Four experiments are reported in which subjects gained extensive experience with artificial grammars in explicit and implicit processing tasks. Results indicated that (a) implicit processing was sufficient for learning a finite state grammar but was inadequate for learning another type of grammar based on logical rules, (b) Subjects were able to communicate some of their implicit knowledge of the grammars to another person, (c) Consistent with rule induction but not memory array models of learning, verbal protocols indicated there was no tendency to converge on the same set of cues used to identify valid strings, (d) A synergistic learning effect occurred when both implicit and explicit processing tasks were used in the grammar based on logical rules but not in the finite state grammar. A theoretical framework is proposed in which implicit learning is conceptualized as an automatic, memory-based mechanism for detecting patterns of family resemblance among exemplars.

Four experiments in which subjects learned to control two versions of a complex simulated process control task show that verbalizable knowledge of procedures used to perform these tasks is very limited and is acquired late in learning. Individual learning curves associated with these tasks showed sudden improvements in performance, which were not accompanied by a similar increase in verbalizable knowledge. It was also found that verbal instructions consisting of exemplar memorization, strategies for rule induction, simple heuristics, and experts’ instructions were all effective in enhancing novice subjects’ performance. A theoretical framework is proposed in which subjects draw on two separate but interacting knowledge structures to perform these tasks. One knowledge structure is based on memory for past experiences (close analogies), and the other is based on one's current mental model of the task. Implicit sets of competing rules that control response selection are derived from both sources of knowledge. It is suggested that dissociations between task performance and verbalizing occur because memory-based processing tends to have more control over response selection because of its greater specificity, whereas a mental model tends to be the preferred mode for verbal reporting because of its greater accessibility.

We used the theory of reasoned action to build a model of nurse turnover. Based primarily on the theory, a questionnaire was constructed and administered to 1,835 registered nurses. Six months after the questionnaires were completed, we obtained status information (remained or resigned) for those nurses who returned useable questionnaires. For status, differential intention was the only significant predictor. The significant predictors of differential intention were differential attitude, differential subjective norm, and differential moral obligation. For the combination of all predictors, R* -.32 for status, and K ! = .68 for differential intention. These findings held up under replication procedures. Additional findings suggested potential modifications of the theory of reasoned action and the methodology used to validate its principles. Overall, the theory demonstrated its usefulness both from conceptual and applied perspectives. This research was supported by Louisiana Board of Regents' Research and Development Program Grant 83-LBR/063-B33.We wish to thank the Louisiana Hospital Association, the Louisiana State Nurses Association, and all of the nurses and hospital administrators who gave so freely of their expertise and time. We also wish to thank four anonymous reviewers, Gregory Dobbins and Dirk Steiner for their helpful suggestions on an earlier draft, Patricia Wozniak for her help with the data analysis, and Brian Bienn, Tanya Demons, and Elizabeth Erffmeyer for their help with data collection. We especially wish to thank Robert M. Guion for the care he devoted to this article, as evidenced by his insightful comments.

On the basis of 3 experiments, Perruchet and Pacteau (1990) argued that implicitly acquired knowledge of a synthetic grammar consists of little more than knowledge of pairwise associations between pairs of letters in the grammar. By comparing their results with a study by Mathews, Buss, Stanley, Blanchard-Fields, Cho, and Druhan (1989), it is argued that (a) implicitly acquired knowledge is much richer and more abstract than suggested by Perruchet and Pacteau, (b) their recognition measures are less sensitive than the Mathews, Buss, et al. recall measures for detecting conscious awareness of implicit knowledge, and (c) fragmentary knowledge of a grammar constitutes abstract rules that enable performance of complex tasks when integrated into a system for combining knowledge across rules.

Learners are able to use 2 different types of knowledge to perform a skill. One type is a conscious mental model, and the other is based on memories of instances. The authors conducted 3 experiments that manipulated training conditions designed to affect the availability of 1 or both types of knowledge about an artificial grammar. Participants were tested for both speed and accuracy of their ability to generate letter sequences. Results indicate that model-based training leads to slow accurate responding. Memory-based training leads to fast, less accurate responding and highest achievement when perfect accuracy was not required. Evidence supports participants' preference for using the memory-based mode when exposed to both types of training. Finally, the accuracy contributed by model-based training declined over a retention interval.