Lucia M. Vaina Dept. of Biomedical Engineering Boston University Boston, MA 02215

Perceptual learning is defined as fast improvement in performance and retention of the learned ability over a period of time. In a set of psy(cid:173) chophysical experiments we demonstrated that perceptual learning oc(cid:173) curs for the discrimination of direction in stochastic motion stimuli. Here we model this learning using two approaches: a clustering model that learns to accommodate the motion noise, and an averaging model that learns to ignore the noise. Simulations of the models show performance similar to the psychophysical results.

This article addresses the problem of the recognition of the usefulness of man-made objects in actions. In a recent article, [L. M. Vaina and M. C. Jaulent, Intelligent System Journal 6, 313-336 (1991)] we defined the functional recognition problem in terms of the compatibility between the primary functions of objects and action requirements. We proposed a model, based on fuzzy sets and possibility theory, for the organization of this knowledge and the evaluation of the compatibility to perform functional recognition. In this article, we present an implementation of functional compatibility which takes the functional descriptions of objects and the object requirements of an action as inputs, and maps them into an output space that evaluates their functional compatibility. © 1994 John Wiley & Sons, Inc.

For an intelligent entity to carry out tasks in the real world, perceived three-dimensional shapes are transformed into objects identified by their functional category which makes explicit the roles or uses of objects in actions. This study describes an approach to the recognition of functions that combines ideas about representations of shapes, concepts, and object categories, with goal requirements of actions. A particular conceptual model of the compatibility between objects and actions is introduced, the outline of the solution is given, and the experimental domain of hand actions and of objects useful in such actions is described; the solution is currently under implementation and computational verifications. the article is organized as follows: first, the computational definition of the problem of functional recognition and a comprehensive theoretical framework for it is given and, second, the relation between primary functions of hand-manipulable objects and auxillary functions is discussed and modeled in the framework of fuzzy sets and possibility theory.

Humans can recover the three-dimensional structure of moving objects from their changing two-dimensional retinal image, in the absence of other cues to three-dimensional structure (Wallach and O'Connell 1953; Braunstein 1976). In this paper, we describe a patient, A.F., with bilateral lesions involving the visual cortex who is severely impaired on computing local-speed and global-motion fields, but who can recover structure from motion. The data suggest that although possibly useful, global-motion fields are not necessary for deriving structure from motion. We discuss these results from the perspective of theoretical models for this computation.