Luc Vandromme

The Impact of Verbal Capacity on Theory of Mind in Deaf and Hard of Hearing Children

Even when they have good language skills, many children with hearing loss lag several years behind hearing children in the ability to grasp beliefs of others. The researchers sought to determine whether this lag results from difficulty with the verbal demands of tasks or from conceptual delays. The researchers related childrens performance on a nonverbal theory of mind task to their scores on verbal aptitude tests. Twelve French children (average age about 10 years) with severe to profound hearing loss and 12 French hearing children (average about 7 years) were evaluated. The children with hearing loss showed persistent difficulty with theory of mind tasks, even a nonverbal task, presenting results similar to those of hearing 6-year-olds. Also, the children with hearing loss showed a correlation between language level (lexical and morphosyntactic) and understanding of false beliefs. No such correlation was found in the hearing children.

L’attention conjointe, quarante ans d’évaluations et de recherches de modélisations

L’attention conjointe est le fait de partager ensemble un interet commun pour un objet. Avec l’etude princeps de Scaife et Bruner en 1975, elle est envisagee comme le premier pas vers la cognition sociale. Depuis la naissance, avec la detection du regard jusqu’a la comprehension de l’intentionnalite d’autrui a la fin de la periode pre verbale, cet article propose une revue de litterature sur les paradigmes experimentaux et les modeles qui ont ete elabores pour en rendre compte.

Automatic Autism Spectrum Disorder Detection Thanks to Eye-Tracking and Neural Network-Based Approach

Autism spectrum disorder (ASD) is a neurodevelopmental disorder quite wide and its numerous variations render diagnosis hard. Some works have proven that children suffering from autism have trouble keeping their attention and tend to have a less focused sight. On top of that, eye-tracking systems enable the recording of precise eye focus on a screen. This paper deals with automatic detection of autism spectrum disorder thanks to eye-tracked data and an original Machine Learning approach. Focusing on data that describes the saccades of the patient’s sight, we distinguish, out of our six test patients, young autistic individuals from those with no problems in 83% (five) of tested patients, with a results confidence up to 95%.