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The article presents an empirical comparative study of a Great Britain-based assessment centre and an American assessment centre. The article notes that assessment centres have not enjoyed the success in Great Britain that they have in the US. The article asks if paper and pencil tests can indicate management potential better than other assessment centre activities; if assessment centres can contribute to the judgments of management potential in other ways; and how this research can improve assessment centre effectiveness. The study found that psychometric tests do lend value to management assessment, but are less influential than simulation exercises. The study found that UK centres performed as well as American centres, which should lend credibility in the UK market.

Objective: This paper explores the development of a rigorous computational model of driver behavior in a cognitive architecture--a computational framework with underlying psychological theories that incorporate basic properties and limitations of the human system.Background: Computational modeling has emerged as a powerful tool for studying the complex task of driving, allowing researchers to simulate driver behavior and explore the parameters and constraints of this behavior.Method: An integrated driver model developed in the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture is described that focuses on the component processes of control, monitoring, and decision making in a multilane highway environment.Results: This model accounts for the steering profiles, lateral position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing.Conclusion: The model demonstrates how cognitive architectures facilitate understanding of driver behavior in the context of general human abilities and constraints and how the driving domain benefits cognitive architectures by pushing model development toward more complex, realistic tasks.Application: The model can also serve as a core computational engine for practical applications that predict and recognize driver behavior and distraction. [ABSTRACT FROM AUTHOR]