From representations in predictive processing to degrees of representational features

Representations and their features can be gradual rather than all-or-nothing, but this idea has been largely overlooked in philosophy of mind. This paper develops a gradual account of two representational features—structural similarity and decoupling—within the predictive processing framework of neuroscience. Structural similarity is analyzed along two dimensions: the number of preserved relations and the granularity of state space, both of which vary continuously. Decoupling is gradual in two ways: different brain areas are involved in decoupled processes to varying degrees depending on whether their activity is caused internally or externally, and the degree of decoupling can be further regulated through precision weighting of prediction error. Gradation of both features supports behavioral success.

Study at a glance

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.