Book contents
- Cambridge Textbook Of Neuroscience for Psychiatrists
- Reviews
- Cambridge Textbook of Neuroscience for Psychiatrists
- Copyright page
- Contents
- Contributors
- Introduction
- 1 Cells
- 2 Neurotransmitters and Receptors
- 3 Basic Techniques in Neuroscience
- 4 Neuroanatomy
- 5 Neural Circuits
- 5.1 Appetite
- 5.2 Sleep
- 5.3 Sex and Sex Hormones
- 5.4 Violence and Aggression
- 5.5 Nociception and Pain
- 5.6 The Motor System and Movement Disorders
- 5.7 Computational Models of Learning
- 5.8 Habit Formation
- 5.9 Reward, Pleasure and Motivation
- 5.10 Emotion
- 5.11 Perception
- 5.12 Attention
- 5.13 Apathy, Anhedonia and Fatigue
- 5.14 Memory
- 5.15 Fronto-Executive Functions
- 5.16 Empathy and Theory of Mind
- 5.17 Language
- 5.18 Brain Networks and Dysconnectivity
- 6 Modulators
- 7 Genetics
- 8 Neurodevelopment and Neuroplasticity
- 9 Integrated Neurobiology of Specific Syndromes and Treatments
- 10 Neurodegeneration
- Index
- References
5.7 - Computational Models of Learning
from 5 - Neural Circuits
Published online by Cambridge University Press: 08 November 2023
Book contents
- Cambridge Textbook Of Neuroscience for Psychiatrists
- Reviews
- Cambridge Textbook of Neuroscience for Psychiatrists
- Copyright page
- Contents
- Contributors
- Introduction
- 1 Cells
- 2 Neurotransmitters and Receptors
- 3 Basic Techniques in Neuroscience
- 4 Neuroanatomy
- 5 Neural Circuits
- 5.1 Appetite
- 5.2 Sleep
- 5.3 Sex and Sex Hormones
- 5.4 Violence and Aggression
- 5.5 Nociception and Pain
- 5.6 The Motor System and Movement Disorders
- 5.7 Computational Models of Learning
- 5.8 Habit Formation
- 5.9 Reward, Pleasure and Motivation
- 5.10 Emotion
- 5.11 Perception
- 5.12 Attention
- 5.13 Apathy, Anhedonia and Fatigue
- 5.14 Memory
- 5.15 Fronto-Executive Functions
- 5.16 Empathy and Theory of Mind
- 5.17 Language
- 5.18 Brain Networks and Dysconnectivity
- 6 Modulators
- 7 Genetics
- 8 Neurodevelopment and Neuroplasticity
- 9 Integrated Neurobiology of Specific Syndromes and Treatments
- 10 Neurodegeneration
- Index
- References
Summary
The advent of neuroimaging techniques has driven advances in how we understand where in the brain different aspects of cognition are instantiated, and how this neural activity relates to behaviour. While the translation of this approach to study neuropsychiatric disorders has had some successes, it could be argued that it fails to capture what the brain is doing. Computational models serve as a bridge from brain to behaviour (see Figure 5.7.1), permitting the formulation of mechanistic hypotheses about neural computations and how they might be different in clinical conditions. Most applications of computational models to psychiatric disorders concern altered learning about the world. While many formal models of learning exist, two have had widespread success in their application to psychiatry: reinforcement learning and Bayesian models. Both models are concerned with how we learn from past experiences to form expectations about the world around us.
