Over recent years, the psychology of concepts has been rejuvenated by new work on prototypes, inventive ideas on causal cognition, the development of neo-empiricist theories of concepts, and the inputs of the budding neuropsychology of concepts. But our empirical knowledge about concepts has yet to be organized in a coherent framework. -/- In Doing without Concepts, Edouard Machery argues that the dominant psychological theories of concepts fail to provide such a framework and that drastic conceptual changes are required to make (...) sense of the research on concepts in psychology and neuropsychology. Machery shows that the class of concepts divides into several distinct kinds that have little in common with one another and that for this very reason, it is a mistake to attempt to encompass all known phenomena within a single theory of concepts. In brief, concepts are not a natural kind. Machery concludes that the theoretical notion of concept should be eliminated from the theoretical apparatus of contemporary psychology and should be replaced with theoretical notions that are more appropriate for fulfilling psychologists' goals. The notion of concept has encouraged psychologists to believe that a single theory of concepts could be developed, leading to useless theoretical controversies between the dominant paradigms of concepts. Keeping this notion would slow down, and maybe prevent, the development of a more adequate classification and would overshadow the theoretical and empirical issues that are raised by this more adequate classification. Anyone interested in cognitive science's emerging view of the mind will find Machery's provocative ideas of interest. (shrink)

Are there really beliefs? Or are we learning (from neuroscience and psychology, presumably) that, strictly speaking, beliefs are figments of our imagination, items in a superceded ontology? Philosophers generally regard such ontological questions as admitting just two possible answers: either beliefs exist or they don't. There is no such state as quasi-existence; there are no stable doctrines of semi-realism. Beliefs must either be vindicated along with the viruses or banished along with the banshees. A bracing conviction prevails, then, to the (...) effect that when it comes to beliefs (and other mental items) one must be either a realist or an eliminative materialist. (shrink)

In domains with multiple competing goals, people face a basic challenge: How to make their strategy use flexible enough to deal with shifting circumstances without losing track of their overall objectives. This article examines how young children meet this challenge in one such domain, tic‐tac‐toe. Experiment 1 provides an overviews of development in the area; it indicates that children's tic‐tac‐toe strategies are rule based and that new rules are added one at a time. Experiment 2 demonstrates that even young children (...) flexibly tailor their strategy use to meet shifting circumstances. Experiment 3 indicates that these adaptations are accomplished through a process of goal‐based resource allocation, whereby children focus their cognitive resources on applying rules most consistent with their current primary goal. A computer simulation specifies how this process works and demonstrates its sufficiency for producing behavior much like that of the children. The findings are discussed as part of a broader framework of mechanisms for generating problem‐solving approaches. (shrink)

This article outlines the main themes and motivations of Carruthers, 2006. Its purpose is to provide some background for the critical commentaries of Cowie, Machery, and Wilson (this volume).

What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. (...) But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to. (shrink)

Together, these collected papers develop the idea that human thinking - from scientific creativity to simply understanding what a positive HIV test means - "happens" partly outside the mind.".

This is the classic work upon which modern-day game theory is based. What began as a modest proposal that a mathematician and an economist write a short paper together blossomed, when Princeton University Press published Theory of Games and Economic Behavior. In it, John von Neumann and Oskar Morgenstern conceived a groundbreaking mathematical theory of economic and social organization, based on a theory of games of strategy. Not only would this revolutionize economics, but the entirely new field of scientific inquiry (...) it yielded--game theory--has since been widely used to analyze a host of real-world phenomena from arms races to optimal policy choices of presidential candidates, from vaccination policy to major league baseball salary negotiations. And it is today established throughout both the social sciences and a wide range of other sciences. (shrink)

I consider how several results from cognitive science bear on the nature of representation and how representations might be structured. Distinguishing two notions of representation, presentations, which are cases of direct sensing, and symbols, which stand in for something else, I argue that only symbols pose a philosophical problem for naturalizing content. What is required is an account of how one thing can stand in for another. Milner and Goodale’s dual route model of vision offers a model for this ‘stand-in’ (...) relation. Following this model, symbols must play a functional role of activating cognitive operations relevant to what the symbols stand in for. What emerges is an interconnected network of symbols whose tokenings are central – as opposed to modular – cognitive operations. A consequence of this view, which I call an acquired language of thought (ALOT) since most of the symbols humans use are the words of a natural language, is that the framework for central cognition is constructed during the acquisition of a natural language lexicon.Presentations and Symbols: What Cognition Requires of Representationalism. (shrink)

