INTRODUCTION: TWO KINDS OF RLDUCTIONISM The man who laughs is the one who has not yet heard the terrible news. BERTHOLD BRECHT I propose, in this book, ...

This volume argues for a new image of science that understands both natural and social phenomena to be the product of mechanisms, casting the work of science as an effort to understand those mechanisms. Glennan offers an account of the nature of mechanisms and of the models used to represent them in physical, life, and social sciences.

An individual has a theory of mind if he imputes mental states to himself and others. A system of inferences of this kind is properly viewed as a theory because such states are not directly observable, and the system can be used to make predictions about the behavior of others. As to the mental states the chimpanzee may infer, consider those inferred by our own species, for example, purpose or intention, as well as knowledge, belief, thinking, doubt, guessing, pretending, liking, (...) and so forth. To determine whether or not the chimpanzee infers states of this kind, we showed an adult chimpanzee a series of videotaped scenes of a human actor struggling with a variety of problems. Some problems were simple, involving inaccessible food as in the original Kohler problems; others were more complex, involving an actor unable to extricate himself from a locked cage, shivering because of a malfunctioning heater, or unable to play a phonograph because it was unplugged. With each videotape the chimpanzee was given several photographs, one a solution to the problem, such as a stick for the inaccessible bananas, a key for the locked up actor, a lit wick for the malfunctioning heater. The chimpanzee's consistent choice of the correct photographs can be understood by assuming that the animal recognized the videotape as representing a problem, understood the actor's purpose, and chose alternatives compatible with that purpose. (shrink)

According to some philosophers, computational explanation is proprietary
to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline some promising answers that (...) are being developed by a number of authors. (shrink)

This paper offers an account of what it is for a physical system to be a computing mechanism—a system that performs computations. A computing mechanism is a mechanism whose function is to generate output strings from input strings and (possibly) internal states, in accordance with a general rule that applies to all relevant strings and depends on the input strings and (possibly) internal states for its application. This account is motivated by reasons endogenous to the philosophy of computing, namely, doing (...) justice to the practices of computer scientists and computability theorists. It is also an application of recent literature on mechanisms, because it assimilates computational explanation to mechanistic explanation. The account can be used to individuate computing mechanisms and the functions they compute and to taxonomize computing mechanisms based on their computing power. (shrink)

Finding things funny is a pervasive aspect of human mental and social life, but humor has been neglected in the philosophy of mind and cognitive science. Recently, however, there has been a swell of interest in the topic. This essay critically introduces and evaluates contemporary developments in the field, and generates an associated list of questions that a successful theory of humor should be able to answer.

"The book's qualities are, first, its scope and persuasiveness. The whole book demonstrates the seriousness of humor and its central place in human life. I know of no comparable work.

The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...) also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists. (shrink)

In earlier work I have claimed that emotion and some emotions are not `natural kinds'. Here I clarify what I mean by `natural kind', suggest a new and more accurate term, and discuss the objection that emotion and emotions are not descriptive categories at all, but fundamentally normative categories.

I argue that of the four kinds of quantitative description relevant for understanding brain function, a control theoretic approach is most appealing. This argument proceeds by comparing computational, dynamical, statistical and control theoretic approaches, and identifying criteria for a good description of brain function. These criteria include providing useful decompositions, simple state mappings, and the ability to account for variability. The criteria are justified by their importance in providing unified accounts of multi-level mechanisms that support intervention. Evaluation of the four (...) kinds of description with respect to these criteria supports the claim that control theoretic characterizations of brain function are the kind of quantitative description we ought to provide.Keywords: Neural computation; Control theory; Neural representation; Cognitive architecture; Cognitive models; Theoretical neuroscience. (shrink)

Cognitive Science combines the interdisciplinary streams of cognitive science into a unified narrative in an all-encompassing introduction to the field. This text presents cognitive science as a discipline in its own right, and teaches students to apply the techniques and theories of the cognitive scientist's 'toolkit' - the vast range of methods and tools that cognitive scientists use to study the mind. Thematically organized, rather than by separate disciplines, Cognitive Science underscores the problems and solutions of cognitive science, rather than (...) those of the subjects that contribute to it - psychology, neuroscience, linguistics, etc. The generous use of examples, illustrations, and applications demonstrates how theory is applied to unlock the mysteries of the human mind. Drawing upon cutting-edge research, the text has been updated and enhanced to incorporate new studies and key experiments since the first edition. A new chapter on consciousness has also been added. (shrink)

