Teleosemantics, Externalism, and the Content of Theoretical Concepts

In several works, Ruth Millikan (1998a, 2000, 2006) has developed a ‘teleosemantic’ theory of concepts. Millikan’s theory has three explicit desiderata for concepts: wide scope, non-descriptionist content, and naturalism. I contend that Millikan’s theory cannot fulfill all of these desiderata simultaneously. Theoretical concepts, such as those of chemistry and physics, fall under Millikan’s intended scope, but I will argue that her theory cannot account for these concepts in a way that is compatible with both non-descriptionism and naturalism. In these cases, Millikan’s view is subject to the traditional ‘indeterminacy problem’ for teleosemantic theories. This leaves the content of theoretical concepts indeterminate between a descriptionist and non-descriptionist content. Furthermore, this problem cannot be overcome without giving up the naturalism desideratum. I suggest that the scope of Millikan’s theory should be limited. At best, the theory will be able to attribute naturalistic, non-descriptionist content to a smaller range of concepts

Fodor on imagistic mental representations

Abstract: Fodor’s view of the mind is thoroughly computational. This means that the basic kind of mental entity is a “discursive” mental representation and operations over this kind of mental representation have broad architectural scope, extending out to the edges of perception and the motor system. However, in multiple epochs of his work, Fodor attempted to define a functional role for non-discursive, imagistic representation. I describe and critique his two considered proposals. The first view says that images play a particular kind of functional role in certain types of deliberative tasks. The second says that images are solely restricted to the borders of perception, and act as a sort of medium for the fixing of conceptual reference. I argue, against the first proposal, that a broad-scope computationalism such as Fodor’s renders images in principle functionally redundant. I argue, against the second proposal, that empirical evidence suggests that non-discursive representations are learned through perceptual learning, and directly inform category judgments. In each case, I point out extant debates for which the arguments are relevant. The upshot is that there is motivation for limited scope computationalism, in which some, but not all, mental processes operate on discursive mental representations.Keywords: Computational Theory of Mind; Mental Representation; Perception; Mental Image; Jerry Fodor  Fodor e le rappresentazioni mentali come immaginiRiassunto: La concezione della mente di Fodor è rigorosamente computazionale, ossia le entità mentali di base sono rappresentazioni mentali “discorsive”. Le operazioni su queste rappresentazioni hanno un fine architettonico ampio, che va fino ai confini della percezione e del sistema motorio. In periodi diversi del suo lavoro, Fodor ha proposto due modi per definire un ruolo funzionale per la rappresentazione non-discorsiva come immagine. Tratterò criticamente entrambi. Per il primo, le immagini giocano un particolare tipo di ruolo funzionale in certi tipi di compiti deliberativi, mentre, per il secondo, sono relegate unicamente ai confini della percezione, agendo come medium per fissare il riferimento concettuale. Contro il primo sosterrò che un computazionalismo così ampio come quello di Fodor rende le immagini in principio funzionalmente ridondanti. Contro il secondo sosterrò che l’evidenza empirica suggerisce che le rappresentazioni non-discorsive vengono apprese percettivamente, agendo direttamente sui giudizi di categorizzazione. In entrambi i casi considererò gli argomenti più rilevanti nel dibattito corrente. Si vedrà che ci sono buone ragioni in favore di un computazionalismo più limitato, in cui alcuni processi mentali (ma non tutti) operano su rappresentazioni mentali discorsive.Parole chiave: Teoria computazionale della mente; Rappresentazione mentale; Percezione; Immagine mentale; Jerry Fodo

Real Patterns in Biological Explanation

In discussion of mechanisms, philosophers often debate about whether quantitative descriptions of generalizations or qualitative descriptions of operations are explanatorily fundamental. I argue that these debates have erred by conflating the explanatory roles of generalizations and patterns. Patterns are types of quantitative relationships that hold between quantities in a mechanism, over time and/or across conditions. While these patterns must often be represented in addition to descriptions of operations in order to explain a phenomenon, they are not equivalent to generalizations, because their explanatory role does not depend on any specific facts about their scope or domain of invariance

