan, P., Seymour, B., & Dolan, R. J. (2006). Cortical substrates for exploratory decisions in humans. Nature, 441 (7095), 876.CrossRefGoogle ScholarPubMed
Dayan, P., Abbott, L. F., & Abbott, L. (2001). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge, MA: MIT Press.Google Scholar
Dayan, P., & Huys, Q. J. (2008). Serotonin, inhibition, and negative mood. PLoS Computational Biology, 4 (2), e4.Google Scholar
Dayan, P., & Huys, Q. J. (2009). Serotonin in affective control. Annual Review of Neuroscience, 32, 95–126.Google Scholar
Declercq, M., De Houwer, J., & Baeyens, F. (2008). Evidence for an expectancy-based theory of avoidance behaviour. Quarterly Journal of Experimental Psychology, 61 (12), 1803–1812.Google Scholar
Dougherty, D. D., Brennan, B. P., Stewart, S. E., Wilhelm, S., Widge, A. S., & Rauch, S. L. (2018). Neuroscientifically informed formulation and treatment planning for patients with obsessive-compulsive disorder: a review. JAMA Psychiatry, 75 (10), 1081–1087.CrossRefGoogle ScholarPubMed
Ehlers, A., Margraf, J., Roth, W. T., Taylor, C. B., & Birbaumer, N. (1988). Anxiety induced by false heart rate feedback in patients with panic disorder. Behaviour Research and Therapy, 26 (1), 1–11.CrossRefGoogle ScholarPubMed
Flagel, S., Pine, D., Ahmari, S., et al. (2016). A Novel Framework for Improving Psychiatric Diagnostic Nosology. Cambridge, MA: MIT Press.Google Scholar
Frank, M. J., Santamaria, A., O’Reilly, R. C., & Willcutt, E. (2007). Testing computational models of dopamine and noradrenaline dysfunction in attention deficit/hyperactivity disorder. Neuropsychopharmacology, 32 (7), 1583.CrossRefGoogle ScholarPubMed
Freeman, J., Garcia, A., Benito, K., et al. (2012). The Pediatric Obsessive Compulsive Disorder Treatment Study for young children (POTS jr): developmental considerations in the rationale, design, and methods. Journal of Obsessive-Compulsive and Related Disorders, 1 (4), 294–300.CrossRefGoogle ScholarPubMed
Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11 (2), 127.CrossRefGoogle ScholarPubMed
Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: the brain as a phantastic organ. Lancet Psychiatry, 1 (2), 148–158.CrossRefGoogle ScholarPubMed
Frith, U. (2003). Autism: Explaining the Enigma. Oxford: Blackwell Publishing.Google Scholar
Garfinkel, S. N., Tiley, C., O’Keeffe, S., Harrison, N. A., Seth, A. K., & Critchley, H. D. (2016). Discrepancies between dimensions of interoception in autism: implications for emotion and anxiety. Biological Psychology, 114, 117–126.Google Scholar
Gazzaniga, M. S., Bogen, J. E., & Sperry, R. W. (1965). Observations on visual perception after disconnexion of the cerebral hemispheres in man. Brain, 88 (2), 221–236.CrossRefGoogle ScholarPubMed
George, M. S., Trimble, M. R., Ring, H. A., Sallee, F., & Robertson, M. M. (1993). Obsessions in obsessive-compulsive disorder with and without Gilles de la Tourette’s syndrome. The American Journal of Psychiatry, 150 (1), 93–97.Google ScholarPubMed
Gilbert, D. L., Budman, C. L., Singer, H. S., Kurlan, R., & Chipkin, R. E. (2014). A D1 receptor antagonist, ecopipam, for treatment of tics in Tourette syndrome. Clinical Neuropharmacology, 37 (1), 26–30.CrossRefGoogle ScholarPubMed
Gillan, C. M., Papmeyer, M., Morein-Zamir, S., et al. (2011). Disruption in the balance between goal-directed behavior and habit learning in obsessive-compulsive disorder. American Journal of Psychiatry, 168 (7), 718–726.Google Scholar
Gillan, C. M., & Robbins, T. W. (2014). Goal-directed learning and obsessive–compulsive disorder. Philosophical Transactions of the Royal Society B: Biological Sciences, 369 (1655), 20130475.Google Scholar
Gómez, C., Lizier, J. T., Schaum, M., et al. (2014). Reduced predictable information in brain signals in autism spectrum disorder. Frontiers in Neuroinformatics, 8, 9.Google Scholar
