Michael Rescorla

_The representational theory of mind_ (RTM) holds that the mind is stocked with mental representations: mental items that represent. They can be stored in memory, manipulated during mental activity, and combined to form complex representations. RTM is widely presupposed within cognitive science, which offers many successful theories that cite mental representations. Nevertheless, mental representations are still viewed warily in some scientific and philosophical circles. This chapter develops a novel version of RTM: _the capacities-based representational theory of mind (C-RTM)_. According to C-RTM, a mental representation is an abstract type that marks the exercise of a representational capacity. Talk about mental representations embodies an ontologically loaded way of classifying mental states through representational capacities that the states deploy. Complex mental representations mark the appropriate joint exercise of multiple representational capacities. The chapter supports C-RTM with examples drawn from cognitive science, including perceptual representations and cognitive maps, and applies C-RTM to long-standing debates over the existence, nature, individuation, structure, and explanatory role of mental representations.

When conditional probabilities $$P\left(H|E\right)$$ are defined through the ratio formula, conditionalization satisfies two intuitively appealing iteration principles: Commutativity (the order of iterated conditionalization does not affect the final outcome) and Accumulation (iterated conditionalization yields the same result as conditionalizing upon the conjunction of the individual conditioning propositions). When P(E) = 0, the ratio formula is ill-defined, so a more general treatment of conditional probability is needed. The standard mathematical treatment, stemming from Kolmogorov’s work, centers upon regular conditional distributions (rcds). I investigate the degree to which Commutativity and Accumulation extend to the general setting provided by rcds. In the right circumstances, rcd conditionalization turns out to satisfy iteration principles analogous to Commutativity and Accumulation. However, the iteration principles usually allow exceptions. Moreover, there are circumstances where rcd conditionalization satisfies nothing resembling Commutativity or Accumulation. Grappling with these complexities is essential for assessing how well rcds serve Bayesian inference.

Beginning with Turing himself, many researchers have suggested that mental processes are Turing-style computations. Proponents typically develop this picture in conjunction with the formal-syntactic conception of computation (FSC), which holds that computation manipulates formal syntactic item s without regard to their representational or semantic properties. I explore an alternative semantically permeated approach, on which many core mental computations are composed from inherently representational elements. The mental symbols over which the computations operate, and hence the computations themselves, have natures inextricably tied to their representational import. We cannot factor out this representational import to generate an explanatorily significant formal syntactic remainder. I argue that the Turing formalism provides no support for FSC over the semantically permeated alternative. I then critique various popular arguments for FSC.

When P(E) > 0, conditional probabilities P(H|E)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(H|E)$$\end{document} are given by the ratio formula. An agent engages in ratio conditionalization when she updates her credences using conditional probabilities dictated by the ratio formula. Ratio conditionalization cannot eradicate certainties, including certainties gained through prior exercises of ratio conditionalization. An agent who updates her credences only through ratio conditionalization risks permanent certainty in propositions against which she has overwhelming evidence. To avoid this undesirable consequence, I argue that we should supplement ratio conditionalization with Kolmogorov conditionalization, a strategy for updating credences based on propositions E such that P(E) = 0. Kolmogorov conditionalization can eradicate certainties, including certainties gained through prior exercises of conditionalization. Adducing general theorems and detailed examples, I show that Kolmogorov conditionalization helps us model epistemic defeat across a wide range of circumstances.

Kolmogorov conditionalization is a strategy for updating credences based on propositions that have initial probability 0. I explore the connection between Kolmogorov conditionalization and Dutch books. Previous discussions of the connection rely crucially upon a factivity assumption: they assume that the agent updates credences based on true propositions. The factivity assumption discounts cases of misplaced certainty, i.e., cases where the agent invests credence 1 in a falsehood. Yet misplaced certainty arises routinely in scientific and philosophical applications of Bayesian decision theory. I prove a non-factive Dutch book theorem and converse Dutch book theorem for Kolmogorov conditionalization. The theorems do not rely upon the factivity assumption, so they establish that Kolmogorov conditionalization has unique pragmatic virtues that persist even in cases of misplaced certainty.

