Autism as a disorder of prediction

Abstract

A rich collection of empirical findings accumulated over the past three decades attests to the diversity of traits that constitute the autism phenotypes. It is unclear whether subsets of these traits share any underlying causality. This lack of a cohesive conceptualization of the disorder has complicated the search for broadly effective therapies, diagnostic markers, and neural/genetic correlates. In this paper, we describe how theoretical considerations and a review of empirical data lead to the hypothesis that some salient aspects of the autism phenotype may be manifestations of an underlying impairment in predictive abilities. With compromised prediction skills, an individual with autism inhabits a seemingly "magical" world wherein events occur unexpectedly and without cause. Immersion in such a capricious environment can prove overwhelming and compromise one's ability to effectively interact with it. If validated, this hypothesis has the potential of providing unifying insights into multiple aspects of autism, with attendant benefits for improving diagnosis and therapy.

Keywords: Markov models; endophenotype; heterogeneity; probabilistic processing; theory.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

(A) A simple Markov system comprising probabilistically linked states. The domains that serve as diagnostic criteria for autism (language processing, social interactions, and behavioral repertoire) can all be modeled as temporally evolving Markov systems. The computation of transition probabilities is a key requirement for estimating a Markov system. (B) The task of transition probability estimation: from an observed temporal history of multiple state-to-state transitions, estimate P(B|A, Δt); the conditional probability of one state (“B”) given the other (“A”) and temporal duration, Δt, beyond A’s occurrence. The PIA hypothesis states that autism may be associated with inaccuracies in estimating this conditional probability and, hence, in one’s ability to discern predictive relationships between entities.

Fig. 2.

(A) A schematic depiction of the PIA hypothesis. Relationships between two events can be characterized by their strength [P(B|A)] and temporal separation (Δt). In this space, the interface between undetectable and detectable relationships marks the ASF, denoted by the solid curve here. The PIA hypothesis posits that autism is accompanied by a shift of the ASF toward the upper left (red arrows) corresponding to a reduction in one’s sensitivity to relationships that are weak and/or have large temporal spans. This shift renders some interevent relationships, which are evident to neurotypical individuals, invisible to those with autism. The vertical bands indicate that different tasks rely on the detection of interevent relationships over varying time-scales. For instance, whereas motor-control and language learning operate in the millisecond regime, social interactions and planning involve longer time intervals. (B) Behavioral manifestations of PIA may differ depending on which temporal regimes experience the greatest ASF shifts. As depicted in the four small graphs, different autism subtypes may arise in part from ASF shifts of different kinds.