Purely parallel neural networks can model object recognition in brief displays – the same conditions under which illusory conjunctions have been demonstrated empirically. Correcting errors of illusory conjunction is the “tag-assignment” problem for a purely parallel processor: the problem of assigning a spatial tag to nonspatial features, feature combinations, and objects. This problem must be solved to model human object recognition over a longer time scale. Our model simulates both the parallel processes that may underlie illusory conjunctions and the serial (...) processes that may solve the tag-assignment problem in normal perception. One component of the model extracts pooled features and another provides attentional tags that correct illusory conjunctions. Our approach addresses two questions: How can objects be identified from simultaneously attended features in a parallel, distributed representation? How can the spatial selectional requirements of such an attentional process be met by a separation of pathways for spatial and nonspatial processing? Our analysis of these questions yields a neurally plausible simulation of tag assignment based on synchronizing feature processing activity in a spatial focus of attention. (shrink)

The effect of attention on perceived brightness and contrast was investigated in eight experiments. Attention was manipulated by engaging observers in an attention-demanding concurrent task or by directing attention to a location with a peripheral cue. In all of the dual-task manipulations, attention reduced the variability of responses. However, attention did not affect the brightness of stimuli, nor did it affect the amount of simultaneous brightness contrast. Results with peripheral location cues were similar; however, the effect of attention in these (...) experiments could be attributed to nonperceptual factors. The metaphorical “spotlight” of attention reduces observers' uncertainty about the brightness of a stimulus, but it does not “illuminate” in terms of brightness or contrast. (shrink)

In attempts to formulate a computational understanding of brain function, one of the fundamental concerns is the data structure by which the brain represents information. For many decades, a conceptual framework has dominated the thinking of both brain modelers and neurobiologists. That framework is referred to here as "classical neural networks." It is well supported by experimental data, although it may be incomplete. A characterization of this framework will be offered in the next section. Difficulties in modeling important functional aspects (...) of the brain on the basis of classical neural networks alone have led to the recognition that another, general mechanism must be invoked to explain brain function. That mechanism I call " binding." Binding by neural signal synchrony had been mentioned several times in the liter ature before it was fully formulated as a general phenomenon. Although experimental evidence for neural syn chrony was soon found, the idea was largely ignored for many years. Only recently has it become a topic of animated discussion. In what follows, I will summarize the nature and the roots of the idea of binding, especially of temporal binding, and will discuss some of the objec tions raised against it. (shrink)