The role of semantic features in word production

According to Levelt's language production model, to name an object, speakers must first conceptualize and lexicalize the object before it can be named. Conceptualization is conducted through the semantic network, with concepts activating lexical items at the lemma level, i.e., lexicalization. So far, research has focused on the roles of semantic categories (i.e., semantic category interference) and single semantic features (i.e., semantic feature interference) but less so on the number of overlapping features. To investigate the role of the number of overlapping features in language production, we conducted a picture-word interference study in Mandarin Chinese, varying the semantic category and shape congruency whilst controlling for classifier congruency. We also recorded behavioural and electrophysiological responses. We observed a main effect of semantic category that was stronger than a main effect of shape (i.e., a semantic feature). That is, the reduction in naming accuracies, the increase in naming latencies, and the increase in ERP amplitudes between 275 - 575 ms post-stimulus onset (N400 effect) for congruent vs. incongruent conditions were larger for semantic category than for shape. In addition, we found an interaction effect between semantic category and the semantic feature 'shape' regarding naming accuracies and also at the electrophysiological level. We conclude that, with increasing feature overlap between word pairs, there may be more spreading of activation between such pairs in word production.

Authors

Institutions

Publication Details

Journal
Brain Research
Published
2025-11-22
DOI
https://doi.org/10.1016/j.brainres.2025.150065
Primary Topic
Neurobiology of Language and Bilingualism
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The role of semantic features in word production

Jurriaan Witteman, Niels Olaf Schiller, Yufang Wang
Brain Research
Neurobiology of Language and Bilingualism
article

The role of semantic features in word production

Jurriaan Witteman, Niels Olaf Schiller, Yufang Wang
article en

Abstract

According to Levelt's language production model, to name an object, speakers must first conceptualize and lexicalize the object before it can be named. Conceptualization is conducted through the semantic network, with concepts activating lexical items at the lemma level, i.e., lexicalization. So far, research has focused on the roles of semantic categories (i.e., semantic category interference) and single semantic features (i.e., semantic feature interference) but less so on the number of overlapping features. To investigate the role of the number of overlapping features in language production, we conducted a picture-word interference study in Mandarin Chinese, varying the semantic category and shape congruency whilst controlling for classifier congruency. We also recorded behavioural and electrophysiological responses. We observed a main effect of semantic category that was stronger than a main effect of shape (i.e., a semantic feature). That is, the reduction in naming accuracies, the increase in naming latencies, and the increase in ERP amplitudes between 275 - 575 ms post-stimulus onset (N400 effect) for congruent vs. incongruent conditions were larger for semantic category than for shape. In addition, we found an interaction effect between semantic category and the semantic feature 'shape' regarding naming accuracies and also at the electrophysiological level. We conclude that, with increasing feature overlap between word pairs, there may be more spreading of activation between such pairs in word production.

Brain ResearchVol. 1871
Leiden University (NL), City University of Hong Kong (HK)
City University of Hong Kong, China Scholarship Council
Openalex Percentile: Top 63%
Neurobiology of Language and Bilingualism
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.