ReviewFrontiers in human neuroscience2018
Computational Neuropsychology and Bayesian Inference.
Review in Frontiers in human neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
73 citing papers in PubMed.
- The Computational, Pharmacological, and Physiological Determinants of Sensory Learning under Uncertainty.Current biology : CB · 2021Trial
- Deep learning reveals a neurocomputational mechanism predicting depression risk in adolescents.Science advances · 2026Article
- Limb apraxia and active inference in the visuomotor pathways.Psychonomic bulletin & review · 2026Review
- The phantastic organ: predictive processing in the brain.Frontiers in psychology · 2026Review
- Compensatory hallucinogenesis across three neuropsychiatric disorders: a Bayesian account.Brain communications · 2026Review
- Digital Twin Brain: Generating Multitask Behavior from Connectomes for Personalized Therapy.BME frontiers · 2026Article
- The rise and deceleration of neuronal excitability in aging and Alzheimer's disease: Mechanisms, implications, and therapeutic targets.Ageing research reviews · 2026Review
- Contextual auditory processing in the inferior colliculus is affected in a sex- and age-dependent manner in the valproic acid-induced rat model of autism.PLoS biology · 2025Article
- Computational markers show specific deficits for dyslexia and ADHD in complex learning settings.NPJ science of learning · 2025Article
- Electrocardiography-derived autonomic profiles in depression and suicide risk with insights from the UK Biobank.Npj mental health research · 2025Article
- Emergent Aspects of the Integration of Sensory and Motor Functions.Brain sciences · 2025Review
- The power of the group: How neuroscience supports expanding the therapeutic dyad through group psychotherapy.Frontiers in psychology · 2025Article
- Precision neuropsychology in the area of AI.Frontiers in psychology · 2025Article
- The Many Roles of Precision in Action.Entropy (Basel, Switzerland) · 2024Review
- Multisensory perceptual and causal inference is largely preserved in medicated post-acute individuals with schizophrenia.PLoS biology · 2024Article
- When the interoceptive and conceptual clash: The case of oppositional phenomenal self-modelling in Tourette syndrome.Cognitive, affective & behavioral neuroscience · 2024Review
- A model of time-varying music engagement.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Visual mismatch negativity in Parkinson's psychosis and potential for testing treatment mechanisms.Brain communications · 2024Article
- The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Digital Being: social media and the predictive mind.Neuroscience of consciousness · 2024Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine 'prior' beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology - optimal inference with suboptimal priors - and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient's behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.