Evidence map›Paper›PMID 41376860›Full record

ArticleSchizophrenia research. Cognition2026

Unique signatures in verbal fluency task performance in schizophrenia and depression.

Sunghye Cho, Yan Cong, Aarush Mehta, Amir H Nikzad, Sarah A Berretta, Leily M Behbehani, Mark Liberman, Sunny X Tang

Abstract read
In one paragraph

Article in Schizophrenia research. Cognition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sunghye ChoLinguistic Data Consortium & Linguistics Department, University of Pennsylvania, Philadelphia, PA, USA.
Yan CongSchool of Languages and Cultures, Purdue University, West Lafayette, IN, USA.
Aarush MehtaZucker Hillside Hospital, Northwell Health, New Hyde Park, NY, USA.
Amir H NikzadZucker Hillside Hospital, Northwell Health, New Hyde Park, NY, USA.
Sarah A BerrettaZucker Hillside Hospital, Northwell Health, New Hyde Park, NY, USA.
Leily M BehbehaniZucker Hillside Hospital, Northwell Health, New Hyde Park, NY, USA.
Mark LibermanLinguistic Data Consortium & Linguistics Department, University of Pennsylvania, Philadelphia, PA, USA.
Sunny X TangLinguistic Data Consortium & Linguistics Department, University of Pennsylvania, Philadelphia, PA, USA.

Funding

Advancing Computational Linguistic Biomarkers of Disorganized Speech in PsychosisK23MH130750 · NIMH · FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH · PI TANG, SUNNY XIAOJING · 2022 to 2024
$586k
NIMH NIH HHS K23 MH130750
6 · The paper itself

Abstract

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 ( 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.

Indexed as

Automated speech analysisDepressionSchizophreniaSemantic knowledgeVerbal fluency

Identifiers

PMID41376860
PMCPMC12689178

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.