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Related Experiment Video

Updated: Jul 20, 2025

Strategies for Assessing Autistic-Like Behaviors in Mice

Strategies for Assessing Autistic-Like Behaviors in Mice

Published on: September 20, 2024

Exploring randomness in autism.

Vasileios Mantas¹, Vasileia Kotoula², Artemios Pehlivanidis¹

¹1st Department of Psychiatry, Aiginiteion Hospital, National and Kapodistrian University of Athens, Athens, Attica, Greece.

|August 2, 2023

Summary

Autistic individuals and comparison groups showed similar random number generation patterns. Machine learning identified distinct individual patterns, suggesting potential for group discrimination in innate randomness. Further research with larger samples is needed.

Keywords:

ASD Autism Dual process theory Randomness

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Area of Science:

Cognitive Psychology
Neuroscience
Autism Spectrum Disorders Research

Background:

Dual system processing model involves intuitive (System 1) and analytical (System 2) decision-making.
Both systems may utilize randomness, with System 1 more prominent in arbitrary choices.
An innate randomness mechanism is hypothesized to influence decision-making.

Purpose of the Study:

To investigate potential innate randomness mechanisms in individuals with Autism Spectrum Disorders (ASD).
To isolate System 1 processing by examining arbitrary decision-making in high-functioning ASD participants.
To explore differences in random number generation between autistic individuals and a neurotypical comparison group.

Main Methods:

Participants with high-functioning ASD and a matched comparison group performed a modified random number generation task.
The task was designed to minimize working memory load, facilitating the emergence of innate randomness.
Standard analysis and machine learning techniques were employed to assess sequence randomness.

Main Results:

No significant differences in randomness characteristics were found between autistic and comparison groups using standard analysis.
A moving window approach also revealed no significant differences in sequence randomness.
Machine learning models successfully discriminated between the groups based on features of random sequences with high accuracy.

Conclusions:

Individual patterns in random sequence production may differ between autistic and comparison groups, enabling accurate discrimination.
These findings suggest potential group-specific characteristics in innate randomness expression.
Replication in larger samples is necessary to confirm findings and draw firm conclusions about innate randomness in ASD.