Biomedical subjects

J E Hummel

Publications and source records attributed to J E Hummel.

3 recordsLinked to original sources

PubMed1992

Dynamic binding in a neural network for shape recognition.

Given a single view of an object, humans can readily recognize that object from other views that preserve the parts in the original view. Empirical evidence suggests that this capacity reflects the activation of a viewpoint-invariant structural description specifying the object's parts and the relations among them. This article presents a neural network that generates such a description. Structural description is made possible through a solution to the dynamic binding problem: Temporary conjunctions of attributes (parts and relations) are represented by synchronized oscillatory activity among independent units representing those attributes. Specifically, the model uses synchrony (a) to parse images into their constituent parts, (b) to bind together the attributes of a part, and (c) to bind the relations to the parts to which they apply. Because it conjoins independent units temporarily, dynamic binding allows tremendous economy of representation and permits the representation to reflect the attribute structure of the shapes represented.

Depth Perception↗

PubMed1992

Metric invariance in object recognition: a review and further evidence.

Phenomenologically, human shape recognition appears to be invariant with changes of orientation in depth (up to parts occlusion), position in the visual field, and size. Recent versions of template theories (e.g., Ullman, 1989; Lowe, 1987) assume that these invariances are achieved through the application of transformations such as rotation, translation, and scaling of the image so that it can be matched metrically to a stored template. Presumably, such transformations would require time for their execution. We describe recent priming experiments in which the effects of a prior brief presentation of an image on its subsequent recognition are assessed. The results of these experiments indicate that the invariance is complete: The magnitude of visual priming (as distinct from name or basic level concept priming) is not affected by a change in position, size, orientation in depth, or the particular lines and vertices present in the image, as long as representations of the same components can be activated. An implemented seven layer neural network model (Hummel & Biederman, 1992) that captures these fundamental properties of human object recognition is described. Given a line drawing of an object, the model activates a viewpoint-invariant structural description of the object, specifying its parts and their interrelations. Visual priming is interpreted as a change in the connection weights for the activation of: a) cells, termed geon feature assemblies (GFAs), that conjoin the output of units that represent invariant, independent properties of a single geon and its relations (such as its type, aspect ratio, relations to other geons), or b) a change in the connection weights by which several GFAs activate a cell representing an object.

Adult↗

PubMed1990

Causality and the allocation of attention during comprehension.

Recent research has suggested that each statement in a narrative text is understood by relating it to its causal antecedents and consequences and that the text as a whole is understood by finding a causal path linking its opening to its final outcome. Fletcher and Bloom (1988) have proposed that in order to accomplish this goal, while minimizing the number of times long-term memory has to be searched, readers focus their attention on the last clause of a narrative that has causal antecedents but no consequences in the preceding text. As a result, a statement that is followed by a causal antecedent should remain the focus of attention, while the same statement followed by a consequence should not. This prediction was tested and confirmed in three experiments which show that when a target statement is followed by a sentence that includes only causal antecedents, (a) continuation sentences related to it are read more quickly, (b) target words drawn from it are easier to recognize, and (c) subject-generated continuations are more likely to be causally related to it.

Attention↗