According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation (...) and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individual's existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction. Key Words: concept learning; conceptual development; features; perceptual learning; stimulus encoding. (shrink)

Talk of linguistic universals has given cognitive scientists the impression that languages are all built to a common pattern. In fact, there are vanishingly few universals of language in the direct sense that all languages exhibit them. Instead, diversity can be found at almost every level of linguistic organization. This fundamentally changes the object of enquiry from a cognitive science perspective. This target article summarizes decades of cross-linguistic work by typologists and descriptive linguists, showing just how few and unprofound the (...) universal characteristics of language are, once we honestly confront the diversity offered to us by the world's 6,000 to 8,000 languages. After surveying the various uses of “universal,” we illustrate the ways languages vary radically in sound, meaning, and syntactic organization, and then we examine in more detail the core grammatical machinery of recursion, constituency, and grammatical relations. Although there are significant recurrent patterns in organization, these are better explained as stable engineering solutions satisfying multiple design constraints, reflecting both cultural-historical factors and the constraints of human cognition.Linguistic diversity then becomes the crucial datum for cognitive science: we are the only species with a communication system that is fundamentally variable at all levels. Recognizing the true extent of structural diversity in human language opens up exciting new research directions for cognitive scientists, offering thousands of different natural experiments given by different languages, with new opportunities for dialogue with biological paradigms concerned with change and diversity, and confronting us with the extraordinary plasticity of the highest human skills. (shrink)

Anthropologists and linguists have long been aware that the body is explicitly referred to in conventional description of emotion in languages around the world. There is abundant linguistic data showing expression of emotions in terms of their imagined ‘locus’ in the physical body. The most important methodological issue in the study of emotions is language, for the ways people talk give us access to ‘folk descriptions’ of the emotions. ‘Technical terminology’, whether based on English or otherwise, is not excluded from (...) this ‘folk’ status. It may appear to be safely ‘scientific’ and thus culturally neutral, but in fact it is not: technical English is a variety of English and reflects, to some extent, culture-specific ways of thinking (and categorising) associated with the English language. People — as researchers studying other people, or as people in real-life social association — cannot directly access the emotional experience of others, and language is the usual mode of ‘packaging’ one’s experience so it may be accessible to others. Careful description of linguistic data from as broad as possible a cross-linguistic base is thus an important part of emotion research. All people experience biological events and processes associated with certain thoughts (or, as psychologists say, ‘appraisals’), but there is more to ‘emotion’ than just these physiological phenomena. Speakers of some languages talk about their emotional experiences as if they are located in some internal organ such as ‘the liver’, yet they cannot localise feeling in this physical organ. This phenomenon needs to be understood better, and one of the problems is finding a method of comparison that allows us to compare descriptions from different languages which show apparently great formal and semantic variation. Some simple concepts including feel and body are universal or near-universal, and as such are good candidates for terms of description which may help to eradicate confusion and exoticism from cross-linguistic comparison and semantic typology. Semantic analysis reveals great variation in concepts of emotion across languages and cultures — but such analysis requires a sound and well-founded methodology. While leaving room for different approaches to the task, we suggest that such a methodology can be based on empirically established linguistic universal (or near-universal) concepts, and on ‘cognitive scenarios’ articulated in terms of these concepts. Also, we warn against the danger of exoticism involved in taking all body part references ‘literally’. Above all, we argue that what is needed is a combination of empirical cross-linguistic investigations and a theoretical and methodological awareness, recognising the impossibility of exploring other people’s emotions without keeping language in focus: both as an object and as a tool of study. (shrink)

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. (shrink)