pragmatics, and cognitive foundations of language. Thinking and reasoning. These constitute an important domain of cognitive science that is closely linked to philosophical interests. Problem solving, such as that which figures in solving puzzles, playing games, or serving as an expert in a domain, has provided a prototype for thinking. Newell and Simon’s influential work construed problem solving as a search through a problem space and introduced the idea of heuristics – generally reliable but fallible simplifying devices to facilitate the search. One arena for problem solving, scientific reasoning and discovery, has particularly interested philosophers. Artificial intelligence researchers such as Simon and Patrick Langley, as well as philosophers such as Paul Thagard and Lindley Darden, have developed computer programs that can utilize the same data as that available to historical scientists to develop and evaluate theories and plan future experiments. Cognitive scientists have also sought to study the cognitive processes underlying the sorts of logical reasoning (both deductive and inductive) whose normative dimensions have been a concern of philosophers. Philip Johnson- Laird, for example, has sought to account for human performance in dealing with syllogistic reasoning by describing a processing of constructing and manipulating mental models. Finally, the process of constructing and using analogies is another aspect of reasoning that has been extensively studied by traditional philosophers as well as cognitive scientists.
Memory, attention, and learning. Cognitive scientists have differentiated a variety of types of memory. The distinction between long- and short-term memory was very influential in the information-processing models of the 1970s. Short-term memory was characterized by limited capacity, such as that exhibited by the ability to retain a seven-digit telephone number for a short period. In much cognitive science work, the notion of working memory has superseded short-term memory, but many theorists are reluctant to construe this as a separate memory system (as opposed to a part of long-term memory that is activated at a given time). Endel Tulving introduced a distinction between semantic memory (general knowledge that is not specific to a time or place) and episodic memory (memory for particular episodes or occurrences). More recently, Daniel Schacter proposed a related distinction that emphasizes consciousness: implicit memory (access without awareness) versus explicit memory (which does involve awareness and is similar to episodic memory). One of the interesting results of cognitive research is the dissociation between different kinds of memory: a person might have severely impaired memory of recent events while having largely unimpaired implicit memory. More generally, memory research has shown that human memory does not simply store away information as in a file cabinet. Rather, information is organized according to preexisting structures such as scripts, and can be influenced by events subsequent to the initial storage. Exactly what gets stored and retrieved is partly determined by attention, and psychologists in the information-processing tradition have sought to construct general cognitive models that emphasize memory and attention. Finally, the topic of learning has once again become prominent. Extensively studied by the behaviorists of the precognitive era, learning was superseded by memory and attention as a research focus in the 1970s. In the 1980s, artificial intelligence researchers developed a growing interest in designing systems that can learn; machine learning is now a major problem area in AI. During the same period, connectionism arose to offer an alternative kind of learning model. Perception and motor control. Perceptual and motor systems provide the inputs and outputs to cognitive systems. An important aspect of perception is the recognition of something as a particular kind of object or event; this requires accessing knowledge of objects and events. One of the central issues concerning perception questions the extent to which perceptual processes are influenced by higher-level cognitive information (top-down processing) versus how much they are driven purely by incoming sensory information (bottom-up processing). A related issue concerns the claim that visual imagery is a distinct cognitive process and is closely related to visual perception, perhaps relying on the same brain processes. A number of cognitive science inquiries (e.g., by Roger Shepard and Stephen Kosslyn) have focused on how people use images in problem solving and have sought evidence that people solve problems by rotating images or scanning them. This research has been extremely controversial, as other investigators have argued against the use of images and have tried to account for the performance data that have been generated in terms of the use of propositionally represented information. Finally, a distinction recently has been proposed between the What and Where systems. All of the foregoing issues concern the What system (which recognizes and represents objects as exemplars of categories). The Where system, in contrast, concerns objects in their environment, and is particularly adapted to the dynamics of movement. Gibson’s ecological psychology is a long-standing inquiry into this aspect of perception, and work on the neural substrates is now attracting the interest of cognitive scientists as well.
