International Applications of Computer-based Testing and Interpretation

The computer has been used to administer, score and interpret psychological tests in a number of countries throughout the world. This paper traces early work in the United States, Great Britain and continental Europe and reviews the current international status of computer-assisted psychological testing. Several problems with transporting existing computer-based psychological testing technology across languages and cultures are discussed.
Les ordinateurs sont utilisés pour administrer, noter et interpréter les tests psychologiques dans un certain nombre de pays du monde entier. Cet article évoque les travaux réalisés aux Etats-Unis, en Grande Bretagne et en Europe Occidentale, et passe en revue les données du courant international en la matière. Plusieurs problèmes technologiques sont soulevés par la diffusion de ces tests, notamment sur les plans du langage et de la culture.     

Perceptual Categorization: Connectionist Modelling and Decision Rules

Although it is currently popular to model human associative learning using connectionist networks, the mechanism by which their output activations are converted to probabilities of response has received relatively little attention. Several possible models of this decision process are considered here, including a simple ratio rule, a simple difference rule, their exponential versions, and a winner-take-all network. Two categorization experiments that attempt to dissociate these models are reported. Analogues of the experiments were presented to a single-layer, feed-forward, delta-rule network. Only the exponential ratio rule and the winner-take-all architecture, acting on the networks' output activations that corresponded to responses available on test, were capable of fully predicting the mean response results. In addition, unlike the exponential ratio rule, the winner-take-all model has the potential to predict latencies. Further studies will be required to determine whether latencies produced under more stringent conditions conform to the model's predictions.