A new study published in Translational Psychiatry is offering a computational explanation for why people with schizophrenia and major depressive disorder can struggle with tasks that require them to ignore distractions, stop an automatic response, or rapidly adjust their behavior. The research, led by T. Zhang, S. Wang, H. Shi and colleagues, examines the mental machinery behind interference control and response inhibition—two cognitive functions that are essential for decision-making, emotional regulation, and everyday goal-directed behavior.
Interference control allows the brain to focus on relevant information while suppressing competing signals. Response inhibition, meanwhile, enables a person to cancel or withhold an action that has already been prepared. These processes are often tested using laboratory tasks in which participants must respond quickly to one type of stimulus while ignoring another, or stop a response when a sudden signal appears. Although difficulties in both areas have been documented in schizophrenia and major depressive disorder, the new work asks a more precise question: do the two conditions disrupt the same computational processes, or do they produce distinct forms of impairment?
That distinction is important because schizophrenia and major depressive disorder are clinically different illnesses, yet they can share symptoms such as slowed thinking, poor concentration, reduced cognitive flexibility, and difficulty controlling behavior. Traditional neuropsychological scores can reveal that performance is impaired, but they often cannot explain why. A person may respond more slowly because the brain is processing information less efficiently, because decision thresholds have changed, because irrelevant signals are exerting too much influence, or because the individual is adopting a cautious response strategy. Computational modeling is designed to separate these possibilities.
Instead of treating a participant’s response time and accuracy as simple measurements, computational approaches use mathematical models to estimate hidden stages of information processing. These models can distinguish the speed at which evidence is accumulated, the amount of evidence required before committing to a response, the ability to suppress competing information, and the time consumed by processes outside the decision itself. By applying such models to interference and inhibition tasks, the researchers investigated whether schizophrenia and depression share a common computational signature while also displaying disorder-specific patterns.
The study’s central contribution is its focus on both convergence and divergence. Shared mechanisms may point to cognitive vulnerabilities that cut across diagnostic boundaries, such as inefficient evidence accumulation or weakened control over competing responses. Disorder-specific mechanisms, by contrast, may help explain why similar behavioral symptoms arise from different underlying neural and psychological processes. In schizophrenia, abnormalities in executive control may be linked to broader disruptions in selecting and updating relevant information. In major depressive disorder, altered response control may interact with slowed cognition, reduced motivation, negative attention biases, or difficulty allocating mental effort.
This framework could help resolve a long-standing puzzle in psychiatric research: why patients with different diagnoses often perform similarly on the same cognitive task, even though their clinical experiences are not identical. A shared behavioral outcome does not necessarily mean that the brain arrived there through the same route. One individual may fail to stop a response because distracting information is overpowering the decision process, while another may hesitate because the internal threshold for action has become unusually high. Computational analysis can reveal these hidden differences behind an apparently identical test score.
The findings are also relevant to the search for more precise psychiatric treatments. Most current diagnoses are based primarily on reported symptoms and observable behavior rather than on measurable biological mechanisms. If computational parameters can reliably identify distinct forms of inhibitory or interference-control dysfunction, they could eventually support more individualized interventions. For example, patients with inefficient information processing might benefit from cognitive training or treatments aimed at improving attentional control, while those with excessive response caution or motivational disengagement might require a different therapeutic strategy.
The research may also influence how scientists design future studies of brain function. Interference and response inhibition are frequently described as broad “executive functions,” but that label can conceal several separable operations. A computational account encourages researchers to connect behavior with specific neural systems, including frontoparietal networks involved in cognitive control, basal ganglia circuits involved in action selection, and neuromodulatory systems that regulate learning and response thresholds. Combining these models with neuroimaging, electrophysiology, or pharmacological studies could clarify how shared and disorder-specific mechanisms are implemented in the brain.
At the same time, computational findings should not be interpreted as a complete explanation of either disorder. Schizophrenia and major depressive disorder are highly heterogeneous, and the mechanisms identified at the group level may not apply equally to every individual. Cognitive performance can also be influenced by medication, illness duration, symptom severity, sleep, motivation, and general health. Nevertheless, the study provides a valuable step toward moving psychiatric neuroscience beyond broad descriptions of impairment. By asking how decisions fail—not merely whether they fail—the work points toward a more detailed science of mental illness, in which apparently similar symptoms can be traced to distinct disruptions in the brain’s computational control systems.
Subject of Research: Shared and disorder-specific computational mechanisms of interference control and response inhibition in schizophrenia and major depressive disorder
Article Title: Shared and disorder-specific computational mechanisms of interference and response inhibition in schizophrenia and major depressive disorder
Article References: Zhang, T., Wang, S., Shi, H. et al. “Shared and disorder-specific computational mechanisms of interference and response inhibition in schizophrenia and major depressive disorder.” Translational Psychiatry (2026). https://doi.org/10.1038/s41398-026-04345-3
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s41398-026-04345-3
Keywords: schizophrenia, major depressive disorder, cognitive control, interference inhibition, response inhibition, computational psychiatry, executive function, decision-making, psychiatric neuroscience, neurocognitive mechanisms
