Attention and Conscious Perception in the Hypothesis Testing Brain

Abstract

Conscious perception and attention are difficult to study, partly because their relation to each other is not fully understood. Rather than conceiving and studying them in isolation from each other it may be useful to locate them in an independently motivated, general framework, from which a principled account of how they relate can then emerge. Accordingly, these mental phenomena are here reviewed through the prism of the increasingly influential predictive coding framework. On this framework, conscious perception can be seen as the upshot of prediction error minimization and attention as the optimization of precision expectations during such perceptual inference. This approach maps on well to a range of standard characteristics of conscious perception and attention, and can be used to interpret a range of empirical findings on their relation to each other.

Symptoms and the body: Taking the inferential leap

“…Conscious contents, on the other hand, are determined by the generative model with the lowest overall prediction error. By this view, there is a close correspondence between what is "attended to" (i.e., receives gain) and what the individual is conscious of (as gain increases the likelihood of the model being optimal), but the two concepts are nevertheless separable (Hohwy, 2012).…”

“…In order to do this, it must be able to estimate the likelihood of any residual prediction error being random noise, or whether it could be reduced further by updating the prediction. The system does so by developing context-dependent expectations about the likely precision of its inputs, and it compensates for these by adjusting the weight placed on them in the perceptual process (Hohwy, 2012). Visual prediction errors are higher in the dark, for example, and relatively more likely to be a product of noise than signal than when it is light.…”

“…The process of attributing weights to the prediction errors to optimize perception (precision optimization) is thought to be an important factor determining the dominant generative model and therefore conscious percepts (Feldman and Friston, 2010;Hohwy, 2012). Typically, priors have less influence and are more subject to revision when processing involves units expecting precise information from the sensorium (e.g., in the light).…”

“…This gain mechanism is attracted to causal regularity in the world, giving a probabilistic advantage to generative models that maximize the signal-to-noise ratio (Hohwy, 2012). When a cue promoting attention is valid (i.e., predictive), it facilitates perception of the corresponding stimuli; when it is invalid, subsequent stimuli will be at a perceptual disadvantage (Hohwy, 2012). As precision weights vary according to individual and contextual factors, these factors will influence the accuracy of somatic perception.…”

A social Bayesian brain: How social knowledge can shape visual perception

“…Moreover, they assert that perceptual inference is implemented through minimization of these prediction error signals (Bastos et al, 2012;Rao & Ballard, 1999). Inference via prediction error minimization is assumed to take place over multiple levels of perceptual and cognitive processing, so that posteriors at one level form priors for the level immediately below (Friston, 2009;Friston, 2010b;Hohwy, 2012). In a hierarchical scheme, if prediction errors at one level cannot be sufficiently minimized by predictions from the layer immediately above, prediction error will percolate upward through the system (see Fig.…”