Four-month-old infants sometimes can perceive the unity of a partly hidden object. In each of a series of experiments, infants were habituated to one object whose top and bottom were visible but whose center was occluded by a nearer object. They were then tested with a fully visible continuous object and with two fully visible object pieces with a gap where the occluder had been. Pattems of dishabituation suggested that infants perceive the boundaries of a partly hidden object by analyzing (...) the movements of its surfaces: infants perceived a connected object when its ends moved in a common translation behind the occluder. Infants do not appear to perceive a connected object by analyzing the colors and forms of surfaces: they did not perceive a connected object when its visible parts were stationary, its color was homogeneous, its edges were aligned, and its shape was simple and regular. These findings do not support the thesis, from gestalt psychology, that object perception first arises as a consequence of a tendency to perceive the simplest, most regular configuration, or the Piagetian thesis that object perception depends on the prior coordination of action. Perception of objects may depend on an inherent conception of what an object is. (shrink)

Learning in educational settings most often emphasizes declarative and procedural knowledge. Studies of expertise, however, point to other, equally important components of learning, especially improvements produced by experience in the extraction of information: Perceptual learning. Here we describe research that combines principles of perceptual learning with computer technology to address persistent difficulties in mathematics learning. We report three experiments in which we developed and tested perceptual learning modules to address issues of structure extraction and fluency in relation to algebra and (...) fractions. PLMs focus students’ learning on recognizing and discriminating, or mapping key structures across different representations or transformations. Results showed significant and persisting learning gains for students using PLMs. PLM technology offers promise for addressing neglected components of learning: Pattern recognition, structural intuition, and fluency. Using PLMs as a complement to other modes of instruction may allow students to overcome chronic problems in learning. (shrink)

Schyns, Goldstone & Thibaut argue that categorization experience results in the learning of new perceptual features that are not derivable from the learner's existing feature set. We explore the meaning and implications of this “nonderivability” claim and relate it to the question of whether perceptual invariants are learnable, and if so, what might be entailed in learning them.

Deep convolutional networks exceed humans in sensitivity to local image properties, but unlike biological vision systems, do not discover and encode abstract relations that capture important properties of objects and events in the world. Coupling network architectures with additional machinery for encoding abstract relations will make deep networks better models of human abilities and more versatile and capable artificial devices.