Behavioral Sciences, Social Psychology; autism, cognition, components, psychiatric impairments, comorbidity
We propose a theory of ASD as a condition of comorbid cognitive impairments that corrupt the learning, encoding, and manipulation of episodic and semantic memories. We consider (i) episodic and semantic memory functions of the entorhinal-hippocampal complex, (ii) constraints on the transfer and encoding of these memory components into neocortical areas, and (iii) the demands of cognitively manipulating memories in distributed computations being necessary for goal oriented interactions. In ASD, learning and cognitive challenges manifest in diverse ways but especially in high-complexity model predictive control tasks with latent variables. ASD impairments in social interactions represent a prototypical example. Social interactions are at the high end of complexity and require processes (i)–(iii) to work in a concerted fashion due to the need for the learning and estimation of many, sometimes latent, parameters, including emotions, intention, physical and mental capabilities as well as the predictive modeling of these parameters for decision making and timed-action series. We put forth the idea that autism is a result of an arbitrary combination of otherwise not prominent corruptions in processes (i)–(iii). Together, these corruptions may severely impair intelligence and slow down learning, especially in high complexity learning tasks. Over time, slow learning may spare the spontaneous learning-by-doing method - namely, repetitive behavioral patterns, whereas behavioral failures related to complex tasks can restrict interest in such task, thus inducing a fear of novelty; conversely, the fear of novelty restricts interest and can slow learning down. We embed our thoughts into a predictive autoencoding, goal-oriented model of a deterministic world. We compare this model to others, such as the noisy brain model, the Bayesian prior theory, the mirror neuron theory and the weak central coherence theory. We argue that the predictive autoencoder model of the deterministic world harmonizes with these other models and embraces them in a straightforward way.