Evidence map›Paper›PMID 37901072›Full record

ArticleFrontiers in psychology2023

Comparison of category and letter fluency tasks through automated analysis.

Carmen Gonzalez-Recober, Naomi Nevler, Sanjana Shellikeri, Katheryn A Q Cousins, Emma Rhodes, Mark Liberman, Murray Grossman, David Irwin, Sunghye Cho

Abstract read
In one paragraph

Article in Frontiers in psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Rapid Lexical Retrieval and Executive Functioning in Very Mild Aphasia.American journal of speech-language pathology · 2026
    Article
  5. Article
  6. Article
  7. Developing digital health technologies for frontotemporal degeneration.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Review
  8. Article
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

9 authors.

Carmen Gonzalez-RecoberPenn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, United States.
Naomi NevlerPenn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, United States.
Sanjana ShellikeriPenn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, United States.
Katheryn A Q CousinsPenn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, United States.
Emma RhodesPenn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, United States.
Mark LibermanLinguistic Data Consortium, University of Pennsylvania, Philadelphia, PA, United States.
Murray GrossmanPenn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, United States.
David IrwinPenn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, United States.
Sunghye ChoLinguistic Data Consortium, University of Pennsylvania, Philadelphia, PA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Category and letter fluency tasks are commonly used neuropsychological tasks to evaluate lexical retrieval. Methods: This study used validated automated methods, which allow for more expansive investigation, to analyze speech production of both category ("Animal") and letter ("F") fluency tasks produced by healthy participants ( Results: Participants produced significantly more words on the category fluency task relative to the letter fluency task ( Discussion: These findings showed that certain lexical measures such as AoA, word familiarity, and semantic ambiguity were important for understanding how these tasks differ. Additionally, it found that acoustic measures such as inter-word RT and word duration are also imperative to analyze when comparing the two tasks. By implementing these automated techniques, which are reproducible and scalable, to analyze fluency tasks we were able to quickly detect these differences. In future clinical settings, we expect these methods to expand our knowledge on speech feature differences that impact not only total scores, but many other speech measures among clinical populations.

Indexed as

automated analysiscategory fluency taskdigital speechletter fluency taskverbal fluency task

Identifiers

PMID37901072
PMCPMC10600440

What OpenQuestion holds

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Registered trials

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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.