Dataset Information

Affect and gaze responses during an Emotion-Evoking Task in infants at an increased likelihood for autism spectrum disorder.

ABSTRACT:

Background

The majority of research examining emotional difficulties in autism spectrum disorder (ASD) prior to age 2 relies on parent report.

Methods

We examined behavioral responses (affect and gaze) during emotionally salient tasks designed to elicit mildly positive and negative emotional states in infants. At 12 and 18 months, infants at an increased likelihood for an ASD diagnosis (IL; have an older sibling with ASD; n = 60) and low likelihood (LL; no family history of ASD; n = 21) completed the Emotion-Evoking (EE) Task and parents completed the Infant Behavior Questionnaire-Revised (IBQ-R). All children received an Autism Diagnostic Observation Scale-second Edition assessment for ASD symptomatology at 24 months.

Results

The main findings were (1) the IL group displayed higher rates of negative affect and spent less time looking at the task objects compared to the LL group, and (2) affect and gaze scores at 12 and 18 months, but not scores on the IBQ-R, predicted ASD symptoms at 24 months.

Limitations

The data were drawn from an IL sample and may not be generalizable to the general ASD population, and the children were not followed to determine a diagnosis of ASD.

Conclusion

These results suggest that behavioral responses can provide important information that complements parent reports of emotional regulation in IL infants as early as 12 months of age.

SUBMITTER: Sacrey LR

PROVIDER: S-EPMC8493694 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Several studies have shown that facial attention differs in children with autism. Measuring eye gaze and emotion recognition in children with autism is challenging, as standard clinical assessments must be delivered in clinical settings by a trained clinician. Wearable technologies may be able to bring eye gaze and emotion recognition into natural social interactions and settings. OBJECTIVE:This study aimed to test: (1) the feasibility of tracking gaze using wearable smart glasses during a facial expression recognition task and (2) the ability of these gaze-tracking data, together with facial expression recognition responses, to distinguish children with autism from neurotypical controls (NCs). METHODS:We compared the eye gaze and emotion recognition patterns of 16 children with autism spectrum disorder (ASD) and 17 children without ASD via wearable smart glasses fitted with a custom eye tracker. Children identified static facial expressions of images presented on a computer screen along with nonsocial distractors while wearing Google Glass and the eye tracker. Faces were presented in three trials, during one of which children received feedback in the form of the correct classification. We employed hybrid human-labeling and computer vision-enabled methods for pupil tracking and world-gaze translation calibration. We analyzed the impact of gaze and emotion recognition features in a prediction task aiming to distinguish children with ASD from NC participants. RESULTS:Gaze and emotion recognition patterns enabled the training of a classifier that distinguished ASD and NC groups. However, it was unable to significantly outperform other classifiers that used only age and gender features, suggesting that further work is necessary to disentangle these effects. CONCLUSIONS:Although wearable smart glasses show promise in identifying subtle differences in gaze tracking and emotion recognition patterns in children with and without ASD, the present form factor and data do not allow for these differences to be reliably exploited by machine learning systems. Resolving these challenges will be an important step toward continuous tracking of the ASD phenotype.