When the world becomes 'too real': a Bayesian explanation of autistic perception.

By Elizabeth Pellicano, David C. Burr. Trends in Cognitive Sciences, 2012

1,096 citations recorded by the source corpus.

Perceptual experience is influenced both by incoming sensory information and prior knowledge about the world, a concept recently formalised within Bayesian decision theory. We propose that Bayesian models can be applied to autism - a neurodevelopmental condition with atypicalities in sensation and perception - to pinpoint fundamental differences in perceptual mechanisms. We suggest specifically that attenuated Bayesian priors - 'hypo-priors' - may be responsible for the unique perceptual experience of autistic people, leading to a tendency to perceive the world more accurately rather than modulated by prior experience. In this account, we consider how hypo-priors might explain key features of autism - the broad range of sensory and other non-social atypicalities--in addition to the phenomenological differences in autistic perception.

Subfields: Cognitive Neuroscience, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/81a809e0377eebfe85b59f2ca6195a52a9615bfc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Developmental and Educational Psychology

Citations in the corpus

The Computational Anatomy of Psychosis (2013, background): “, the loss of central coherence in autism (Pellicano and Burr, 2012).”
Theory of cortical function (2017, background): “For example, NA fluctuations might exhibit a 1/f amplitude spectrum (81).”
Precise minds in uncertain worlds: predictive coding in autism. (2014, background): “Though these findings may mean that perception is less influenced by prior knowledge (in this case the shifted prototype) (Pellicano & Burr, 2012), we would propose that it is the consequence of an abnormal updating of representations (prototypes).”
The anatomy of choice: active inference and agency (2013, background): “…of hierarchical Bayesian inference (Mathys et al., 2011), from theoretical neurobiology (Frank et al., 2007; Fletcher and Frith, 2009; Friston et al., 2012b; Pellicano and Burr, 2012) and from empirical studies (Fiorillo et al., 2008; Coull et al., 2011; Galea et al., 2012; Zokaei et al., 2012).”
Active interoceptive inference and the emotional brain (2016, background): “This theme is emerging repeatedly in psychiatry: from false inference as an account of positive symptoms (hallucinations and delusions) in schizophrenia (76), to the loss of central coherence in autism (77).”
An aberrant precision account of autism (2014, positive): “The debate between low " hypo-priors " (Pellicano and Burr, 2012) and decreased sensory noise (Brock, 2012) can be usefully contextualized under hierarchical predictive coding: the issue here is whether precision is too low at higher (prior) levels of the hierarchy , or too high at lower (sensory)…”
An Introduction to Model-Based Cognitive Neuroscience (2015, background): “neuron within a circuit, ultimately again within the entire brain, a ‘functional’ connectome [119].”