The Resilience of Computationalism

Computationalism—the view that cognition is computation—has always been controversial. It faces two types of objection. According to insufficiency objections, computation is insufficient for some cognitive phenomenon X. According to objections from neural realization, cognitive processes are realized by neural processes, but neural processes have feature Y and having Y is incompatible with being (or realizing) computations. In this paper, I explain why computationalism has survived these objections. Insufficiency objections are at best partial: for all they establish, computation may be sufficient for cognitive phenomena other than X, may be part of the explanation for X, or both. Objections from neural realization are based either on a false contrast between feature Y and computation or on an account of computation that is too vague to yield the desired conclusion. To adjudicate the dispute between computationalism and its foes, I will conclude that we need a better account of computation

Scientific Methods Must Be Public, and Descriptive Experience Sampling Qualifies

Hurlburt and Schwitzgebel’s groundbreaking book, Describing Inner Experience: Proponent Meets Skeptic, examines a research method called Descriptive Experience Sampling (DES). DES, which was developed by Hurlburt and collaborators, works roughly as follows. An investigator gives a subject a random beeper. During the day, as the subject hears a beep, she writes a description of her conscious experience just before the beep. The next day, the investigator interviews the subject, asks for more details, corrects any apparent mistakes made by the subject, and draws conclusions about the subject’s mind. Throughout the book, Schwitzgebel challenges some of Hurlburt’s specific conclusions. Yet both agree – and so do I – that DES is a worthy method

Two Kinds of Concept: Implicit and Explicit

In his refreshing and thought-provoking book, Edouard Machery (2009) argues that people possess different kinds of concept. This is probably true and important. Before I get to that, I will briefly disagree on two other points

Computation vs. Information Processing: Why Their Difference Matters to Cognitive Science

Since the cognitive revolution, it’s become commonplace that cognition involves both computation and information processing. Is this one claim or two? Is computation the same as information processing? The two terms are often used interchangeably, but this usage masks important differences. In this paper, we distinguish information processing from computation and examine some of their mutual relations, shedding light on the role each can play in a theory of cognition. We recommend that theorists of cognition be explicit and careful in choosing\ud notions of computation and information and connecting them together. Much confusion can be avoided by doing so

Information Processing, Computation and Cognition

Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism and connectionism/computational neuroscience on the other. We defend the relevance to cognitive science of both computation, at least in a generic sense, and information processing, in three important senses of the term. Our account advances several foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates’ empirical aspects

The Mind as Neural Software? Understanding Functionalism, Computationalism, and Computational Functionalism

Defending or attacking either functionalism or computationalism requires clarity on what they amount to and what evidence counts for or against them. My goal here is not to evaluate their plausibility. My goal is to formulate them and their relationship clearly enough that we can determine which type of evidence is relevant to them. I aim to dispel some sources of confusion that surround functionalism and computationalism, recruit recent philosophical work on mechanisms and computation to shed light on them, and clarify how functionalism and computationalism may or may not legitimately come together.\u

Recovering What Is Said with Empty Names

Millians hold that the meaning of a proper name is just its referent. Empty names have no referent. Accordingly, assuming semantic compositionality, utterances containing empty names have no literal, truth-evaluable meaning. Nothing truth-evaluable is said, in Grice’s sense, by such utterances. But the intuitions of ordinary speakers are that utterances containing empty names are meaningful. In recent years, some Millians have attempted to explain such ordinary intuitions in terms of what is pragmatically imparted by sentences containing empty names. We argue that this strategy fails. We show that when nothing truth-evaluable is said by an utterance, intuitions to this effect can be recovered by speakers. But no such intuitions appear to be recoverable for sentences containing empty names. Furthermore, we argue that alternative Millian strategies for dealing with empty names are even worse off. Unlike Millianism, a good semantic theory should assign meaning to both full and empty names