From symbols to icons: the return of resemblance in the cognitive neuroscience revolution

Abstract

We argue that one important aspect of the "cognitive neuroscience revolution" identified by Boone and Piccinini (Synthese 193(5):1509-1534. doi:10.1007/ s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both "from below" and "from above"-that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves ("from below"), and from a greater appreciation of the problems solved by bio-cognitive systems, chiefly regulation and prediction ("from above"). We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of (predictive) dynamical models as a central postulate in neurocognitive research.

Pragmatism and the predictive mind

“…Consonant with a broader debate in recent cognitive science, there is a growing literature on to what extent predictive processing advances a representational theory of cognition, with some arguing that it does (Clark 2016;Gladziejewski 2015;Hohwy 2013;Williams 2017;Williams and Colling 2017) and others that it doesn't (Gallagher and Allen 2016;Bruineberg et al 2016;Hutto 2017). In line with common assumptions in the philosophical literature, this debate is often identified with the debate concerning to what extent it explains intentionality, typically understood in terms of content and thus veridicality conditions.…”

“…This perspective on brain function effectively follows insights from mid-twentieth-century cybernetics in modelling brains as regulators responsible for maintaining essential homeostatic variables within viable bounds (Conant and Ashby 1970;cf. Seth 2015;Williams and Colling 2017). The novel contribution of the free-energy principle is to provide an information-theoretic interpretation both of what this homeostatic process amounts to and how it can be achieved in biological agents via the minimization of a quantity to which they have internal access.…”

“…First, the exploitation of structures that function as proxies for other states or conditions is plausibly ubiquitous in biological systems (Bechtel 2008, ch.5;Williams and Colling 2017). Indeed, it is a central tenet of the Bgood regulator theorem^ (Conant and Ashby 1970) developed by the free-energy principle that all optimal regulative systems exploit structures that are isomorphic to the systems they regulate (Friston 2010).…”

“…This understanding of terms like Bmodel^is at odds with how the term is understood in traditions such as cybernetics and control theory, however. Further, exploitable structural similarity is different from mere detection or causal covariation (see Williams and Colling 2017), so the trivialising arguments against such latter relations-advanced, for example, by Ramsey (2007)-do not work. exploits a stand-in (the level of mercury) for an interest-relative (in this case, our interests) environmental variable (ambient temperature).…”

“…There has recently been an emergence of Bstructural resemblance^theories of content within the predictive brain (Gładziejewski 2015;Williams 2017;Williams and Colling 2017), endorsed by Hohwy (Kiefer and Hohwy 2017) himself. The central idea here is that prediction error minimization induces a generative model that mirrors and so resembles the causal-probabilistic structure of the bodily and environmental causes of the brain's evolving sensory inputs.…”