ArticleSchizophrenia bulletin2024
Clarifying Cognitive Control Deficits in Psychosis via Drift Diffusion Modeling and Attractor Dynamics.
Article in Schizophrenia bulletin, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- The selective dynamics of proactive and reactive control in GAD: A taVNS study using hierarchical drift diffusion modeling.Cognitive, affective & behavioral neuroscience · 2026Article
- Longitudinal analysis of decision-making deficits in binge-eating disorders using drift diffusion modeling.Appetite · 2026Article
- Model-based analysis of stop-signal data reveals robust neural and clinical correlates of evidence accumulation but not inhibition.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Cognitive Mechanisms of Aberrant Self-Referential Social Perception in Psychosis and Bipolar Disorder: Insights From Computational Modeling.Schizophrenia bulletin · 2026Article
- Multivariate Decoding and Drift-Diffusion Modeling Reveal Adaptive Control in Trilingual Comprehension.Brain sciences · 2025Article
- Relationships between cognition and symptoms across mood and psychotic disorders.Psychiatry research · 2025Article
- Personalized Cognitive Health in Psychiatry: Current State and the Promise of Computational Methods.Schizophrenia bulletin · 2024Review
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5 authors at 1 institution in 1 country.
Funding
Abstract
background and hypothesisCognitive control deficits are prominent in individuals with psychotic psychopathology. Studies providing evidence for deficits in proactive control generally examine average performance and not variation across trials for individuals-potentially obscuring detection of essential contributors to cognitive control. Here, we leverage intertrial variability through drift-diffusion models (DDMs) aiming to identify key contributors to cognitive control deficits in psychosis. STUDY
designPeople with psychosis (PwP; N = 122), their first-degree biological relatives (N = 78), and controls (N = 50) each completed 120 trials of the dot pattern expectancy (DPX) cognitive control task. We fit full hierarchical DDMs to response and reaction time (RT) data for individual trials and then used classification models to compare the DDM parameters with conventional measures of proactive and reactive control. STUDY
resultsPwP demonstrated slower drift rates on proactive control trials suggesting less efficient use of cue information. Both PwP and relatives showed protracted nondecision times to infrequent trial sequences suggesting slowed perceptual processing. Classification analyses indicated that DDM parameters differentiated between the groups better than conventional measures and identified drift rates during proactive control, nondecision time during reactive control, and cue bias as most important. DDM parameters were associated with real-world functioning and schizotypal traits.
conclusionsModeling of trial-level data revealed that slow evidence accumulation and longer preparatory periods are the strongest contributors to cognitive control deficits in psychotic psychopathology. This pattern of atypical responding during the DPX is consistent with shallow basins in attractor dynamic models that reflect difficulties in maintaining state representations, possibly mediated by excess neural excitation or poor connectivity.
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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.