Gradin, V. B., Kumar, P., Waiter, G., et al. (2011). Expected value and prediction error abnormalities in depression and schizophrenia. Brain, 134 (6), 1751–1764.CrossRefGoogle ScholarPubMed
Gray, J. A. (1982). Précis of the neuropsychology of anxiety: an enquiry into the functions of the septo-hippocampal system. Behavioral and Brain Sciences, 5 (3), 469–484.Google Scholar
Graybiel, A. M. (1995). Building action repertoires: memory and learning functions of the basal ganglia. Current Opinion in Neurobiology, 5 (6), 733–741.Google Scholar
Graybiel, A. M., & Rauch, S. L. (2000). Toward a neurobiology of obsessive-compulsive disorder. Neuron, 28 (2), 343–347.CrossRefGoogle Scholar
Happé, F., & Frith, U. (2006). The weak coherence account: detail-focused cognitive style in autism spectrum disorders. Journal of Autism and Developmental Disorders, 36 (1), 5–25.Google Scholar
Hassabis, D., Kumaran, D., Vann, S. D., & Maguire, E. A. (2007). Patients with hippocampal amnesia cannot imagine new experiences. Proceedings of the National Academy of Sciences, 104 (5), 1726–1731.Google Scholar
Hauser, T. U., Fiore, V. G., Moutoussis, M., & Dolan, R. J. (2016). Computational psychiatry of ADHD: neural gain impairments across marrian levels of analysis. Trends in Neurosciences, 39 (2), 63–73.Google Scholar
Hauser, T. U., Iannaccone, R., Ball, J., Mathys, C., Brandeis, D., Walitza, S., & Brem, S. (2014). Role of the medial prefrontal cortex in impaired decision making in juvenile attention-deficit/hyperactivity disorder. JAMA Psychiatry, 71 (10), 1165–1173.CrossRefGoogle ScholarPubMed
Hebb, D. (1957). The Organization of Behavior. New York, NY: Wiley.Google Scholar
Hertz, J., Krogh, A., Palmer, R. G., & Horner, H. (1991). Introduction to the theory of neural computation. Physics Today, 44, 70.CrossRefGoogle Scholar
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79 (8), 2554–2558.Google Scholar
Huang, Y., & Rao, R. P. (2011). Predictive coding. Wiley Interdisciplinary Reviews Cognitive Science, 2 (5), 580–593.Google Scholar
Huys, Q. J., Daw, N. D., & Dayan, P. (2015). Depression: a decision-theoretic analysis. Annual Review of Neuroscience, 38, 1–23.CrossRefGoogle ScholarPubMed
Huys, Q. J., Eshel, N., O’Nions, E., Sheridan, L., Dayan, P., & Roiser, J. P. (2012). Bonsai trees in your head: how the Pavlovian system sculpts goal-directed choices by pruning decision trees. PLoS Computational Biology, 8 (3), e1002410.Google Scholar
Huys, Q. J., Maia, T. V., & Frank, M. J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19 (3), 404.Google Scholar
Huys, Q. J., Pizzagalli, D. A., Bogdan, R., & Dayan, P. (2013). Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis. Biology of Mood & Anxiety Disorders, 3 (1), 12.CrossRefGoogle ScholarPubMed
Ito, R., & Lee, A. C. (2016). The role of the hippocampus in approach-avoidance conflict decision-making: evidence from rodent and human studies. Behavioural Brain Research, 313, 345–357.Google Scholar
Johnson, A., & Redish, A. D. (2007). Neural ensembles in CA3 transiently encode paths forward of the animal at a decision point. Journal of Neuroscience, 27 (45), 12176–12189.Google Scholar
Kahneman, D. (2011). Thinking, Fast and Slow. Oxford: Macmillan.Google Scholar
Kalanithi, P. S., Zheng, W., Kataoka, Y., et al. (2005). Altered parvalbumin-positive neuron distribution in basal ganglia of individuals with Tourette syndrome. Proceedings of the National Academy of Sciences, 102 (37), 13307–13312.Google Scholar
Kim, E. J., Park, M., Kong, M.-S., Park, S. G., Cho, J., & Kim, J. J. (2015). Alterations of hippocampal place cells in foraging rats facing a “predatory” threat. Current Biology, 25 (10), 1362–1367.Google Scholar