Bounded rationality is a fundamental feature of cognition. We make choices between alternatives in light of our goals, relying on incomplete information and limited resources. As a consequence, PROBLEM SOLVING cannot be exhaustive: we cannot explore all the possibilities which confront us, and search must be constrained in ways that facilitate search efficiency even at the expense of search effectiveness. If we think of problem solving as a search through the space of possibilities as it was conceptualized by Allen Newell (...) and Herbert A. Simon, limitations on search entail that for all but the simplest problems we can investigate at most a small proportion of the possibilities. Moreover, our evaluation of these possibilities will be uncertain and incomplete. We must rely on heuristic methods for pruning the tree of possibilities and for evaluating the possibilities we consider. In medical diagnosis, for example, the problem is one of inferring what disease is present from a given set of symptoms, when no one symptom is invariably present with a given disease, and the same symptom can have various causes. Moreover, patients can be suffering from more than one disease, a fact which compounds the difficulties of accurate diagnosis. Given that symptoms cannot generally be assumed to be independent and are noisy indicators of the underlying disease, the size of the search space turns out to be large, and inference concerning underlying diseases is inevitably uncertain. One solution is to limit the range of the search, eliminating some candidates as implausible; that is, we may adopt assumptions limiting the range of diseases considered to a narrow range of those antecedently likely. Good diagnosticians evidently narrow the range to no more than a handful of candidates. Search must be limited in order to be efficient. (shrink)

In Furnishing the Mind, Jesse Prinz attempts to swing the pendulum back toward empiricism.

Contemporary cognitive science has two principal branches: the classical computational approach (sometimes called classicism) and connectionism. Rules are fundamental to theorizing about the basic units of processing in both classicism and connectionism. But we will be concerned primarily with rules that apply to representations and that determine transitions from one cognitive/representational state to the next. Such rules are fundamental to classicism, since, according to classicism, cognitive processes simply are rule‐governed cognitive state transitions. Rules that apply to representations are not a (...) built‐in feature of connectionist architecture, so the role of such rules in connectionism is less clear. We will argue that they are neither necessary nor (in general) desirable in connectionism. We discuss the status of rules first in classicism, then in connectionism. (shrink)

The point of this paper is to provide a principled framework for a naturalistic, interactivist-constructivist model of rational capacity and a sketch of the model itself, indicating its merits. Being naturalistic, it takes its orientation from scientific understanding. In particular, it adopts the developing interactivist-constructivist understanding of the functional capacities of biological organisms as a useful naturalistic platform for constructing such higher order capacities as reason and cognition. Further, both the framework and model are marked by the finitude and fallibility (...) that science attributes to organisms, with their radical consequences, and also by the individual and collective capacities to improve their performances that learning organisms display. Part A prepares the ground for the exposition through a critique of the dominant Western analytic tradition in rationalising science, followed by a brief exposition of the naturalist framework that will be employed to frame the construction. This results in two sets of guidelines for constructing an alternative. Part B provides the new conception of reason as a rich complex of processes of improvement against epistemic values, and argues its merits. It closes with an account of normativity and our similarly developing rational knowledge of it, including (reflexively) of reason itself. (shrink)

Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon's notion of satisficing, the authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up nor integrate all information. By computer simulation, the (...) authors held a competition between the satisficing "Take The Best" algorithm and various "rational" inference procedures. The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference. (shrink)

Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We review the major progress made so far: the discovery of less-is-more effects; the study of the ecological rationality of heuristics, which examines in which environments a given strategy succeeds or fails, and why; an advancement from vague labels to computational models of heuristics; the (...) development of a systematic theory of heuristics that identifies their building blocks and the evolved capacities they exploit, and views the cognitive system as relying on an “adaptive toolbox;” and the development of an empirical methodology that accounts for individual differences, conducts competitive tests, and has provided evidence for people’s adaptive use of heuristics. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies. (shrink)

