From representations in predictive processing to degrees of representational features
Danaja Rutar, Wanja Wiese, Johan Kwisthout
Minds and Machines May 3, 2022 DOI: 10.1007/s11023-022-09599-6 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Topics | Philosophy of mind |
| Keywords | Decoupling probability Granularity Gradation Weighting Similarity geometry Artificial intelligence Correctness Cognitive science Cognitive psychology Theoretical computer science Pattern recognition psychology Epistemology Algorithm Metaphysics Image mathematics |
| Citations | 3 |
| Key points | Argues that structural similarity and decoupling, two features of structural representations, are gradual and can be analyzed within the predictive processing framework, with gradation in these features conducive to behavioral success. |
Abstract
Abstract Whilst the topic of representations is one of the key topics in philosophy of mind, it has only occasionally been noted that representations and representational features may be gradual. Apart from vague allusions, little has been said on what representational gradation amounts to and why it could be explanatorily useful. The aim of this paper is to provide a novel take on gradation of representational features within the neuroscientific framework of predictive processing. More specifically, we provide a gradual account of two features of structural representations: structural similarity and decoupling. We argue that structural similarity can be analysed in terms of two dimensions: number of preserved relations and state space granularity. Both dimensions can take on different values and hence render structural similarity gradual. We further argue that decoupling is gradual in two ways. First, we show that different brain areas are involved in decoupled cognitive processes to a greater or lesser degree depending on the cause (internal or external) of their activity. Second, and more importantly, we show that the degree of decoupling can be further regulated in some brain areas through precision weighting of prediction error. We lastly argue that gradation of decoupling (via precision weighting) and gradation of structural similarity (via state space granularity) are conducive to behavioural success.