Machine learning for distinguishing saudi children with and without autism via eye-tracking data

BACKGROUND

Despite the prevalence of Autism Spectrum Disorder (ASD) globally, there's a knowledge gap pertaining to autism in Arabic nations. Recognizing the need for validated biomarkers for ASD, our study leverages eye-tracking technology to understand gaze patterns associated with ASD, focusing on joint attention (JA) and atypical gaze patterns during face perception. While previous studies typically evaluate a single eye-tracking metric, our research combines multiple metrics to capture the multidimensional nature of autism, focusing on dwell times on eyes, left facial side, and joint attention.

METHODS

We recorded data from 104 participants (41 neurotypical, mean age: 8.21 ± 4.12 years; 63 with ASD, mean age 8 ± 3.89 years). The data collection consisted of a series of visual stimuli of cartoon faces of humans and animals, presented to the participants in a controlled environment. During each stimulus, the eye movements of the participants were recorded and analyzed, extracting metrics such as time to first fixation and dwell time. We then used these data to train a number of machine learning classification algorithms, to determine if these biomarkers can be used to diagnose ASD.

RESULTS

We found no significant difference in eye-dwell time between autistic and control groups on human or animal eyes. However, autistic individuals focused less on the left side of both human and animal faces, indicating reduced left visual field (LVF) bias. They also showed slower response times and shorter dwell times on congruent objects during joint attention (JA) tasks, indicating diminished reflexive joint attention. No significant difference was found in time spent on incongruent objects during JA tasks. These results suggest potential eye-tracking biomarkers for autism. The best-performing algorithm was the random forest one, which achieved accuracy = 0.76 ± 0.08, precision = 0.78 ± 0.13, recall = 0.84 ± 0.07, and F1 = 0.80 ± 0.09.

CONCLUSIONS

Although the autism group displayed notable differences in reflexive joint attention and left visual field bias, the dwell time on eyes was not significantly different. Nevertheless, the machine algorithm model trained on these data proved effective at diagnosing ASD, showing the potential of these biomarkers. Our study shows promising results and opens up potential for further exploration in this under-researched geographical context.

FamFac - A Database of Famous Faces for Psychology Experiments

Introduction

High variation in the low-level proprieties of visual stimuli and varying degrees of familiarity with famous faces may have caused a bias in the results of investigations that tried to disentangle the processes involved in familiar and unfamiliar face processing (e.g., temporal differences in the detection of the first event-related potentials specialized in face processing may have been caused by different methods of controlling variance in the low-level proprieties of visual stimuli).

Objective

To address these problems, we developed a freely available database of 183 famous faces whose low-level proprieties (brightness, size, resolution) have been homogenized and the level of familiarity established.

Method

The brightness of the stimuli was standardized by a custom-developed algorithm. The size and the resolution of the pictures were homogenized in Gimp. The familiarity level of the famous faces was established by a group of 48 Portuguese college students.

Results

Our results suggest that the brightness of each image did not differ significantly from the mean brightness value of the stimuli set, confirming the standardizing ability of the algorithm. Forty-one famous faces were classified as highly familiar.

Main findings and implications

This study provides two important resources, as both the algorithm and the database are freely available for research purposes. The homogenization of the low-level features and the control of the level of familiarity of the famous faces included in our database should ensure that they do not elicit confounding effects such as the ones verified in past studies.

Is the Other-Race Effect in Working Memory Due to Attentional Refreshing?

The other-race effect is the observation that faces from another ethnicity induce worst recall performance than faces from one's own ethnicity. This effect has been defined as a type of familiarity effect, with more familiar faces better recalled than less familiar faces. In this study, we tested the hypothesis that a working memory maintenance mechanism called attentional refreshing mediates the other-race effect and that faces from one's own ethnicity are refreshed more efficiently than faces from other ethnicities. In two experiments, face ethnicity was orthogonally manipulated with cognitive load of a concurrent processing task in a complex-span paradigm (Exp. 1) and with the memory load in a Brown-Peterson paradigm (Exp. 2). Both cognitive and memory load effects are indices of the functioning of attentional refreshing. Testing Caucasian young adults, Caucasian and East-Asian faces were contrasted. Results from both experiments were congruent and against our initial hypothesis. The other-race effect in working memory does not appear to be supported by attentional refreshing. Furthermore, the results are congruent with the idea that faces as a whole are not maintained in working memory via attentional refreshing.

A Distinct Neurophenotype of Fearful Face Processing in Alcohol Use Disorder With and Without Comorbid Anxiety

BACKGROUND

Individuals with alcohol use disorder (AUD) can present with comorbid anxiety symptoms and often have deficits in emotional processing. Previous research suggests brain response is altered during facial affect recognition tasks, especially in limbic areas, due to either AUD or anxiety symptomology; however, the impact of both AUD and clinically significant anxiety symptoms during these tasks has not yet been examined.

METHODS

In this study, we investigated neural activation differences during an emotional face-matching task. Participants (N = 232) underwent fMRI scanning, as part of a larger study. Three groups were investigated: individuals with diagnosed AUD and elevated anxiety traits (AUD + ANX, n = 90), individuals with diagnosed AUD but non-clinically significant levels of anxiety (AUD-ANX, n = 39), and healthy controls (HC, n = 103).

RESULTS

Our results illustrate distinct neurophenotypes of AUD, where individuals with comorbid anxiety symptomology have blunted emotional face processing while those with singular AUD are hyperresponsive.

CONCLUSIONS

This suggests AUD with anxiety symptomology may have a unique neurobiological underpinning, and treatment and intervention should be tailored to individual constellations of symptoms.