A general two-stage theory of human inference is proposed. A distinction is drawn between heuristic processes which select items of task information as ‘relevant’, and analytic processes which operate on the selected items to generate inferences or judgements. These two stages are illustrated in a selective review of work on both deductive and statistical reasoning. Factors identified as contributing to heuristic selection include perceptual salience, linguistic suppositions and semantic associations. Analytic processes are considered to be context dependent: people reason from (...) experience, not from inference rules. The paper includes discussion of the theory in comparison with other contemporary theories of human inference, and in relation to the current debate about human rationality. (shrink)

Species pluralism gives us reason to doubt the existence of the species category. The problem is not that species concepts are chosen according to our interests or that pluralism and the desire for hierarchical classifications are incompatible. The problem is that the various taxa we call 'species' lack a common unifying feature.

In the movie The Gold Rush Charlie Chaplin and his friend are stranded in a log cabin in the middle of winter while a blizzard rages. The cabin is isolated, and they have a very big problem – there is nothing to eat. They pace around wondering what to do. Charlie's friend starts to see Charlie as a chicken, and he tries to kill him. He chases Charlie around the cabin many times. Eventually they hit upon the idea of boiling (...) an old boot and eating it for dinner. With great delicacy they sit at the table and eat the boot as if it were a gourmet meal. They solved the problem of having nothing to eat. While their solution to the problem did not result in a culinary feast, this example reveals two crucial features of problem solving. First, a problem exists when a goal must be achieved and the solution is not immediately obvious. Second, problem solving often involves attempting different ways to solve the problem. Put more formally, a problem has four components. First, there is an initial state. This is the person's state of knowledge at the start of a problem. Second, there is the goal state: the goal that the person wishes to achieve. Third are the actions or operations that the problem‐solver can use to get to the goal state. Fourth is the task environment that the solver is working in. The task environment consists of the features of the physical environment that can either directly or indirectly constrain or suggest different ways of solving a problem. I will sketch out the main currents of thinking in research in this area, beginning by reviewing the history of research on problem solving and then focusing on a number of important issues in problem‐solving research. Finally, I will give an overview of some recent developments. (shrink)

An analysis of two heuristic strategies for the development of mechanistic models, illustrated with historical examples from the life sciences. In Discovering Complexity, William Bechtel and Robert Richardson examine two heuristics that guided the development of mechanistic models in the life sciences: decomposition and localization. Drawing on historical cases from disciplines including cell biology, cognitive neuroscience, and genetics, they identify a number of "choice points" that life scientists confront in developing mechanistic explanations and show how different choices result in divergent (...) explanatory models. Describing decomposition as the attempt to differentiate functional and structural components of a system and localization as the assignment of responsibility for specific functions to specific structures, Bechtel and Richardson examine the usefulness of these heuristics as well as their fallibility—the sometimes false assumption underlying them that nature is significantly decomposable and hierarchically organized. When Discovering Complexity was originally published in 1993, few philosophers of science perceived the centrality of seeking mechanisms to explain phenomena in biology, relying instead on the model of nomological explanation advanced by the logical positivists (a model Bechtel and Richardson found to be utterly inapplicable to the examples from the life sciences in their study). Since then, mechanism and mechanistic explanation have become widely discussed. In a substantive new introduction to this MIT Press edition of their book, Bechtel and Richardson examine both philosophical and scientific developments in research on mechanistic models since 1993. (shrink)

This volume collects Gigerenzer's recent articles on the psychology of rationality. This volume should appeal, like the earlier volumes, to a broad mixture of cognitive psychologists, philosophers, economists, and others who study decision making.

Classic analysis of the subject and the development of personal probability; one of the greatest controversies in modern statistcal thought.