A striking thesis lies at the core of Davidson's philosophy: when we attribute intentional content to another creature's mental states and speech acts, we must treat the creature as largely conforming to our own rational norms. I will discuss how this thesis informs Davidson's treatment of rationality and intentionality. After reviewing some historical background, I present basic aspects of Davidson's position. I then examine various worries about the position. I conclude by highlighting some key Davidsonian insights into rationality.

Conditionalization governs how to reallocate credence in light of new evidence. One prominent argument in favor of Conditionalization holds that an agent who violates it is vulnerable to a diachronic Dutch book: a series of acceptable bets offered at multiple times that inflict a sure loss. van Fraassen argues that an agent who violates the Principle of Reflection is likewise vulnerable to a diachronic Dutch book. He concludes that agents should conform to both Conditionalization and Reflection. Some authors reply that Reflection is implausible and hence that there must be something wrong with diachronic Dutch book arguments. Other authors try to isolate a principled difference between the Dutch book argument for Conditionalization and the Dutch book argument for Reflection, such that the former argument may succeed even though the latter fails. I pursue a version of this strategy. I contend that, once we properly elucidate the notion of sure loss, non‐reflectors are not vulnerable to a sure loss. An agent who violates Reflection is not thereby subject to a diachronic Dutch book. Appearances to the contrary result from an unmotivated focus upon an overly narrow set of gambling scenarios.

Lewis proved a Dutch book theorem for Conditionalization. The theorem shows that an agent who follows any credal update rule other than Conditionalization is vulnerable to bets that inflict a sure loss. Lewis’s theorem is tailored to factive formulations of Conditionalization, i.e. formulations on which the conditioning proposition is true. Yet many scientific and philosophical applications of Bayesian decision theory require a non-factive formulation, i.e. a formulation on which the conditioning proposition may be false. I prove a Dutch book theorem tailored to non-factive Conditionalization. I also discuss the theorem’s significance.

Conditionalization is a norm that governs the rational reallocation of credence. I distinguish between factive and non-factive formulations of Conditionalization. Factive formulations assume that the conditioning proposition is true. Non-factive formulations allow that the conditioning proposition may be false. I argue that non-factive formulations provide a better foundation for philosophical and scientific applications of Bayesian decision theory. I furthermore argue that previous formulations of Conditionalization, factive and non-factive alike, have almost universally ignored, downplayed, or mishandled a crucial causal aspect of Conditionalization. To formulate Conditionalization adequately, one must explicitly address the causal structure of the transition from old credences to new credences. I offer a formulation of Conditionalization that takes these considerations into account, and I compare my preferred formulation with some prominent formulations found in the literature.

It is widely agreed that one can fruitfully describe a computing system at various levels. Discussion typically centers on three levels: the representational level, the syntactic level, and the hardware level. I will argue that the three-level picture works well for artificial computing systems (i.e. computing systems designed and built by intelligent agents) but less well for natural computing systems (i.e. computing systems that arise in nature without design or construction by intelligent agents). Philosophers and cognitive scientists have been too hasty to extrapolate lessons drawn from artificial computation to the much different case of natural computation.

The perceptual system estimates distal conditions based upon proximal sensory input. It typically exploits information from multiple cues across and within modalities: it estimates shape based upon visual and haptic cues; it estimates depth based upon convergence, binocular disparity, motion parallax, and other visual cues; and so on. Bayesian models illuminate the computations through which the perceptual system combines sensory cues. I review key aspects of these models. Based on my review, I argue that we should posit co-referring perceptual representations corresponding to distinct sensory cues. For example, the perceptual system represents a distal size using a representation canonically linked with vision and a distinct representation canonically linked with touch. Distinct co-referring perceptual representations represent the same denotation, but they do so under different modes of presentation. Bayesian cue combination models demonstrate that psychological explanation of perception should attend to mode of presentation and not simply to denotation.

