Three Problems for the Predictive Coding Theory of Attention extended abstract.pages

While philosophers of science and epistemologists are well acquainted with Bayesian methods of belief updating, there is a new Bayesian revolution sweeping neuroscience and perceptual psychology. First proposed by Helmholtz, predictive coding is the view that the human brain is fundamentally a hypothesis generator. Though predictive coding has most prominently offered a theory of perception – the bulk of the empirical support for the theory also lies in this domain – the Bayesian framework also promises to deliver a comprehensive theory of attention that falls out of the perceptual theory without the need for positing additional machinery. The predictive coding (PC) theory of attention proposed by Feldman & Friston (2010) and defended by Hohwy (2012, 2013) is that attention is “the process of optimizing precision of prediction errors in hierarchical perceptual inference” (Hohwy 2013, p.195). Prediction errors are measurements of the difference, or mismatch, between predicted and actual evidence. Expected precisions are a measure of how reliable, or precise, we expect the prediction error signal to be in a given context: how likely is it in a given situation that the incongruent data constitutes legitimate prediction error as opposed to noise? On this picture, attention has the functional role of guiding perceptual inference by directing processing resources towards the prediction errors with the higher expected precisions. We argue here that this theory of attention faces significant challenges on three counts. First, while the theory may provide a successful account of endogenous spatial attention, it fails to model endogenous feature-based attention: for attention to be driven by expectations of precision, then it has to be driven to an area where a large prediction error is generated. However, this is the inverse of what is needed to drive attention towards the relevant object. We further consider whether Clark’s (2013) proposed ‘provisional detection’ solution to a similar problem raised by (Bowman, Filetti, Wyble, & Olivers, 2013) can be understood along the lines of ‘gist perception’ (Bar, 2003), and whether this resolves the issue. Second, it is unclear how the theory may accommodate non-perceptual forms of attention such as attention to one’s thoughts. The PC theory of attention is committed to the claim that attention just is the amplification of gain on prediction error, and that this is driven by expected precision. So the proposal would be that we pay attention to our thoughts when we expect them to be precise. However, this proposal remains to be filled out. Do we expect our thoughts to be more precise on some occasions rather than others? If so, what learned causal regularity underlies this expectation? Third, it fails to accommodate the influence of affectively salient objects or high cost