Alcohol expectancy theory suggests that genetically influenced personality characteristics may lead to differential acquisition of expectancy information, and that this information then may serve as one mediational pathway for alcoholism risk. Research has already shown that expectancy information can predict and even mediate risk, but it has yet to be shown that personality traits can influence the acquisition of alcohol expectancy information. To that end, personality characteristics known to be risk factors for the development of excessive alcohol use were assessed in 83 male undergraduates. In addition, each participant studied, in a paired-associate learning/cued-recall test paradigm, a list of word pairs matching alcohol content words (keg, beer), positive/arousing alcohol expectancy words (happy, fun), and neutral words (backpack, desk). Their rate of learning the second word from each pair after being cued with the first word was then assessed across three trials of this task, and overall learning of the pairs was then assessed using free-recall. To determine whether learning rates for each type of word pair was a function of participants' status on the personality risk measures, hierarchical regression analyses were conducted for cued- and free-recall data. Results indicated that higher sensation seeking, more drinking-related problems, and a lesser degree of a family history of alcohol problems were predictive of greater recall for word pairs containing alcohol and expectancy information. Consistent with predictions, these risk indicators were also associated with a higher rate of learning for the alcohol and expectancy pairs. These findings suggest that significant differences exist in the ability to learn alcohol to expectancy word associations, and lend support to recent theories that implicate individual difference factors as a predisposition for the development of problematic alcohol to expectancy associations in memory.

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As system automation increases and evolves, the intervention of the supervising operator becomes ever less frequent but ever more crucial. The adaptive automation approach is one in which control of tasks dynamically shifts between humans and machines, being an alternative to traditional static allocation in which task control is assigned during system design and subsequently remains unchanged during operations. It is proposed that adaptive allocation should adjust to the individual operators' characteristics in order to improve performance, avoid errors, and enhance safety. The roles of three individual difference variables relevant to adaptive automation are described: attentional control, desirability of control, and trait anxiety. It was hypothesized that these traits contribute to the level of performance for target detection tasks for different levels of difficulty as well as preferences for different levels of automation. The operators' level of attentional control was inversely proportional to automation level preferences, although few objective performance changes were observed. The effects of sensory modality were also assessed, and auditory signal detection was superior to visual signal detection. As a result, the following implications have been proposed: operators generally preferred either low or high automation while neglecting the intermediary level; preferences and needs for automation may not be congruent; and there may be a conservative response bias associated with high attentional control, notably in the auditory modality.

Increased technology reliance along with today’s global fast paced society has produced increasingly complex, dynamic operating environments in disciplines as diverse as the military, healthcare, and transportation. These complex human machine systems often place additional cognitive and metacognitive demands on the operator. Thus, there is a crucial need to develop training tools for all levels of operators in these dynamic systems. The current study was designed to empirically test the effects of four training methods on performance and mental model accuracy in a microworld simulation game. It was hypothesized that process-focused guidance targeting metacognitive level processes as well as combined process and problem focused guidance would result in better performance and mental model accuracy than problemfocused guidance alone or unguided training approaches. Additionally, it was expected that individual differences in prior decision making ability, metacognitive awareness, working memory span, and fluid intelligence would moderate the relationship between the type of instructional guidance and outcomes. Results supported the development of decision-making skills through process-focused instructional guidance, particularly for initially low performing or more novice individuals. Results highlight the importance of individual learner experience prior to training. Similarly, this research aims to expand the literature by providing support for process-focused training as a method to support non-expert decision making skills. While further research needs are outlined, the current research represents an important step forward in both the theoretical literature providing support for instruction designed to support domain general decision making skills in non-experts. iv Practical implications regarding improved guidance for future instructional and training systems design, personnel selection, operator and system performance evaluation, and safety are also discussed.

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