Recent developments. The breadth of cognitive science has been expanding in recent years. In the 1970s, cognitive science inquiries tended to focus on processing activities of adult humans or on computer models of intelligent performance; the best work often combined these approaches. Subsequently, investigators examined in much greater detail how cognitive systems develop, and developmental psychologists have increasingly contributed to cognitive science. One of the surprising findings has been that, contrary to the claims of William James, infants do not seem to confront the world as a ‘blooming, buzzing confusion,’ but rather recognize objects and events quite early in life. Cognitive science has also expanded along a different dimension. Until recently many cognitive studies focused on what humans could accomplish in laboratory settings in which they performed tasks isolated from reallife contexts. The motivation for this was the assumption that cognitive processes were generic and not limited to specific contexts. However, a variety of influences, including Gibsonian ecological psychology (especially as interpreted and developed by Ulric Neisser) and Soviet activity theory, have advanced the view that cognition is much more dynamic and situated in real-world tasks and environmental contexts; hence, it is necessary to study cognitive activities in an ecologically valid manner.
Another form of expansion has resulted from a challenge to what has been the dominant architecture for modeling cognition. An architecture defines the basic processing capacities of the cognitive system. The dominant cognitive architecture has assumed that the mind possesses a capacity for storing and manipulating symbols. These symbols can be composed into larger structures according to syntactic rules that can then be operated upon by formal rules that recognize that structure. Jerry Fodor has referred to this view of the cognitive system as the ‘language of thought hypothesis’ and clearly construes it as a modern heir of rationalism. One of the basic arguments for it, due to Fodor and Zenon Pylyshyn, is that thoughts, like language, exhibit productivity (the unlimited capacity to generate new thoughts) and systematicity (exhibited by the inherent relation between thoughts such as ‘Joan loves the florist’ and ‘The florist loves Joan’). They argue that only if the architecture of cognition has languagelike compositional structure would productivity and systematicity be generic properties and hence not require special case-by-case accounts. The challenge to this architecture has arisen with the development of an alternative architecture, known as connectionism, parallel distributed processing, or neural network modeling, which proposes that the cognitive system consists of vast numbers of neuronlike units that excite or inhibit each other. Knowledge is stored in these systems by the adjustment of connection strengths between processing units; consequently, connectionism is a modern descendant of associationism. Connectionist networks provide a natural account of certain cognitive phenomena that have proven challenging for the symbolic architecture, including pattern recognition, reasoning with soft constraints, and learning. Whether they also can account for productivity and systematicity has been the subject of debate. Philosophical theorizing about the mind has often provided a starting point for the modeling and empirical investigations of modern cognitive science. The ascent of cognitive science has not meant that philosophers have ceased to play a role in examining cognition. Indeed, a number of philosophers have pursued their inquiries as contributors to cognitive science, focusing on such issues as the possible reduction of cognitive theories to those of neuroscience, the status of folk psychology relative to emerging scientific theories of mind, the merits of rationalism versus empiricism, and strategies for accounting for the intentionality of mental states. The interaction between philosophers and other cognitive scientists, however, is bidirectional, and a number of developments in cognitive science promise to challenge or modify traditional philosophical views of cognition. For example, studies by cognitive and social psychologists have challenged the assumption that human thinking tends to accord with the norms of logic and decision theory. On a variety of tasks humans seem to follow procedures (heuristics) that violate normative canons, raising questions about how philosophers should characterize rationality. Another area of empirical study that has challenged philosophical assumptions has been the study of concepts and categorization. Philosophers since Plato have widely assumed that concepts of ordinary language, such as red, bird, and justice, should be definable by necessary and sufficient conditions. But celebrated studies by Eleanor Rosch and her colleagues indicated that many ordinary-language concepts had a prototype structure instead. On this view, the categories employed in human thinking are characterized by prototypes (the clearest exemplars) and a metric that grades exemplars according to their degree of typicality. Recent investigations have also pointed to significant instability in conceptual structure and to the role of theoretical beliefs in organizing categories. This alternative conception of concepts has profound implications for philosophical methodologies that portray philosophy’s task to be the analysis of concepts.