# Visual feature analysis on selective appetite in individuals with autism spectrum disorders

## TL;DR

This study explores how visual features of sunny-side-up eggs influence food preferences in individuals with autism spectrum disorders compared to neurotypical individuals.

## Contribution

The study introduces a systematic analysis of visual features to understand selective eating behaviors in individuals with ASD.

## Key findings

- Factors influencing visual preferences for sunny-side-up eggs were identified using Non-negative Matrix Factorization and Decision Tree analysis.
- Linear regression analysis revealed distinct preference boundaries between liked and disliked foods in ASD and TD groups.
- The findings suggest potential to predict food preferences and address selective eating behaviors in individuals with ASD.

## Abstract

Individuals with autism spectrum disorders (ASD) experience more severe selective eating problems than their neurotypical peers. Identifying the causes of selective eating behavior poses a considerable challenge, even for caregivers. Accurate identification of the underlying causes of this behavior is essential for developing interventions aimed at overcoming dysfunctional, unbalanced diets. However, studies that meticulously identify the causes of selective eating behaviors are scarce. This investigation aims to explore the differences in preferences for sunny-side-up eggs between individuals with ASD and those with typical development (TD), focusing on the factors influencing their likes and dislikes through a systematic analysis of visual features.

Thirty-nine individuals with ASD (mean age, 23.4 ± 4.7 years; 82% men) and fifty individuals with TD (mean age, 22.2 ± 1.3 years; 64% men) participated in this study. We used a total of 50 images of sunny-side-up eggs as visual stimuli. Using Non-negative Matrix Factorization and Decision Tree analysis, factors associated with visual preferences for sunny-side-up eggs were identified.

We could identify factors associated with visual preferences for sunny-side-up eggs. Subsequent linear regression analysis provided insight into how these visual features delineate preference boundaries between liked and disliked foods, with noteworthy distinctions emerging between the ASD and TD groups.

This study provides novel insights into the visual determinants of food preferences in individuals with ASD through systematic analysis of image features. Our findings indicated the potential to predict preferences while elucidating the causes of selective eating behaviors, thereby offering solutions for individuals with ASD.