 At the heart of the framework is the idea that minds and mental activity can be understood not as any particular kind of substance, but rather as some form of information processing. The currently most intuitive and probably still dominant version of the idea is expressed with the computer analogy, though some have argued that antecedents and earlier versions of the core insight can be found as far back as Aristotle’s doctrine of hylomorphism and its distinction between matter and form (Shields 1991; Nussbaum &amp; Putnam 1992; cf. Nelson 1990; Olshewsky 1992). The computer analogy holds that the relation between the physical and mental aspects of a person, between bodies and minds, between the physical and the mental more generally, can be understood on the model of the relation between hardware and software (Putnam 1967). On this view, mental states are functional states identified by the role they play in the “program” that channels information through and guides the control system of a physical body. Mental processes, then, are understood as computational processes performed on mental representations, symbols that have both syntactic and semantic properties, and that are usually but not necessarily realized in brains. Mental processes and representations can be modeled using the formal resources of computation theory, with syntactic relations between the mental representations encoding patterns of causal relations that hold between the physical states that realize them (Fodor 1975; Stich 1983; Rescorla 2015 [2020]). In the last several decades, aspects of this orthodoxy have been challenged, especially regarding the existence and character of mental representations (Pitt 2000 [2020]). Several alternatives to and extensions of this “classical computationalist” way of understanding information processing have made inroads into cognitive science, and have been discussed by philosophers under the headings of connectionism (Buckner &amp; Garson 2019), embodied cognition (Clark 1997a; Shapiro 2011; R. Wilson &amp; Foglia 2011 [2021]), enactivism (Hutto &amp; Myin 2013; Di Paolo &amp; Thompson 2014), dynamic systems approaches (Chemero 2009; M. Anderson 2014), complexity science (Favela 2020), and predictive processing (Hohwy 2013; Clark 2016).