Evidence map›Paper›PMID 40478818›Full record

ArticlePloS one2025

Examining Chat GPT with nonwords and machine psycholinguistic techniques.

Michael S Vitevitch

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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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

2 citing papers in PubMed.

  1. Article
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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

1 author.

Michael S VitevitchSpoken Language Laboratory, Department of Speech-Language-Hearing, Sciences & Disorders, University of Kansas, Lawrence, Kansas, USA.ORCID 0000-0002-1209-0838

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Strings of letters or sounds that lack meaning (i.e., nonwords) have been used in cognitive psychology and psycholinguistics to provide foundational knowledge of human processing and representation, and insights into language-related performance. The present set of studies used the machine psycholinguistic approach (i.e., using nonword stimuli and tasks similar to those used with humans) to gain insight into the performance of Chat GPT in comparison to human performance. In Study 1, Chat GPT was able to provide correct definitions to many extinct words (i.e., real English words that are no longer used). In Study 2 the nonwords were real words in Spanish, and Chat GPT was prompted to provide a word that sounded similar to the nonword. Responses tended to be Spanish words unless the prompt specified that the similar sounding word should be an English word. In Study 3 Chat GPT provided subjective ratings of wordlikeness (and buyability) that correlated with ratings provided by humans, and with the phonotactic probabilities of the nonwords. In Study 4, Chat GPT was prompted to generate a new English word for a novel concept. The results of these studies highlight certain strengths and weaknesses in human and machine performance. Future work should focus on developing AI that complements or extends rather than duplicates or competes with human abilities. The machine psycholinguistic approach may help to discover additional strengths and weaknesses of human and artificial intelligences.

Indexed as

PsycholinguisticsAdultFemaleGenerative Artificial IntelligenceHumansLanguageMaleYoung Adult

Identifiers

PMID40478818
PMCPMC12143520

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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.