Excessive attractor instability accounts for semantic priming in schizophrenia

Area of Science:

• Cognitive Neuroscience
• Computational Psychiatry
• Neuroscience

Background:

• Schizophrenia is associated with thought disorders, characterized by peculiar semantic priming patterns.
• Semantic priming involves faster word recognition when preceded by related words, a process altered in schizophrenia.

Purpose of the Study:

• To investigate the neural mechanisms underlying altered semantic priming in schizophrenia using a computational model.
• To explain the specific patterns of reduced long-SOA and augmented short-SOA priming observed in patients.

Main Methods:

• Developed a novel attractor neural network model with synaptic adaptation.
• Introduced attractor instability into the model, simulating dysfunctional synaptic transmission in schizophrenia.
• Conducted two simulated experiments to test the model's predictions.

Main Results:

• The model successfully replicated the full spectrum of semantic priming effects seen in schizophrenia patients.
• Attractor instability in the model led to faster network dynamics, explaining observed priming patterns.
• The model accounted for inconsistencies in short-SOA priming and the consistency of indirect priming.

Conclusions:

• Attractor instability, stemming from dysfunctional synaptic transmission, is a plausible mechanism for altered semantic priming in schizophrenia.
• The model provides a framework for understanding other schizophrenia symptoms like derailment and context utilization difficulties.
• This computational approach offers insights into the overly-broad spreading activation hypothesized in schizophrenic thought disorders.