Lexical-semantic processing of action verbs and non-action nouns in Persian speakers: Behavioral evidence from the semantic similarity judgment task

Abstract

The processing of sensory-motor aspect of word's meaning, and its difference between nouns and verbs, is the main topic of neurolinguistic research. The present study aimed to examine the lexical-semantic processing of Persian non-action nouns and action verbs. The possible effects of semantic correlates on noun/verb dissociation were evaluated without morphological confound. A total of 62 neurologically intact Persian speakers responded to a computerized semantic similarity judgment task, including 34 triplets of non-action nouns and 34 triplets of action verbs by pressing a key. Response Time (RT) and percentage error were considered as indirect measures of lexical-semantic encoding efficiency. We also assessed the latency of hand movement execution with no linguistic demand. The results showed that action verbs elicited more errors and had slower RT compared with object nouns. Mixed ANOVA revealed that the observed noun/verb distinction was not affected by demographic factors. These results provided evidence that the lexical-semantic encoding of Persian action verbs, compared to non-action nouns, requires more support from cognitive sources‏ ‏during the processing of the motor‏-‏related semantic feature. The possible accounts for the different processing of action verbs in terms of semantic view are suggested.

Keywords:

• Action
• nouns
• object
• Persian
• semantic processing
• verbs

Acknowledgments

This paper was part of a PhD dissertation of the first author in speech-language pathology at the University of Social Welfare and Rehabilitation Sciences, Tehran. The authors would like to thank all students who participated in this study and the faculty members of the Speech Therapy Department of Babol University of Medical Sciences for their generous assistance in the sampling procedure. We also appreciate Dr. Gholami for his invaluable suggestions on data analysis.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Python is an interpretive, multi-purpose, high-level, dynamic, and object-oriented programming language used for producing and developing various software programs and technologies. This language was developed in the Netherlands by Guido van Rossum in 1991.

Additional information

Funding

This paper received a grant from the University of Social Welfare and Rehabilitation Sciences, Tehran [Grant code: IR.USWR.REC.1397.170].