Knill, D. C., & Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and computation. Trends in Neurosciences, 27 (12), 712–719.CrossRefGoogle ScholarPubMed
Koob, G. F., & Volkow, N. D. (2010). Neurocircuitry of addiction. Neuropsychopharmacology, 35 (1), 217.Google Scholar
Laplane, D., Levasseur, M., Pillon, B., et al. (1989). Obsessive-compulsive and other behavioural changes with bilateral basal ganglia lesions: a neuropsychological, magnetic resonance imaging and positron tomography study. Brain, 112 (3), 699–725.Google Scholar
Lashley, K. S. (1951). The Problem of Serial Order in Behavior, Vol. 21. Indianapolis, IN: Bobbs-Merrill.Google Scholar
Lee, D. K., Itti, L., Koch, C., & Braun, J. (1999). Attention activates winner-take-all competition among visual filters. Nature Neuroscience, 2 (4), 375.Google Scholar
Lieberman, J. A. (2015). Shrinks: The Untold Story of Psychiatry. London: Hachette.Google Scholar
Loh, M., Rolls, E. T., & Deco, G. (2007). A dynamical systems hypothesis of schizophrenia. PLoS Computational Biology, 3 (11), e228.Google Scholar
Lovibond, P. F., Saunders, J. C., Weidemann, G., & Mitchell, C. J. (2008). Evidence for expectancy as a mediator of avoidance and anxiety in a laboratory model of human avoidance learning. The Quarterly Journal of Experimental Psychology, 61 (8), 1199–1216.Google Scholar
Lynn, C. W., & Bassett, D. S. (2019). The physics of brain network structure, function and control. Nature Reviews Physics, 1 (5), 318–332.Google Scholar
MacDonald, A. W., Zick, J. L., Chafee, M. V., & Netoff, T. I. (2016). Integrating insults: using fault tree analysis to guide schizophrenia research across levels of analysis. Frontiers in Human Neuroscience, 9, 698.Google Scholar
MacLeod, A. K., & Byrne, A. (1996). Anxiety, depression, and the anticipation of future positive and negative experiences. Journal of Abnormal Psychology, 105 (2), 286.Google Scholar
Maia, T. V., & Conceicao, V. A. (2017). The roles of phasic and tonic dopamine in tic learning and expression. Biological Psychiatry, 82 (6), 401–412.Google Scholar
Maia, T. V., & Frank, M. J. (2011). From reinforcement learning models to psychiatric and neurological disorders. Nature Neuroscience, 14 (2), 154.Google Scholar
Maia, T. V., & McClelland, J. L. (2012). A neurocomputational approach to obsessive-compulsive disorder. Trends in Cognitive Sciences, 16 (1), 14–15.Google Scholar
Mathys, C. (2016). How could we get nosology from computation? In Redish, A. D. & Gordon, J. A. (Eds.), Computational Psychiatry: New Perspectives on Mental Illness. Strüngmann Forum Reports, Vol. 20. Cambridge, MA: MIT Press.Google Scholar
Miloyan, B., Bulley, A., & Suddendorf, T. (2016). Episodic foresight and anxiety: proximate and ultimate perspectives. British Journal of Clinical Psychology, 55 (1), 4–22.Google Scholar
Mobbs, D., Petrovic, P., Marchant, J. L., et al. (2007). When fear is near: threat imminence elicits prefrontal-periaqueductal gray shifts in humans. Science, 317 (5841), 1079–1083.CrossRefGoogle ScholarPubMed
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry. Trends in Cognitive Sciences, 16 (1), 72–80.CrossRefGoogle ScholarPubMed
Moutoussis, M., Shahar, N., Hauser, T. U., & Dolan, R. J. (2018). Computation in psychotherapy, or how computational psychiatry can aid learning-based psychological therapies. Computational Psychiatry, 2, 50–73.Google Scholar
Niedenthal, P. M. (2007). Embodying emotion. Science, 316 (5827), 1002–1005.Google Scholar
NIMH. (2019a). National Institute of Mental Health: Anxiety disorders. Available at: www.nimh.nih.gov/health/topics/anxiety-disorders/index.shtml [last accessed July 22, 2022].Google Scholar
NIMH. (2019b). National Institute of Mental Health: Research domain criteria. Available at: www.nimh.nih.gov/research/research-funded-by-nimh/rdoc/index.shtml [last accessed July 22, 2022].Google Scholar
