Acquisition of concepts with characteristic and defining features

Acquisition of Concepts with Characteristic and Defining Features Thomas R. Shultz (thomas.shultz@mcgill.ca) Department of Psychology and School of Computer Science, McGill University, 1205 Penfield Avenue Montreal, QC H3A 1B1 Canada Jean-Philippe Thivierge (jthivier@indiana.edu) Department of Psychological and Brain Sciences, Indiana University, 1101 East Tenth Street Bloomington, IN 47408 USA Kristin Laurin (klaurin@artsmail.uwaterloo.ca) Department of Psychology, University of Waterloo, 200 University Avenue West Waterloo, ON N2L 3G1 Canada Such confusion would be particularly acute early in concept acquisition when neither version of a concept is fully in place. Another problem with the dual view is difficulty in accounting for the developmental shift from probabilistic to defining features. If defining and characteristic versions of concepts reside in different systems that are represented in incompatible formats governed by distinct principles, how do the two systems interact and how does one eventually overtake the other? A similar issue played out in the area of past-tense acquisition, where a dual view could not explain migration between regular and irregular verb classes over the history of English, a migration that was naturally simulated within a homogenous neural network (Hare & Elman, 1995). In contrast to the dual-system approach, there may be a continuum of concept crispness, perhaps covered by a more unified theory. A concept could have many features, including some that are defining and others that are probabilistic. It would be parsimonious for both sorts of features to be represented in the same fashion, with the likelihood of a feature appearing in any exemplar varying probabilistically. A defining feature appears in examples with a probability of 1, whereas a characteristic feature appears with a still substantial probability of less than 1. It is reasonable to suppose that exemplar classification is affected by both the isolation of concepts in a multidimensional feature space and by the dispersion of exemplars in this space. That is, fuzzier concepts would be characterized by residing in a relatively crowded region of feature space and by having more widely dispersed examples; crisper concepts by residence in a relatively isolated region of feature space and by limited dispersion of examples. Exemplars of an isolated concept are unlikely to be confused with those of other concepts, whereas exemplars of crowded concepts could easily be misclassified, particularly if they are loosely dispersed around their prototypes. This notion of conceptual crowding is consistent with the idea that concepts are not randomly distributed in a multidimensional feature space, but rather may cluster in regions (Pinker & Prince, 1996), sometimes called consequential regions (Shepard, A psychologically-documented case of exemplar dispersion concerns the difference between jars and bottles (Malt, Abstract Concepts with defining features have sufficiently rigid boundaries that examples of one concept are rarely confused with another concept, whereas probabilistic concepts have vague boundaries, producing frequent misclassifications. It has been argued that, although neural learning might account for probabilistic (or fuzzy) concepts, it is incapable of accounting for acquisition of concepts with defining features. Simulations reported here with encoder networks call this view into question. These sibling-descendant cascade-correlation networks account for a variety of phenomena with concepts positioned along the probabilistic to defining-features continuum. Classification of exemplars by our networks was affected by both isolation of the concept in semantic feature space and by dispersion of its examples around a prototype. The detection of defining features cured networks from the effects of conceptual crowding and example dispersion. Simulations captured the developmental shift from characteristic to defining features in a natural way. Keywords: Characteristic-to-defining shift; acquisition; defining features; neural networks. concept Introduction A major dichotomy has been proposed between concepts with characteristic versus defining features. Concepts with defining features have such rigid boundaries that exemplars of one are only rarely confused with another. In contrast, probabilistic concepts have vague and overlapping boundaries, often making exemplars difficult to classify. It has been argued that, although neural learning might provide an adequate account of fuzzy concepts, it cannot account for the acquisition and representation of concepts with defining features, such as kinship terms (Armstrong, Gleitman, & Gleitman, 1983; Pinker, 1997). Some suggest instead dualistic theories – a neural theory for probabilistic concepts and a symbolic theory for defining-feature concepts (Pinker & Prince, 1996). Moreover, there is an interesting developmental shift from emphasis on characteristic to defining features (Keil & Batterman, 1984). The dual-theory approach has a number of problems that have not been solved. One problem is that a person would not likely know which concept to access, the defining-feature one or the probabilistic one, for any particular example or context.