Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components - not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.

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Perceptual symbol systems.

The purpose of this article is to review basic empirical results from the psychological literature in a way that provides a useful foundation for research on consumer knowledge. A conceptual organization for this diverse literature is provided by two fundamental distinctions. First, consumer expertise is distinguished from product-related experience. Second, five distinct aspects, or dimensions, of expertise are identified: cognitive effort, cognitive structure, analysis, elaboration, and memory. Improvements in the first two dimensions are shown to have general beneficial effects on the latter three. Analysis, elaboration, and memory are shown to have more specific interrelationships. The empirical findings related to each dimension are reviewed and, on the basis of those findings, specific research hypotheses about the effects of expertise on consumer behavior are suggested.

Dimensions of Consumer Expertise

Psychology of Learning and Motivation publishes empirical and theoretical contributions in cognitive and experimental psychology, ranging from classical and instrumental conditioning to complex learning and problem solving. Each chapter thoughtfully integrates the writings of leading contributors, who present and discuss significant bodies of research relevant to their discipline. Volume 62 includes chapters on such varied topics as automatic logic and effortful beliefs, complex learning and development, bias detection and heuristics thinking, perceiving scale in real and virtual environments, using multidimensional encoding and retrieval contexts to enhance our understanding of source memory, causes and consequences of forgetting in thinking and remembering and people as contexts in conversation. * Volume 62 of the highly regarded Psychology of Learning and Motivation series* An essential reference for researchers and academics in cognitive science* Relevant to both applied concerns and basic research

The Psychology of Learning and Motivation: Advances in Research and Theory

Distinctions have been proposed between systems of reasoning for centuries. This article distills properties shared by many of these distinctions and characterizes the resulting systems in light of recent findings and theoretical developments. One system is associative because its computations reflect similarity structure and relations of temporal contiguity. The other is "rule based" because it operates on symbolic structures that have logical content and variables and because its computations have the properties that are normally assigned to rules. The systems serve complementary functions and can simultaneously generate different solutions to a reasoning problem. The rule-based system can suppress the associative system but not completely inhibit it. The article reviews evidence in favor of the distinction and its characterization. One of the oldest conundrums in psychology is whether people are best conceived as parallel processors of information who operate along diffuse associative links or as analysts who operate by deliberate and sequential manipulation of internal representations. Are inferences drawn through a network of learned associative pathways or through application of a kind of"psychologic" that manipulates symbolic tokens in a rule-governed way? The debate has raged (again) in cognitive psychology for almost a decade now. It has pitted those who prefer models of mental phenomena to be built out of networks of associative devices that pass activation around in parallel and distributed form (the way brains probably function) against those who prefer models built out of formal languages in which symbols are composed into sentences that are processed sequentially (the way computers function). An obvious solution to the conundrum is to conceive of the

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The empirical case for two systems of reasoning.

The authors describe a model of autobiographical memory in which memories are transitory mental constructions within a self-memory system (SMS). The SMS contains an autobiographical knowledge base and current goals of the working self. Within the SMS, control processes modulate access to the knowledge base by successively shaping cues used to activate autobiographical memory knowledge structures and, in this way, form specific memories. The relation of the knowledge base to active goals is reciprocal, and the knowledge base "grounds" the goals of the working self. It is shown how this model can be used to draw together a wide range of diverse data from cognitive, social, developmental, personality, clinical, and neuropsychological autobiographical memory research.

The construction of autobiographical memories in the self-memory system

Novices attempting to solve a problem often are reminded of an earlier problem that illustrated a principle. Two experiments examined how these earlier problems are used and how this use is related to these remindings. Subjects studied four probability principles with related word problems. Test problems varied in their similarity to the study problems on story lines, objects, and correspondence of objects (variable roles). Experiment 1 tested whether remindings cue the principle or serve as the sources of detailed analogies. When the appropriate formula was provided with each test, the similarity of story lines had no effect, but object correspondences had a large effect. These results support an analogical account in which mapping is affected by the similarity of objects between study and test problems. Experiment 2 began to separate the aspects of similarity affecting the access and use of earlier problems by showing that, with confusable principles, similar story lines increased the access, but did not affect the use. The access appears to be sensitive to the relative similarity of examples because with distinctive principles, similar story lines had little effect. Discussion focuses on the further specification of the processes of noticing and analogical use of earlier problems.

This is like that: The use of earlier problems and the separation of similarity effects.

Distinguishing Types of Superficial Similarities: Different Effects on the Access and Use of Earlier Problems

Remindings and their effects in learning a cognitive skill.

Food for Thought: Cross-Classification and Category Organization in a Complex Real-World Domain.

Categorization models based on laboratory research focus on a narrower range of explanatory constructs than appears necessary for explaining the structure of natural categories. This mismatch is caused by the reliance on classification as the basis of laboratory studies. Category representations are formed in the process of interacting with category members. Thus, laboratory studies must explore a range of category uses. The authors review the effects of a variety of category uses on category learning. First, there is an extensive discussion contrasting classification with a predictive inference task that is formally equivalent to classification but leads to a very different pattern of learning. Then, research on the effects of problem solving, communication, and combining inference and classification is reviewed.

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Brian H. Ross

Papers

This is like that: The use of earlier problems and the separation of similarity effects.

Distinguishing Types of Superficial Similarities: Different Effects on the Access and Use of Earlier Problems

Remindings and their effects in learning a cognitive skill.

Food for Thought: Cross-Classification and Category Organization in a Complex Real-World Domain.

Category Use and Category Learning