Nolen-Hoeksema, S. (2000). The role of rumination in depressive disorders and mixed anxiety/depressive symptoms. Journal of Abnormal Psychology, 109 (3), 504.Google Scholar
O’Keefe, J., & Nadel, L. (1978). The Hippocampus as a Cognitive Map. Oxford: Clarendon Press.Google Scholar
Paulus, M. P., & Yu, A. J. (2012). Emotion and decision-making: affect-driven belief systems in anxiety and depression. Trends in Cognitive Sciences, 16 (9), 476–483.Google Scholar
Pellicano, E., & Burr, D. (2012). When the world becomes ‘too real’: a Bayesian explanation of autistic perception. Trends in Cognitive Sciences, 16 (10), 504–510.Google Scholar
Perusini, J. N., & Fanselow, M. S. (2015). Neurobehavioral perspectives on the distinction between fear and anxiety. Learning & Memory, 22 (9), 417–425.Google Scholar
Peterson, B. S., Skudlarski, P., Anderson, A. W., et al. (1998). A functional magnetic resonance imaging study of tic suppression in Tourette syndrome. Archives of General Psychiatry, 55 (4), 326–333.Google Scholar
Ramachandran, V. S., Blakeslee, S., & Shah, N. (1998). Phantoms in the Brain: Probing the Mysteries of the Human Mind. New York, NY: William Morrow.Google Scholar
Raymond, J. G., Steele, J. D., & Seriés, P. (2017). Modeling trait anxiety: from computational processes to personality. Frontiers in Psychiatry, 8, 1.Google Scholar
Redish, A. D. (1999). Beyond the Cognitive Map: From Place Cells to Episodic Memory. Cambridge, MA: MIT Press.Google Scholar
Redish, A. D. (2004). Addiction as a computational process gone awry. Science, 306 (5703), 1944–1947.Google Scholar
Redish, A. D. (2013). The Mind Within the Brain: How We Make Decisions and How Those Decisions Go Wrong. Oxford: Oxford University Press.Google Scholar
Redish, A. D. (2016). Vicarious trial and error. Nature Reviews Neuroscience, 17 (3), 147.Google Scholar
Redish, A. D., & Gordon, J. A. (2016). Computational Psychiatry: New Perspectives on Mental Illness, Vol. 20. Cambridge, MA: MIT Press.Google Scholar
Redish, A. D., Jensen, S., & Johnson, A. (2008). Addiction as vulnerabilities in the decision process. Behavioral and Brain Sciences, 31 (4), 461–487.CrossRefGoogle ScholarPubMed
Redish, A. D., Kummerfeld, E., Morris, R. L., & Love, A. C. (2018). Opinion: reproducibility failures are essential to scientific inquiry. Proceedings of the National Academy of Sciences, 115 (20), 5042–5046.Google Scholar
Reynolds, J. H., & Heeger, D. J. (2009). The normalization model of attention. Neuron, 61 (2), 168–185.Google Scholar
Robinson, T. E., & Berridge, K. C. (2001). Incentive-sensitization and addiction. Addiction, 96 (1), 103–114.Google Scholar
Rolls, E. T., Loh, M., & Deco, G. (2008). An attractor hypothesis of obsessive–compulsive disorder. European Journal of Neuroscience, 28 (4), 782–793.Google Scholar
Sagvolden, T., & Sergeant, J. A. (1998). Attention Deficit/Hyperactivity Disorder: From Brain Dysfunctions to Behaviour. London: Routledge.Google Scholar
Saint-Cyr, J. A., Taylor, A., & Nicholson, K. (1995). Behavior and the basal ganglia. Advances in Neurology, 65, 1–28.Google Scholar
Schacter, D. L., Addis, D. R., & Buckner, R. L. (2008). Episodic simulation of future events: concepts, data, and applications. Annals of the New York Academy of Sciences, 1124 (1), 39–60.Google Scholar
Schmitz, T. W., & Duncan, J. (2018). Normalization and the cholinergic microcircuit: a unified basis for attention. Trends in Cognitive Sciences, 22 (5), 422–437.Google Scholar
Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275 (5306), 1593–1599.Google Scholar
Scoville, W. B., & Milner, B. (1957). Loss of recent memory after bilateral hippocampal lesions. Journal of Neurology, Neurosurgery, and Psychiatry, 20 (1), 11.Google Scholar
