Attention in the predictive mind.
Madeleine Ransom, Sina Fazelpour, Christopher Mole
Consciousness and cognition January 1, 2017 DOI: 10.1016/j.concog.2016.06.011 via PubMed
Summary
AI-generated from the abstractA prominent theory holds that cognition works by minimizing prediction errors through Bayesian inference, with attention understood as optimizing the precision of those error signals. While this account explains many attention-related phenomena, it fails to accommodate certain forms of voluntary attention. The authors argue that advocates of Bayesian prediction error minimization have overreached by claiming it is all the brain ever does, and that the theory's tools, though powerful, are insufficient for a complete explanation of attention.
Study at a glance
| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Keywords | Hohwy Philosophy of perception Prediction-error coding Voluntary attention |
| Citations | 38 |
| Key finding | The Bayesian prediction error minimization account cannot accommodate all forms of voluntary attention, contrary to claims that it explains all cognition. |
Abstract
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'.