https://www.nature.com/articles/s44220-025-00427-1
challenging paper for computational psychiatry, but it's luckily not just a black/white picture with possibly state being better captured by behavioral tasks
"Questionnaire-based measures can provide little insights on cognitive mechanisms as they rely only on verbal and mnesic processes. By contrast, behavioral measures and computationally derived parameters
may go beyond explicit processes and capture more accurately states than traits, which may be useful in informing therapeutic strategies in the short run (for example, capturing state impulsivity that may help to adapt a psychotropic treatment when facing a patient with suicidal ideation). Altogether, our results represent a cautionary tale about the utilization of behavioral tasks and model parameters as tools for investigating inter-individual differences along therapeutically or diagnostically relevant time windows, and about the challenges facing computational phenotyping for diagnosis and prognosis."
#computationalPsychiatry #neuroscience #testRetestReliability #behavior #questionnaires #psychology #neuroscience
Reinforcement learning task-based behavioral and computational measures displayed low test–retest reliability at the individual level. Also in contrast to self-assessed personality measures, behavioral and computational measures were poor predictors of mental health measures, representing a challenge for computational psychiatry.
Computational Models in Psychiatry: Simulating the Mind’s Intricate Processes
#ComputationalPsychiatry #Neuroscience #SocialScience #Genetics #Epigenetics #MachineLearning #MentalHealth #BrainScience #StressResearch #NeuralNetworks #SocialBehavior #Immunology #FutureOfScience #MindAndBody
Quantifying Cognitive Abilities: Numerosity and Evidence Accumulation in Brain Models
#ComputationalPsychiatry #BrainScience #MentalHealthResearch #Neuroscience #CognitiveScience #DecisionMaking #ReinforcementLearning #MentalHealth #BrainModels #Psychology #NeuralSignals #BehavioralScience #ScientificDiscovery
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You could also look at our 2008 "addiction is a symptom not a disease" manifesto. It makes the case that the way to understand #addiction is as breakdowns (vulnerabilities) in the #decision systems. (It was an important early paper in the #ComputationalPsychiatry field.) It shows the importance of looking at all of these as decisions.
A. D. Redish, S. Jensen, A. Johnson (2008) “A unified framework for addiction: vulnerabilities in the decision process” Behavioral and Brain Sciences 31:415-437 with discussion pp. 437-487
Also available here:
http://redishlab.neuroscience.umn.edu/Papers/2008%20Redish-Jensen-Johnson%20BBS%20Addiction%20Vulnerabiltiies.pdf
New paper published: new study of decision making and psychosis. Drift diffusion models, attractor dynamics, and patients with psychosis doing the dot pattern expectancy task. Fascinating #computationalpsychiatry work by the #NeuroPRSMH team.
https://academic.oup.com/schizophreniabulletin/advance-article/doi/10.1093/schbul/sbae014/7614300
ok so while I wait for answers:
1) https://yewtu.be/playlist?list=PLHyI3Fbmv0SeKCVD3RTQwGvS7_4efyoh7
are there good books to have an overview of the field ?
I was on the #Brainland podcast with Ken Barrett talking about #ComputationalPsychiatry. It was fun.
https://shows.acast.com/brainland/episodes/computational-psychiatry-is-in-the-building
In conversation with A David Redish.
Ed Bull
Nicolas Clairis
CITO Greenhouse
Alicia Izquierdo, Ph.D.
Redish Lab
jnpn