This paper discusses how to update one’s credences based on evidence that has initial probability 0. I advance a diachronic norm, Kolmogorov Conditionalization, that governs credal reallocation in many such learning scenarios. The norm is based upon Kolmogorov’s theory of conditional probability. I prove a Dutch book theorem and converse Dutch book theorem for Kolmogorov Conditionalization. The two theorems establish Kolmogorov Conditionalization as the unique credal reallocation rule that avoids a sure loss in the relevant learning scenarios.

Interventionism is a theory of causal explanation developed by Woodward and Hitchcock. I defend an interventionist perspective on the causal explanations offered within scientific psychology. The basic idea is that psychology causally explains mental and behavioral outcomes by specifying how those outcomes would have been different had an intervention altered various factors, including relevant psychological states. I elaborate this viewpoint with examples drawn from cognitive science practice, especially Bayesian perceptual psychology. I favorably compare my interventionist approach with well-known nomological and mechanistic theories of psychological explanation.

In this dissertation, I explore the relation between language and thought. I criticize theories that explain the representational dimension of language in terms of the representational dimension of thought. I also investigate the prospects for theories that treat neither thought nor language as explanatorily prior to the other. ;The first chapter, "The Diversity of Representational Content," examines one of the most important arguments for treating thought as explanatorily prior to language. Many creatures seemingly lack language while manifesting serious cognitive activity. These examples of non-linguistic cognition apparently favor treating thought as explanatorily prior to language. In response to this argument, I urge that non-linguistic creatures display proto-thought rather than genuine thought. Genuine thought involves propositional content, while proto-thought involves different types of representational content. Once we distinguish proto-thought from genuine thought, we can maintain that genuine thought intimately involves language even though proto-thought does not. ;In the next two chapters, "The Norms of Reasoned Discourse" and "Why Should I Speak the Truth?", I develop a theory of assertion. I argue that assertion falls under irreducibly linguistic norms: the norms of reasoned discourse. When speakers violate these norms, they cease to reason correctly with one another. ;In the final chapter, "The Code Conception of Language," I deploy my theory of assertion against the conception of language propounded by Paul Grice and David Lewis. Grice and Lewis hold that sentences inherit content from the thoughts they communicate. For instance, a simplified form of the view holds that the sentence "Frank is hungry" inherits content from the belief that Frank is hungry because it serves to transmit that belief. This approach, which currently enjoys widespread popularity, treats thought as explanatorily prior to language. I argue that the approach ignores the norms of reasoned discourse and therefore enshrines a false theory of assertion

Alston, Searle, and Williamson advocate the restrictive model of assertion , according to which certain constitutive assertoric norms restrict which propositions one may assert. Sellars and Brandom advocate the dialectical model of assertion , which treats assertion as constituted by its role in the game of giving and asking for reasons. Sellars and Brandom develop a restrictive version of the dialectical model. I explore a non-restrictive version of the dialectical model. On such a view, constitutive assertoric norms constrain how one must react if an interlocutor challenges one's assertion, but they do not constrain what one should assert in the first place. I argue that the non-restrictive dialectical perspective can accommodate various linguistic phenomena commonly taken to support the restrictive model. 1.

Many philosophers worry that the classical computational theory of mind (CTM) engenders epiphenomenalism. Building on Block’s (1990) discussion, I formulate a particularly troubling version of this worry. I then present a novel solution to CTM’s epiphenomenalist conundrum. I develop my solution within an interventionist theory of causal relevance. My solution departs substantially from orthodox versions of CTM. In particular, I reject the widespread picture of digital computation as formal syntactic manipulation.1

Sensorimotor psychology studies the mental processes that control goal-directed bodily motion. Recently, sensorimotor psychologists have provided empirically successful Bayesian models of motor control. These models describe how the motor system uses sensory input to select motor commands that promote goals set by high-level cognition. I highlight the impressive explanatory benefits offered by Bayesian models of motor control. I argue that our current best models assign explanatory centrality to a robust notion of mental representation. I deploy my analysis to defend intentional realism, to rebut eliminativism and instrumentalism regarding mental representation, and to explore the relation between intentionality and normativity