Jerry Fodor presents a new development of his famous Language of Thought hypothesis, which has since the 1970s been at the centre of interdisciplinary debate about how the mind works. Fodor defends and extends the groundbreaking idea that thinking is couched in a symbolic system realized in the brain. This idea is central to the representational theory of mind which Fodor has established as a key reference point in modern philosophy, psychology, and cognitive science. The foundation stone of our present (...) cognitive science is Turing's suggestion that cognitive processes are not associations but computations; and computation requires a language of thought. So the latest on the Language of Thought hypothesis, from its progenitor, promises to be a landmark in the study of the mind. LOT 2 offers a more cogent presentation and a fuller explication of Fodor 's distinctive account of the mind, with various intriguing new features. The central role of compositionality in the representational theory of mind is revealed: most of what we know about concepts follows from the compositionality of thoughts. Fodor shows the necessity of a referentialist account of the content of intentional states, and of an atomistic account of the individuation of concepts. Not least among the new developments is Fodor 's identification and persecution of pragmatism as the leading source of error in the study of the mind today. LOT 2 sees Fodor advance undaunted towards the ultimate goal of a theory of the cognitive mind, and in particular a theory of the intentionality of cognition. No one who works on the mind can ignore Fodor 's views, expressed in the coruscating and provocative style which has delighted and disconcerted countless readers over the years. (shrink)

This book offers a philosophy for error-prone humans trying to understand messy systems in the real world.

The thirty-five chapters in this book describe various judgmental heuristics and the biases they produce, not only in laboratory experiments but in important...

This book is a comprehensive development and defense of one of the guiding assumptions of evolutionary psychology: that the human mind is composed of a large number of semi-independent modules. The Architecture of the Mind has three main goals. One is to argue for massive mental modularity. Another is to answer a 'How possibly?' challenge to any such approach. The first part of the book lays out the positive case supporting massive modularity. It also outlines how the thesis should best (...) be developed, and articulates the notion of 'module' that is in question. Then the second part of the book takes up the challenge of explaining how the sorts of flexibility and creativity that are distinctive of the human mind could possibly be grounded in the operations of a massive number of modules.Peter Carruthers's third aim is to show how the various components of the mind are likely to be linked and interact with one another - indeed, this is crucial to demonstrating how the human mind, together with its familiar capacities, can be underpinned by a massively modular set of mechanisms. He outlines and defends the basic framework of a perception / belief / desire / planning / motor-control architecture, as well as detailing the likely components and their modes of connectivity. Many specific claims about the place within this architecture of natural language, of a mind-reading system, and others are explained and motivated. A number of novel proposals are made in the course of these discussions, one of which is that creative human thought depends upon a prior kind of creativity of action.Written with unusual clarity and directness, and surveying an extensive range of research in cognitive science, this book will be essential reading for anyone with an interest in the nature and organization of the mind. (shrink)

A common picture of evolution by natural selection sees it as a process through which organisms change so that they become better adapted to their environment. However, agents do not merely respond to the challenges their environments pose. They modify their environments, filtering and transforming the action of the environment on their bodies A beaver, in making a dam, engineers a stream, increasing both the size of its safe refuge and reducing its seasonal variability. Beavers, like many other animals, are (...) ecological engineers. They act to modify the physical challenges posed by their environment. Nests, burrows and other shelters reduce the impacts of adverse weather and of other agents. Animal also modify their exposure to biological risks. Hygienic behaviour reduces the impact of disease. Intensive grooming; moving to new roosts; using a. (shrink)

During the last 25 years, researchers studying human reasoning and judgment in what has become known as the “heuristics and biases” tradition have produced an impressive body of experimental work which many have seen as having “bleak implications” for the rationality of ordinary people (Nisbett and Borgida 1975). According to one proponent of this view, when we reason about probability we fall victim to “inevitable illusions” (Piattelli-Palmarini 1994). Other proponents maintain that the human mind is prone to “systematic deviations from (...) rationality” (Bazerman & Neale 1986) and is “not built to work by the rules of probability” (Gould 1992). It has even been suggested that human beings are “a species that is uniformly probability-blind” (Piattelli-Palmarini 1994). This provocative and pessimistic interpretation of the experimental findings has been challenged from many different directions over the years. One of the most recent and energetic of these challenges has come from the newly emerging field of evolutionary psychology, where it has been argued that it’s singularly implausible to claim that our species would have evolved with no “instinct for probability” and, hence, be “blind to chance” (Pinker 1997, 351). Though evolutionary psychologists concede that it is possible to design experiments that “trick our probability.. (shrink)