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Home Exclusive Mental Health Autism

Autistic adults have very precise visual representation of emotions, but this does not help them recognize emotions correctly

by Vladimir Hedrih
October 12, 2023
in Autism
(Photo credit: Adobe Stock)

(Photo credit: Adobe Stock)

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A recent study that autistic adults have more precise visual representation of emotions compared to non-autistic individuals. However, this does not help them recognize emotions better. In contrast, non-autistic individuals with better representation of emotions were also better at recognizing emotions. The study was published in Scientific Reports.

Autism spectrum disorder is a complex neurodevelopmental condition that affects social interaction, communication, and behavior. It is characterized by a wide range of symptoms, which is why it’s referred to as a “spectrum.” Common features of autism spectrum disorder include difficulties in understanding and interpreting social cues, repetitive behaviors, and restricted interests. Symptoms typically become apparent in early childhood.

The ability to recognize emotions of others is not a core feature for diagnosing autism, but it has attracted a lot of research interest. While some studies reported that individuals with autism spectrum disorder have more difficulty in recognizing the emotions of others compared to non-autistic individuals, other studies have found no differences or the differences they found were very specific and limited.

Study author Connor T. Keating and his colleagues wanted to explore whether autistic and non-autistic adults differed in the precision of visual representation of emotions and how quickly they were able to use these representations to solve tasks. They were also interested in seeing whether the individual differences in this capacity were associated with the ability to accurately recognize emotional expressions.

The study included 45 autistic and 45 non-autistic adults, sourced from the Birmingham Psychology Autism Research Team database, the Centre for Autism Research Oxford database, and through Prolific. Every participant in the autistic category had previously been diagnosed by a clinician who was not part of the research team. Both groups had more females than males, with the average participant age around 35 years.

Participants completed assessments of autistic traits (the Autism Quotient), alexithymia (the Toronto Alexithymia Scale), non-verbal reasoning (the Matrix Reasoning Item Bank), and Emotion recognition (the PLF Emotion recognition task, they viewed videos of actors displaying emotions and had to rate the type and intensity of the emotion). Alexithymia is a psychological condition characterized by difficulty in identifying, expressing, and understanding one’s own emotions.

They also completed two other emotional representation tasks: the ExpressionMap and the Visual Matching Task. In the ExpressionMap, participants viewed a looping video at random speeds that exhibited an emotional expression. Their job was to calibrate the video’s speed to align with the real-time emotional display. The hypothesis was that those with less accurate emotional representations would assign varied speeds to videos portraying emotions.

For the Visual Matching Task, participants synchronized the speed of one emotion video with a reference video showcasing a different emotion. The alignment accuracy between the two videos was then calculated, offering another measure of emotion representation quality.

Results showed that participants were the most precise when determining the speed of the sad expressions, followed by happy expressions. Older individuals and those with better non-verbal reasoning were more successful on the emotional representation tasks. Contrary to the researchers’ expectations, autistic participants displayed greater precision in video speed adjustments. This suggests that autistic individuals maintain a more accurate visual representation of emotions.

Individuals with better non-verbal reasoning were better able to match speeds of two different emotional expressions, but results of autistic and non-autistic individuals did not differ on this task. Comparison of the two groups on other indicators of visual representation of emotions also yielded no differences.

Finally, the researchers used a machine learning algorithm to explore which characteristics contribute to the quality of mental representation of emotions in the two groups. Results revealed that for non-autistic participants, the quality of mental representation of emotions and non-verbal reasoning were important factors for their ability to correctly recognize emotions.

On the other hand, the ability of autistic individuals to recognize emotions was not associated with any of the studied factors, indicating that the quality of visual representation of emotions might not be helping these individuals to be more successful in recognizing emotions.

“We found that autistic individuals had more precise visual emotion representations than their non-autistic counterparts (in the speed domain). That is, the autistic participants were more precise (i.e., consistent) in the speeds they attributed to angry, happy and sad facial expressions across repetitions,” the reseachers explained.

“Nevertheless, this enhanced precision did not confer any benefit for their emotion recognition. Whilst for non-autistic people, non-verbal reasoning and the interaction between precision of emotion representations and matching ability predicted emotion recognition, no variables contributed to autistic emotion recognition.”

The study makes a valuable contribution to the scientific understanding of social perception of people with autism spectrum disorder. However, it also has limitations that need to be considered. Notably, the study sample was small. Additionally, real-world recognition of emotions is much more complex and based on many more factors than the models of emotions used in the study.

The study, “Autistic adults exhibit highly precise representations of others’ emotions but a reduced influence of emotion representations on emotion recognition accuracy”, was authored by Connor T. Keating, Eri Ichijo, and Jennifer L. Cook.

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