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A motor vehicle accident causal system, from a human factors viewpoint, is presented. The system, or model, is based upon a cause and effect relationship: the"effect"being the primary failure or behavior which led directly to the collision situation; the"cause"being the reasons for the failure or behavior. Effects are described as information-processing failures of four types: (1) perception, (2) comprehension, (3) decision, and (4) action failures. The reasons for these information processing failures are categorized as (1) physical or physiological failures, (2) driver conditons or states, (3) experience or exposure factors, (4) conflicting behaviors or preoccupation, and (5) risk-taking behaviors. A causal reporting system for utilization by accident research groups is discussed in terms of primary or principal causes, severity–increasing factors, and relevant conditions. Two recent studies which used similar causal systems are described and findings from them are presented. A discussion of the possible benefits of such a system as results emerge is related to driver education and training techniques.

Performance in a memory-span task using eight-letter sequences was explored as a function of presentation rate (.5, .75, 1.0, 2.0, and 3.0 sec/item) and presentation mode (visual, auditory, simultaneous visual and auditory, and mixed visual and auditory). Results indicate that performance in the mixed mode was inferior to the other three modes, but the other modes did not differ from each other. As presentation rate decreased, performance improved. These results are consistent with current theories of memory and indicate that the mode in which alphanumeric information is displayed is unimportant provided the modes are not mixed.

Several studies have examined the impact of a number of minimum legal drinking age 21 (MLDA-21) laws on underage alcohol consumption and alcohol-related crashes in the United States. These studies have contributed to our understanding of how alcohol control laws affect drinking and driving among those who are under age 21. However, much of the extant literature examining underage drinking laws use a "Law/No law" coding which may obscure the variability inherent in each law. Previous literature has demonstrated that inclusion of law strengths may affect outcomes and overall data fit when compared to "Law/No law" coding. In an effort to assess the relative strength of states' underage drinking legislation, a coding system was developed in 2006 and applied to 16 MLDA-21 laws. The current article updates the previous endeavor and outlines a detailed strength coding mechanism for the current 20 MLDA-21 laws.