Background: Word comprehension across semantic categories is a key area of language development. Using online automated eye-tracking technology to reduce response demands during a word comprehension test may be advantageous in children with autism spectrum disorder (ASD).

Objectives: To measure online accuracy of word recognition across eleven semantic categories in preschool children with ASD and in typically developing (TD) children matched for gender and developmental age.

Methods: Using eye-tracker methodology we measured the relative number of fixations on a target image as compared to a foil of the same category shown simultaneously on screen. This online accuracy measure was considered a measure of word understanding. We tested the relationship between online accuracy and offline word recognition and the effects of clinical variables on online accuracy. Twenty-four children with ASD and 21 TD control children underwent the eye-tracking task.

Results: On average, children with ASD were significantly less accurate at fixating on the target image than the TD children. After multiple comparison correction, no significant differences were found across the eleven semantic categories of the experiment between preschool children with ASD and younger TD children matched for developmental age. The ASD group showed higher intragroup variability consistent with greater variation in vocabulary growth rates. Direct effects of non-verbal cognitive levels, vocabulary levels and gesture productions on online word recognition in both groups support a dimensional view of language abilities in ASD.

Conclusions: Online measures of word comprehension across different semantic categories show higher interindividual variability in children with ASD and may be useful for objectively monitor gains on targeted language interventions.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6370186PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0211802PLOS

Publication Analysis

Top Keywords

semantic categories
20
children asd
20
online accuracy
16
comprehension semantic
12
preschool children
12
word comprehension
12
word recognition
12
children
11
online
8
categories preschool
8

Similar Publications

Talent Identification: Time to Move Forward on Estimation of Potentials? Proposed Explanations and Promising Methods.

Sports Med

January 2025

IRMES-UPR 7329, Institut de Recherche Médicale et d'Épidémiologie du Sport, Université Paris Cité, 11 Avenue du Tremblay, 75012, Paris, France.

The scientific literature on talent identification is extensive, with significant advancements made over the past 30 years. However, as with any field, the translation of research into practice and its impact on the field have been slower than anticipated. Indeed, recent findings highlight a pervasive relative age effect, the effects of maturation being often overlooked, disparate populations between young and senior performers, and a necessity to embrace a holistic approach.

View Article and Find Full Text PDF

Visual semantic decoding aims to extract perceived semantic information from the visual responses of the human brain and convert it into interpretable semantic labels. Although significant progress has been made in semantic decoding across individual visual cortices, studies on the semantic decoding of the ventral and dorsal cortical visual pathways remain limited. This study proposed a graph neural network (GNN)-based semantic decoding model on a natural scene dataset (NSD) to investigate the decoding differences between the dorsal and ventral pathways in process various parts of speech, including verbs, nouns, and adjectives.

View Article and Find Full Text PDF

Expanding the concept of ID conversion in TogoID by introducing multi-semantic and label features.

J Biomed Semantics

January 2025

Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Kashiwa, Chiba, Japan.

Background: TogoID ( https://togoid.dbcls.jp/ ) is an identifier (ID) conversion service designed to link IDs across diverse categories of life science databases.

View Article and Find Full Text PDF

Cracking arbitrariness: A data-driven study of auditory iconicity in spoken English.

Psychon Bull Rev

January 2025

Department of Psychology, University of Milano - Bicocca, Piazza dell'Ateneo Nuovo 1, Milan, MI, 20126, Italy.

Auditory iconic words display a phonological profile that imitates their referents' sounds. Traditionally, those words are thought to constitute a minor portion of the auditory lexicon. In this article, we challenge this assumption by assessing the pervasiveness of onomatopoeia in the English auditory vocabulary through a novel data-driven procedure.

View Article and Find Full Text PDF

Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is proposed. Firstly, the particularity of the scene in the multi-fitting detection task is analyzed. Hence, the aggregation of the fittings is defined as the scene according to the professional knowledge of the power field and the habit of the operators in identifying the fittings.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!