Exploring the quantitative nature of empathy, systemising and autistic traits using factor mixture modelling

Area of Science:

• Neurodevelopmental disorders
• Psychology
• Genetics

Background:

• Autism research has historically focused on either a single latent dimension or distinct subgroups.
• A need exists for research examining the simultaneous categorical and dimensional aspects of autism.

Purpose of the Study:

• To explore the underlying structure of autism.
• To identify clinically relevant subgroups within the autism spectrum.
• To analyze a sample with varying genetic vulnerability for autism spectrum disorder.

Main Methods:

• Factor mixture modeling was employed.
• Data included measures of empathy, systemising, and autistic traits.
• Participants comprised individuals with autism, their parents, and general population controls.

Main Results:

• A two-factor, three-class model was identified.
• The factors represented empathy and systemising.
• Class 1: High systemising, low empathy (primarily autistic individuals). Class 2: Balanced scores. Class 3: High empathy, low systemising (primarily controls/parents).

Conclusions:

• Autism is best conceptualized as a dimensional construct.
• Meaningful subgroups within autism can be identified.
• Empathy, systemising, and autistic traits are key differentiators for these subgroups.