"Subitizing," the process of enumeration when there are fewer than 4 items, is rapid (40-100 ms/item), effortless, and accurate. "Counting," the process of enumeration when there are more than 4 items, is slow (250-350 ms/item), effortful, and error-prone. Why is there a difference in the way the small and large numbers of items are enumerated? A theory of enumeration is proposed that emerges from a general theory of vision, yet explains the numeric abilities of preverbal infants, children, and adults. We argue that subitizing exploits a limited-capacity parallel mechanism for item individuation, the FINST mechanism, associated with the multiple target tracking task (Pylyshyn, 1989; Pylyshyn & Storm, 1988). Two kinds of evidence support the claim that subitizing relies on preattentive information, whereas counting requires spatial attention. First, whenever spatial attention is needed to compute a spatial relation (cf. Ullman, 1984) or to perform feature integration (cf. Treisman & Gelade, 1980), subitizing does not occur (Trick & Pylyshyn, 1993a). Second, the position of the attentional focus, as manipulated by cue validity, has a greater effect on counting than subitizing latencies (Trick & Pylyshyn, 1993b).

Subitizing, the enumeration of 1-4 items, is rapid (40-120 ms/item) and accurate. Counting, the enumeration of 5 items or more, is slow (250-350 ms/item) and error-prone. Why are small numbers of items enumerated differently from large numbers of items? It is suggested that subitizing relies on a preattentive mechanism. Ss could subitize heterogeneously sized multicontour items but not concentric multicontour items, which require attentional processing because preattentive gestalt processes misgroup contours from different items to form units. Similarly, Ss could subitize target items among distractors but only if the targets and distractors differed by a feature, a property derived through preattentive analysis. Thus, subitizing must rely on a mechanism that can handle a few items at once, which operates before attention but after preattentive operations of feature detection and grouping.

No abstract

No abstract

Driver inattention is thought to cause many automobile crashes. However, the research on attention is fragmented, and the applied research on driving and attention is further split between three largely independent traditions: the experimental research, the differential crash rate research, and the automation research. The goal of this review is to provide a conceptual framework to unify the research-a framework based on the combination of two fundamental dimensions of attentional selection: selection with and without conscious awareness (controlled and automatic), and selection by innate and acquired cognitive mechanisms (exogenous and endogenous). When applied to studies chosen to represent a broad range within the experimental literature, it reveals links between a variety of factors, including inexperience, inebriation, distracting stimuli, heads-up displays, fatigue, rumination, and secondary tasks such as phone conversations. This framework also has clear implications for the differential crash literature and the study of automated systems that support or replace functions of the driver. We conclude that driving research and policy could benefit from consideration of the different modes of attentional selection insofar as they integrate literatures, reveal directions for future research, and predict the effectiveness of interventions for crash-prevention.