Contents, vehicles, and complex data analysis in neuroscience

The notion of representation in neuroscience has largely been predicated on localizing the components of computational processes that explain cognitive function. On this view, which I call “algorithmic homuncularism,” individual, spatially and temporally distinct parts of the brain serve as vehicles for distinct contents, and the causal relationships between them implement the transformations specified by an algorithm. This view has a widespread influence in philosophy and cognitive neuroscience, and has recently been ably articulated and defended by Shea. Still, I am skeptical about algorithmic homuncularism, and I argue against it by focusing on recent methods for complex data analysis in systems neuroscience. I claim that analyses such as principle components analysis and linear discriminant analysis prevent individuating vehicles as algorithmic homuncularism recommends. Rather, each individual part contributes to a global state space, trajectories of which vary with important task parameters. I argue that, while homuncularism is false, this view still supports a kind of “vehicle realism,” and I apply this view to debates about the explanatory role of representation

Why we may not find intentions in the brain

Intentions are commonly conceived of as discrete mental states that are the direct cause of actions. In the last several decades, neuroscientists have taken up the project of finding the neural implementation of intentions, and a number of areas have been posited as implementing these states. We argue, however, that the processes underlying action initiation and control are considerably more dynamic and context sensitive than the concept of intention can allow for. Therefore, adopting the notion of ‘intention’ in neuroscientific explanations can easily lead to misinterpretation of the data, and can negatively influence investigation into the neural correlates of intentional action.We suggest reinterpreting the mechanisms underlying intentional action, and we will discuss the elements that such a reinterpretation needs to account for

Bayes, predictive processing, and the cognitive architecture of motor control

Despite their popularity, relatively scant attention has been paid to the upshot of Bayesian and predictive processing models of cognition for views of overall cognitive architecture. Many of these models are hierarchical ; they posit generative models at multiple distinct "levels," whose job is to predict the consequences of sensory input at lower levels. I articulate one possible position that could be implied by these models, namely, that there is a continuous hierarchy of perception, cognition, and action control comprising levels of generative models. I argue that this view is not entailed by a general Bayesian/predictive processing outlook. Bayesian approaches are compatible with distinct formats of mental representation. Focusing on Bayesian approaches to motor control, I argue that the junctures between different types of mental representation are places where the transitivity of hierarchical prediction may be broken, and I consider the upshot of this conclusion for broader discussions of cognitive architecture

Pluralistic Attitude-Explanation and the Mechanisms of Intentional Action.

According to the Causal Theory of Action (CTA), genuine actions are individuated by their causal history. Actions are bodily movements that are causally explained by citing the agent’s reasons. Reasons are then explained as some combination of propositional attitudes – beliefs, desires, and/or intentions. The CTA is thus committed to realism about the attitudes. This paper explores current models of decision-making from the mind sciences, and argues that it is far from obvious how to locate the propositional attitudes in the causal processes they describe. The outcome of the analysis is a proposal for pluralism: there are several ways one could attempt to map states like “intention” onto decision-making processes, but none will fulfill all of the roles attributed to the attitudes by the CTA

Real Patterns in Biological Explanation

In discussion of mechanisms, philosophers often debate about whether quantitative descriptions of generalizations or qualitative descriptions of operations are explanatorily fundamental. I argue that these debates have erred by conflating the explanatory roles of generalizations and patterns. Patterns are types of quantitative relationships that hold between quantities in a mechanism, over time and/or across conditions. While these patterns must often be represented in addition to descriptions of operations in order to explain a phenomenon, they are not equivalent to generalizations, because their explanatory role does not depend on any specific facts about their scope or domain of invariance

Why do biologists use so many diagrams?

Diagrams have distinctive characteristics that make them an effective medium for communicating research findings, but they are even more impressive as tools for scientific reasoning. Focusing on circadian rhythm research in biology to explore these roles, we examine diagrammatic formats that have been devised (a) to identify and illuminate circadian phenomena and (b) to develop and modify mechanistic explanations of these phenomena

Why do biologists use so many diagrams?

Diagrams have distinctive characteristics that make them an effective medium for communicating research findings, but they are even more impressive as tools for scientific reasoning. Focusing on circadian rhythm research in biology to explore these roles, we examine diagrammatic formats that have been devised (a) to identify and illuminate circadian phenomena and (b) to develop and modify mechanistic explanations of these phenomena