Seamans, J. K., & Yang, C. R. (2004). The principal features and mechanisms of dopamine modulation in the prefrontal cortex. Progress in Neurobiology, 74 (1), 1–58.Google Scholar
Seligman, M. E. (1972). Learned helplessness. Annual Review of Medicine, 23 (1), 407–412.CrossRefGoogle ScholarPubMed
Seneca, L. A. (65 ce). Letters from a Stoic. London: HarperCollins.Google Scholar
Seymour, B., Daw, N. D., Roiser, J. P., Dayan, P., & Dolan, R. (2012). Serotonin selectively modulates reward value in human decision-making. Journal of Neuroscience, 32 (17), 5833–5842.Google Scholar
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27 (3), 379–423.CrossRefGoogle Scholar
Smith, A., Li, M., Becker, S., & Kapur, S. (2006). Dopamine, prediction error and associative learning: a model-based account. Network: Computation in Neural Systems, 17 (1), 61–84.Google Scholar
Stephan, K. E., Bach, D. R., Fletcher, P. C., et al. (2016). Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis. Lancet Psychiatry, 3 (1), 77–83.Google Scholar
Suddendorf, T. (2013). The Gap: The Science of What Separates Us from Other Animals. Baltimore: Constellation.Google Scholar
Sutton, R. S., & Barto, A. G. (1998). Introduction to Reinforcement Learning. Cambridge, MA: MIT Press.Google Scholar
Swain, J. E., Scahill, L., Lombroso, P. J., King, R. A., & Leckman, J. F. (2007). Tourette syndrome and tic disorders: a decade of progress. Journal of the American Academy of Child & Adolescent Psychiatry, 46 (8), 947–968.Google Scholar
Tripp, G., & Wickens, J. R. (2008). Research review: dopamine transfer deficit: a neurobiological theory of altered reinforcement mechanisms in ADHD. Journal of Child Psychology and Psychiatry, 49 (7), 691–704.Google Scholar
Tsibulsky, V. L., & Norman, A. B. (1999). Satiety threshold: a quantitative model of maintained cocaine self-administration. Brain Research, 839 (1), 85–93.Google Scholar
Van Boxtel, J. J., & Lu, H. (2013). A predictive coding perspective on autism spectrum disorders. Frontiers in Psychology, 4, 19.Google Scholar
Verduzco-Flores, S., Ermentrout, B., & Bodner, M. (2012). Modeling neuropathologies as disruption of normal sequence generation in working memory networks. Neural Networks, 27, 21–31.Google Scholar
Vinogradov, S. (2017). The golden age of computational psychiatry is within sight. Nature Human Behaviour, 1, 0047.Google Scholar
Walters, C. J., Jubran, J., Sheehan, A., Erickson, M. T., & Redish, A. D. (2019). Avoid-approach conflict behaviors differentially affected by anxiolytics: implications for a computational model of risky decision-making. Neuroscience, 236 (8), 2513–2525.Google Scholar
Walters, C. J., & Redish, A. D. (2018). A case study in computational psychiatry: addiction as failure modes of the decision-making system. In Anticevic, A. & Murray, J. D. (Eds.), Computational Psychiatry: Mathematical Modeling of Mental Illness (Chapter 8, pp. 199–217). Cambridge, MA: Academic Press.Google Scholar
Williams, J., & Dayan, P. (2005). Dopamine, learning, and impulsivity: a biological account of attention-deficit/hyperactivity disorder. Journal of Child & Adolescent Psychopharmacology, 15 (2), 160–179.CrossRefGoogle ScholarPubMed
Wu, J. Q., Szpunar, K. K., Godovich, S. A., Schacter, D. L., & Hofmann, S. G. (2015). Episodic future thinking in generalized anxiety disorder. Journal of Anxiety Disorders, 36, 1–8.Google Scholar
Yeung, M., Treit, D., & Dickson, C. T. (2012). A critical test of the hippocampal theta model of anxiolytic drug action. Neuropharmacology, 62 (1), 155–160.Google Scholar
Zick, J. L., Blackman, R. K., Crowe, D. A., et al. (2018). Blocking NMDAR disrupts spike timing and decouples monkey prefrontal circuits: implications for activity-dependent disconnection in schizophrenia. Neuron, 98 (6), 1243–1255.Google Scholar

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