- Type
- Chapter
- Information
- Cambridge Textbook of Neuroscience for Psychiatrists , pp. 184 - 191Publisher: Cambridge University PressPrint publication year: 2023
References
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed. (DSM-5). American Psychiatric Association, 2013.Google Scholar
Pavlov, PI. Conditioned reflexes: an investigation of the physiological activity of the cerebral cortex. Ann Neurosci 2010; 17(3): 136–141.CrossRefGoogle ScholarPubMed
Thorndike, EL. Animal intelligence: an experimental study of the associative processes in animals. Psychol Rev: Monogr Suppl 1898; 2(4): i–190.Google Scholar
Rescorla, RA, Wagner, AR. A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement. In Classical Conditioning II: Current Research and Theory. Appleton-Century-Crofts, 1972, pp. 64–99.Google Scholar
Schultz, W, Dayan, P, Montague, PR. A neural substrate of prediction and reward. Science 1997; 275(5306): 1593–1599.CrossRefGoogle ScholarPubMed
Kumar, P, Waiter, G, Ahearn, T et al. Abnormal temporal difference reward-learning signals in major depression. Brain 2008; 131(8): 2084–93.CrossRefGoogle Scholar
Lawson, RP, Seymour, B, Loh, E et al. The habenula encodes negative motivational value associated with primary punishment in humans. Proc Natl Acad Sci USA 2014; 111(32): 11858–11863.CrossRefGoogle ScholarPubMed
Lawson, RP, Nord, CL, Seymour, B et al. Disrupted habenula function in major depression. Mol Psychiatry 2017; 22: 202–208.CrossRefGoogle ScholarPubMed
O’Doherty, JP, Dayan, P, Friston, K, Critchley, H, Dolan, RJ. Temporal difference models and reward-related learning in the human brain. Neuron 2003; 38(2): 329–337.CrossRefGoogle ScholarPubMed
Daw, ND, Niv, Y, Dayan, P. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neurosci 2005; 8(12): 1704–1711.CrossRefGoogle ScholarPubMed
Smittenaar, P, FitzGerald, TH, Romei, V, Wright, ND, Dolan, RJ. Disruption of dorsolateral prefrontal cortex decreases model-based in favor of model-free control in humans. Neuron 2013; 80(4): 914–919.CrossRefGoogle ScholarPubMed
Voon, V, Derbyshire, K, Rück, C et al. Disorders of compulsivity: a common bias towards learning habits. Mol Psychiatry 2015; 20(3): 345–352.CrossRefGoogle ScholarPubMed
Gillan, CM, Kosinski, M, Whelan, R, Phelps, EA, Daw, ND. Characterizing a psychiatric symptom dimension related to deficits in goal-directed control. eLife 2016; 5: e11305.CrossRefGoogle ScholarPubMed
Nord, CL, Popa, T, Smith, E et al. The effect of frontoparietal paired associative stimulation on decision-making and working memory. Cortex 2019; 117: 266–276.CrossRefGoogle ScholarPubMed
Bromberg-Martin, ES, Matsumoto, M, Hikosaka, O. Distinct tonic and phasic anticipatory activity in lateral habenula and dopamine neurons. Neuron 2010; 67(1): 144–155.CrossRefGoogle ScholarPubMed
Yang, Y, Wang, H, Hu, J, Hu, H. Lateral habenula in the pathophysiology of depression. Curr Opin Neurobiol 2018; 48: 90–96.CrossRefGoogle Scholar
Gold, JM, Waltz, JA, Matveeva, TM et al. Negative symptoms and the failure to represent the expected reward value of actions: behavioral and computational modeling evidence. Arch Gen Psychiatry 2012; 69(2): 129–138.CrossRefGoogle ScholarPubMed
Valton, V, Romaniuk, L, Douglas Steele, J, Lawrie, S, Seriès, P. Comprehensive review: computational modelling of schizophrenia. Neurosci Biobehav Rev 2017; 83: 631–646.CrossRefGoogle ScholarPubMed
Dima, D, Roiser, JP, Dietrich, DE et al. Understanding why patients with schizophrenia do not perceive the hollow-mask illusion using dynamic causal modelling. Neuroimage 2009; 46(4): 1180–1186.CrossRefGoogle Scholar
Browning, M, Behrens, TE, Jocham, G, O’Reilly, JX, Bishop, SJ. Anxious individuals have difficulty learning the causal statistics of aversive environments. Nature Neurosci 2015; 18(4): 590–596.CrossRefGoogle ScholarPubMed
Lawson, RP, Mathys, C, Rees, G. Adults with autism overestimate the volatility of the sensory environment. Nature Neurosci 2017; 20(9): 1293.CrossRefGoogle ScholarPubMed
Powers, AR, Mathys, C, Corlett, PR. Pavlovian conditioning: induced hallucinations result from overweighting of perceptual priors. Science 2017; 357(6351): 596–600.CrossRefGoogle ScholarPubMed
Corlett, PR, Horga, G, Fletcher, PC et al. Hallucinations and strong priors. Trends Cogn Sci 2019; 23(2): 114–127.CrossRefGoogle ScholarPubMed
Valton, V, Karvelis, P, Richards, KL et al. Acquisition of visual priors and induced hallucinations in chronic schizophrenia. Brain 2019; 142(8): 2523–2537.CrossRefGoogle ScholarPubMed
Sandhu, TR, Xiao, B, Lawson, RP. Transdiagnostic computations of uncertainty: towards a new lens on intolerance of uncertainty. Neurosci Behav Rev 2023; 148: 105123.CrossRefGoogle ScholarPubMed
Browning, M, Carter, CS, Chatham, C et al. Realizing the clinical potential of computational psychiatry: report from the Banbury Center Meeting, February 2019. Biol Psychiatry 88(2): E5–E10, https://doi.org/10.1016/j.biopsych.2019.12.026CrossRefGoogle Scholar