In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work in (...) computational neuroscience that creates difficulties for the mechanist project. Carandini and Heeger (Nat Rev Neurosci 13:51–62, 2012) propose that many neural response properties can be understood in terms of canonical neural computations. These are “standard computational modules that apply the same fundamental operations in a variety of contexts.” Importantly, these computations can have numerous biophysical realisations, and so straightforward examination of the mechanisms underlying these computations carries little explanatory weight. Through a comparison between this modelling approach and minimal models in other branches of science, I argue that computational neuroscience frequently employs a distinct explanatory style, namely, efficient coding explanation. Such explanations cannot be assimilated into the mechanistic framework but do bear interesting similarities with evolutionary and optimality explanations elsewhere in biology. (shrink)

A variety of scientific disciplines have set as their task explaining mental activities, recognizing that in some way these activities depend upon our brain. But, until recently, the opportunities to conduct experiments directly on our brains were limited. As a result, research efforts were split between disciplines such as cognitive psychology, linguistics, and artificial intelligence that investigated behavior, while disciplines such as neuroanatomy, neurophysiology, and genetics experimented on the brains of non-human animals. In recent decades these disciplines integrated, and with (...) the advent of techniques for imaging activity in human brains, the term cognitive neuroscience has been applied to the integrated investigations of mind and brain. This book is a philosophical examination of how these disciplines continue in the mission of explaining our mental capacities. (shrink)

The book stands somewhat apart from the rest of Freud's writings as a study of normal, rather than pathological psychology, and, although it contains the most closely reasoned accounts of complicated psychological processes that Freud ever gave, it remains one of his most readable works. It includes a rich collection of jokes, particularly those of Jewish folk tradition, in which Freud clearly revelled.

This book assesses the adequacy of the traditional theories of laughter and humor, suggests revised theories, and explores such areas as the aesthetics and ethics of humor, and the relation of amusement to other mental states. Theories of laughter and humor originated in ancient times with the view that laughter is an expression of feelings of superiority over another person. This superiority theory was held by Plato, Aristotle, and Hobbes. Another aspect of laughter, noted by Aristotle and Cicero and neglected (...) until Kant and Schopenhauer developed it into the incongruity theory, is that laughter is often a reaction to the perception of some incongruity. According to the third and latest traditional theory, the relief theory of Herbert Spencer and Freud, laughter is the venting of superfluous nervous energy. Historical examples of all these theories are presented along with hybrid theories such as those of Descartes and Bergson. The book also features traditional explorations of the place of humor in aesthetics, drama, and literature. This is the first work in the last fifty years to include the classic sources in the philosophy of humor and the first to present theories by contemporary philosophers. (shrink)

This systematic investigation of computation and mental phenomena by a noted psychologist and computer scientist argues that cognition is a form of computation, that the semantic contents of mental states are encoded in the same general way as computer representations are encoded. It is a rich and sustained investigation of the assumptions underlying the directions cognitive science research is taking. 1 The Explanatory Vocabulary of Cognition 2 The Explanatory Role of Representations 3 The Relevance of Computation 4 The Psychological Reality (...) of Programs: Strong Equivalence 5 Constraining Functional Architecture 6 The Bridge from Physical to Symbolic: Transduction 7 Functional Architecture and Analogue Processes 8 Mental Imagery and Functional Architecture 9 Epilogue: What is Cognitive Science the Science of? (shrink)

Paul E. Griffiths argues that most research on the emotions has been as misguided as Aristotelian efforts to study "superlunary objects" - objects...

The properties of Turing’s famous ‘universal machine’ has long sustained functionalist intuitions about the nature of cognition. Here, I show that there is a logical problem with standard functionalist arguments for multiple realizability. These arguments rely essentially on Turing’s powerful insights regarding computation. In addressing a possible reply to this criticism, I further argue that functionalism is not a useful approach for understanding what it is to have a mind. In particular, I show that the difficulties involved in distinguishing implementation (...) from function make multiple realizability claims untestable and uninformative. As a result, I conclude that the role of Turing machines in philosophy of mind needs to be reconsidered. (shrink)

The study of psychological mechanisms is an interdisciplinary endeavour, requiring insights from many different domains. In this article, I argue that philosophy plays an essential role in this interdisciplinary project, and that effective scientific study of psychological mechanisms requires that working scientists be responsible metaphysicians. This means adopting deliberate metaphysical positions when studying mechanisms that go beyond what is empirically justified regarding the nature of the phenomenon being studied, the conditions of its occurrence, and its boundaries. Such metaphysical commitments are (...) necessary in order to set up experimental protocols, determine which variables to manipulate under experimental conditions, and which conclusions to draw from different scientific models and theories. It is important for scientists to be aware of the metaphysical commitments they adopt, since they can easily be led astray if invoked carelessly. (shrink)