Bias in perceptual learning.

Perceptual learning is commonly understood as conferring some benefit to the learner, such as allowing for the extraction of more information from the environment. However, perceptual learning can be biased in several different ways, some of which do not appear to provide such a benefit. Here we outline a systematic framework for thinking about bias in perceptual learning and discuss how several cases fit into this framework. We argue these biases are compatible with an understanding in which perceptual learning is beneficial, but that its benefits are tied to both a person's narrow interests and the training environment or domain, and so if there are changes to either of these, then benefits can turn into liabilities, though these are often temporary. This article is categorized under: Psychology > Learning Philosophy > Value Linguistics > Language Acquisition.

Affect-biased attention and predictive processing.

In this paper we argue that predictive processing (PP) theory cannot account for the phenomenon of affect-biased attention - prioritized attention to stimuli that are affectively salient because of their associations with reward or punishment. Specifically, the PP hypothesis that selective attention can be analyzed in terms of the optimization of precision expectations cannot accommodate affect-biased attention; affectively salient stimuli can capture our attention even when precision expectations are low. We review the prospects of three recent attempts to accommodate affect with tools internal to PP theory: Miller and Clark's (2018) embodied inference; Seth's (2013) interoceptive inference; and Joffily and Coricelli's (2013) rate of change of free energy. In each case we argue that the account does not resolve the challenge from affect-biased attention. For this reason, we conclude that prediction error minimization is not sufficient to explain all mental phenomena, contrary to the claim that the PP framework provides a unified theory of all mental phenomena or the brain's cognitive functioning. Nevertheless, we suggest that empirical investigation of the interaction between affective salience and precision expectations should prove helpful in understanding the limits of PP theory, and may provide new directions for the application of a Bayesian perspective to perception.

Attention in the predictive mind.

It has recently become popular to suggest that cognition can be explained as a process of Bayesian prediction error minimization. Some advocates of this view propose that attention should be understood as the optimization of expected precisions in the prediction-error signal (Clark, 2013, 2016; Feldman & Friston, 2010; Hohwy, 2012, 2013). This proposal successfully accounts for several attention-related phenomena. We claim that it cannot account for all of them, since there are certain forms of voluntary attention that it cannot accommodate. We therefore suggest that, although the theory of Bayesian prediction error minimization introduces some powerful tools for the explanation of mental phenomena, its advocates have been wrong to claim that Bayesian prediction error minimization is 'all the brain ever does'.