For many years, research on attention has been dominated by theories based on the assumption that attention is limited in capacity. These include the limited-capacity-channel theories of Welford and Broadbent and capacity or resource theories by Moray, Posner, and Kahneman. This article challenges these theories and their many descendants by asking why capacity is limited and what role capacity plays in the computations required to perform attention tasks. There are few satisfactory answers in limited-capacity and resource theories of attention. I show that the effects of load on performance, which are commonly interpreted as evidence for limited capacity, can be produced by models that assume unlimited, limited, and fixed capacity. I argue that attention is better construed as a selection of information that we need to achieve our goals. Following current research on computational models of attention in associative learning, categorization, perceptual learning, cognitive development, neuroscience, and artificial intelligence, I propose that attention is a process of choice in which selection is implemented as multiplicative gain control and processing is constrained by normalization. This perspective focuses on interactions between representations and decision processes applied to them, explaining many attentional phenomena without assuming attention is a limited resource.
Gordon D. Logan (Tue,) studied this question.