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Cognitive Control in Schizophrenia: Advances in Computational Approaches
Deanna M Barch1,2,3, Adam J Culbreth4, Julia M Sheffield5
1Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130.
Current Directions in Psychological Science
|February 19, 2024
Summary
Computational psychiatry advances understanding of cognitive control deficits in schizophrenia using neurocomputational modeling. This approach offers insights into effort, motivation, and future research directions for mental health conditions.
Area of Science:
- Computational psychiatry
- Cognitive neuroscience
- Neurocomputational modeling
Background:
- Psychiatric research is evolving with computational psychiatry, integrating cognitive neuroscience and neurocomputational modeling.
- Cognitive control deficits are a key feature in schizophrenia, necessitating advanced modeling approaches for deeper understanding.
Purpose of the Study:
- To review formal modeling approaches for understanding cognitive control deficits in psychopathology, using schizophrenia as a case study.
- To illustrate the advantages of connectionist and mathematical models in explaining cognitive control deficits and the roles of effort and motivation.
Main Methods:
- Selective review of formal modeling approaches, including biologically plausible connectionist-level models.
- Analysis of mathematical models generating parameter estimates for psychological or neural processes.
- Focus on proactive cognitive control deficits in schizophrenia.
Main Results:
- Formal models offer advantages in understanding cognitive control deficits in schizophrenia.
- These models shed light on the potential roles of effort and motivation in these deficits.
Conclusions:
- Future directions include establishing psychometric properties and modeling psychotic symptoms' interaction with cognitive control.
- Expansion of behavioral and neural modeling to diverse mental health conditions is crucial for examining dissociable substrates of cognitive impairments.
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