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What are the contents of representations in predictive processing?

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Paweł Gładziejewski has recently argued that the framework of predictive processing (PP) postulates genuine representations. His focus is on establishing that certain structures posited by PP actually play a representational role. The goal of this paper is to promote this discussion by exploring the contents of representations posited by PP. Gładziejewski already points out that structural theories of representational content can successfully be applied to PP. Here, I propose to make the treatment slightly more rigorous by invoking Francis Egan’s distinction between mathematical and cognitive contents. Applying this distinction to representational contents in PP, I first show that cognitive contents in PP are (partly) determined by mathematical contents, at least in the sense that computational descriptions in PP put constraints on ascriptions of cognitive contents. After that, I explore to what extent these constraints are specific (i.e., whether PP puts unique constraints on ascriptions of cognitive contents). I argue that the general mathematical contents posited by PP do not constrain ascriptions of cognitive content in a specific way (because they are not relevantly different from mathematical contents entailed by, for instance, emulators in Rick Grush’s emulation theory). However, there are at least three aspects of PP that constrain ascriptions of cognitive contents in more specific ways: (i) formal PP models posit specific mathematical contents that define more specific constraints; (ii) PP entails claims about how computational mechanisms underpin cognitive phenomena (e.g. attention); (iii) the processing hierarchy posited by PP goes along with more specific constraints.

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  1. I am using the term “predictive processing” in the sense of Clark (2013b, p. 202/fn. 5). Predictive processing entails hierarchical, approximately Bayesian inference using prediction error minimization (cf. also section 1).

  2. Bowers and Davis (2012) draw a distinction between three types of Bayesian theorizing (extreme, methodological and theoretical), emphasizing that using Bayesian models in cognitive science does not entail a commitment to the assumption that the brain literally implements Bayesian inference (which the authors see as a weakness, as this can lead to “Bayesian just-so stories”). Cf. also Clark (2013b, p. 189).

  3. As an anonymous referee helpfully pointed out, there are also more general considerations regarding computational (in)tractability which provide a reason to think that explicit representations of probability distributions are not only not required, but also very unlikely to be actually employed by the brain (see, e.g. Kwisthout and van Rooij 2013).

  4. “Surprisal” denotes the negative logarithm of the probability of an event (cf. Friston 2010, p. 128).

  5. I am grateful to an anonymous reviewer for pressing me to clarify this point.

  6. N.B.: Gładziejewski does not explicitly say what he means by the “causal-probabilistic” structure. For instance, it is unclear whether he means that events in the world cause each other probabilistically (although there is one passage which seems to point in this direction, see Gładziejewski 2015, pp. 570 f.), or whether he assumes there is principled randomness of some sort etc. The most charitable reading is arguably that internal model capture the causal structure of the world; due to noise and partial lack of information (which brings about uncertainty), this model has to be probabilistic; whether the causal structure of the world is itself also probabilistic in some sense is left open; hence the term “causal-probabilistic”.

  7. According to the structural concept of representational content (cf. Bartels 2006), the relation of representation involves a (partial) homomorphism, i.e., a structure-preserving mapping between two structures (where the structures are defined as sets with a family of relations). The paradigmatic example is provided by cartographic maps. Here, the sets are points and the structures are given by spatial relations between the points. A more abstract example would be two dynamical systems whose evolutions can be described by the same differential equations. The structures are here given by functional relations between variables (that specify how the variables influence each other over time).

  8. This is not to deny that they are importantly different from traditional, symbolic representations figuring in classical theories of cognition.

  9. The underlying idea is similar to the basic idea of eliminative/revisionist materialism. As Patricia Churchland has pointed out, following a reductionist strategy regarding consciousness and neurobiology is compatible with the idea that “hypotheses at various levels can co-evolve as they correct and inform one another.” Churchland (1994, p. 25).


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I am grateful to Michael Madary and Thomas Metzinger for a number of very helpful comments on drafts of this paper. Thanks also to the anonymous referees for their constructive criticism.

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Correspondence to Wanja Wiese.

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Wiese, W. What are the contents of representations in predictive processing?. Phenom Cogn Sci 16, 715–736 (2017).

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