Unique signatures in verbal fluency task performance in schizophrenia and depression
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Updated: Jan 9, 2026

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Published on: March 11, 2021
Sunghye Cho1, Yan Cong2, Aarush Mehta3,4
1Linguistic Data Consortium & Linguistics Department, University of Pennsylvania, Philadelphia, PA, USA.
Background:
Verbal fluency tasks reveal consistent impairments among individuals with schizophrenia (SSD) and shed valuable insight on semantic and cognitive processing. However, comparisons with other psychiatric groups remain limited. Doing so may help clarify whether characteristics are unique to SSD versus shared across diagnoses. This study compared verbal fluency responses among participants with SSD, unipolar depression, and healthy volunteers (HV).
Methods:
Participants with SSD (n = 64), unipolar depression (n = 70), and HVs (n = 37) completed letter- and category-guided verbal fluency tasks. Using automated pipelines, we extracted total correct responses, pause durations, first response latency, semantic and phonemic distances, and the number and size of semantic/phonemic clusters. Associations between fluency metrics and clinical ratings of speech disturbances (e.g., incoherence, inefficiency) were examined within patient groups.
Results:
Groups differed significantly in total fluency scores, particularly on the category fluency task, with the greatest impairment in SSD and intermediate effects in depression. Significant pairwise group differences emerged in the number of semantic and phonemic clusters in the category task, while cluster size showed fewer differences. In HVs, both semantic and phonemic distances increased with longer pauses, suggesting strategic cluster switching. This pattern was absent in SSD and depression groups. SSD exhibited longer first latency and pauses relative to HVs; participants with depression showed no change in latency, but similarly prolonged pauses.
Conclusion:
Individuals with SSD and depression exhibit distinct patterns in verbal fluency, with category tasks revealing more robust group differences. Automated fluency analysis offers a scalable approach for differentially detecting cognitive-linguistic impairments across psychiatric populations.
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