Schizophrenia II: Computational models Flashcards
What characterizes the Data Driven Approach to Computational Psychiatry? Give 2 examples from the field of schizophrenia research.
- Agnostic to theory
- Machine learning
Diagnostic (data driven): 75% success if sMRI and fMRI are combined
Treatment success (data driven): 80% percent success
What characterizes the Theory Driven Approach to Computational Psychiatry?
- Based on conceptual models + prior evidence
- Formal math. Models of biological/mental processes
- Synthesizing different levels of evidence and explanation
What are the 3 types of theory driven models?
- Synthetic (biophysical)
- Algorythmic
- Optimal (Bayesian)
Give an example of how Syntehtic Models are used in schiz. research.
Example: NMDA receptor Antagonists like PCP and Ketamine = psychotic symptoms –> simulate psychosis
Finding: damped lateral inhibition = mediating factor between psychotic symptoms and biophysical causes
Give an example of the use of algorythmic models in schizo. research.
Example: Schizophr. = hard to learn from positive outcomes (i.e. problem With reward based learning): could be: cant judge positive to be so (WM in OFC), or don’t learn from it (Dopamine).
Reinforcement learning used to model: actor- critic and Q learning
finding: negative systems: not related to reduced learning from positive outcomes as such; but with failure to represent positive value
High negative symptoms -> Actor critique model fit
What aspect of Schizophrenia can be explained using optimal (Bayesian) models? Explain the Framework.
abnormality in brains (Bayesian) inference mechanism leading to failure to integrate new evidence leading to false predictions.
Predictive Coding = decreased precision of prior beliefs, increased precision of sensory data =increase in prediction error
Hallucinations = shift away from prior beliefs and toward sensory evidence = Reduced prior-to-likelihood ratio.
