An entropic explanation of insistence on sameness in autism

Abstract:An information theory-based framework is proposed in attempt to explain insistence on sameness in autism as an instance of a general behavior pattern in which an individual tries to reduce surprise and uncertainty. It offers a new definition of autism as an impairment in which cognitive functions are restricted to discrimination, memorization and prediction of tangible properties of the environment. An analogy between insistence on sameness and constrained minimization of the entropy metric is observed and examined for a set of assumptions that describe cognitive limitations of a person with autism. The metric is given by the formula $D_H(R, M) = H(R|M) + H(M|R)$, where $R$ represents sequences of random stimuli, $M$ is a memory that stores and retrieves them, and where $H(.|.)$ denotes their conditional entropies interpreted as surprise and uncertainty, respectively. It is first inferred that to minimize the metric an individual can learn about $R$ (and store that knowledge in $M$) or can restrict $R$ to the already known $M$. Then, it is concluded that insistence on sameness is a manifestation of the latter. Moreover, it is shown that the proposed framework: (1) Helps to quantify the concepts of surprise, uncertainty, sensory overload and deprivation, anxiety, comfort zone, disappointment, disorientation, pedantry, rigidness, observance or aberrant precision. (2) Leads to a list of guidelines for learning therapies and daily care routines, and allows them to be defined as optimization algorithms and implemented as programs for robotic live-in caregivers. (3) Can be validated with the help of a Turing test-like approach that requires no experiments involving individuals with autism. The framework-if positively validated-will provide formal foundations and design guidelines for therapies aimed at improving self-reliance of individuals with autism in basic activities of daily living.

Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC); Machine Learning (stat.ML)

I.6.3; I.6.5; J.4

arXiv:2608.04616 [cs.LG]

(or arXiv:2608.04616v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.04616 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Śliwiński P (2026) An entropic explanation of insistence on sameness in autism. Frontiers in Computational Neuroscience. 19:1714428

https://doi.org/10.3389/fncom.2025.1714428 Focus to learn more DOI(s) linking to related resources

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