In perception, divided attention refers to conditions in which multiple stimuli are relevant to an observer. To measure the effect of divided attention in terms of perceptual capacity, we introduce an extension of the simultaneous-sequential paradigm. The extension makes predictions for fixed-capacity models as well as for unlimited-capacity models. We apply this paradigm to two example tasks, contrast discrimination and word categorization, and find dramatically different effects of divided attention. Contrast discrimination has unlimited capacity, consistent with independent, parallel processing. Word categorization has a nearly fixed capacity, consistent with either serial processing or fixed-capacity, parallel processing. We argue that these measures of perceptual capacity rely on relatively few assumptions compared to most alternative measures.

How is visual object perception limited by divided attention? Whereas some theories have proposed that it is not limited at all (unlimited capacity), others have proposed that divided attention introduces restrictive capacity limitations or serial processing (fixed capacity). We addressed this question using a task in which observers searched for instances of particular object categories, such as a moose or squirrel. We applied an extended simultaneous-sequential paradigm to test the fixed-capacity and unlimited-capacity models (Experiment 1). The results were consistent with fixed capacity and rejected unlimited capacity. We ascertained that these results were due to attention, and not to sensory interactions such as crowding, by repeating the experiment using a cuing paradigm with physically identical displays (Experiment 2). The results from both experiments were consistent with theories of object perception that have fixed capacity, and they rejected theories with unlimited capacity. Both serial and parallel models with fixed capacity remain viable alternatives.Keywords Visual search . Divided attention . Object recognition . Capacity limitations . Simultaneous-sequential paradigm Visual perception allows us to categorize objects, distinguishing a house from a train or a person from a dog. In navigating cluttered real-world environments, we are presented with multiple objects of interest simultaneously. In this article, we will examine how such divided attention affects our ability to categorize objects. CapacityThe effect of divided attention can be characterized in terms of capacity. Following Broadbent (1958), capacity refers to the quantity of information that can pass through a system during a given time interval. The two extreme, boundary-defining models are unlimited-capacity and fixed-capacity models.In unlimited-capacity models, divided attention does not limit information processing. In this case, performance is limited only by the quality of the sensory data. Importantly, unlimited-capacity processing is not necessarily fast or accurate: The definitive property is that the speed and accuracy of individual stimulus processes are not degraded by divided attention. The prototype of this class of model is the standard parallel model (Gardner, 1973;Townsend, 1974), in which multiple stimuli are analyzed in parallel and the processing rate for each object is independent of the number of objects being analyzed. Such models have been successful in predicting performance for tasks that rely on the perception of simple visual features, such as luminance, orientation, and size (see, e.g

Can one perceive multiple object shapes at once? We tested two benchmark models of object shape perception under divided attention: an unlimited-capacity and a fixed-capacity model. Under unlimited-capacity models, shapes are analyzed independently and in parallel. Under fixed-capacity models, shapes are processed at a fixed rate (as in a serial model). To distinguish these models, we compared conditions in which observers were presented with simultaneous or sequential presentations of a fixed number of objects (The extended simultaneous-sequential method: Scharff, Palmer, & Moore, 2011a, 2011b). We used novel physical objects as stimuli, minimizing the role of semantic categorization in the task. Observers searched for a specific object among similar objects. We ensured that non-shape stimulus properties such as color and texture could not be used to complete the task. Unpredictable viewing angles were used to preclude image-matching strategies. The results rejected unlimited-capacity models for object shape perception and were consistent with the predictions of a fixed-capacity model. In contrast, a task that required observers to recognize 2-D shapes with predictable viewing angles yielded an unlimited capacity result. Further experiments ruled out alternative explanations for the capacity limit, leading us to conclude that there is a fixed-capacity limit on the ability to perceive 3-D object shapes.

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