In this cross-cultural study of stress and coping, students in India (n = 198) and Canada (n = 344) were compared with respect to stress, coping, and selected psychosocial variables, namely, locus of control, self-esteem, life orientation (optimism-pessimism), and social support. The two main hypotheses postulated that, compared to the Canadian students, Indian students would experience more stress and would prefer emotion-focused coping strategies for dealing with stress. It was also predicted that the Indian students would have an external locus of control, low self-esteem, pessimistic life orientation, and greater social support satisfaction. The results reveal instead that the Indian students report less stress than the Canadian students and prefer emotion-focused coping strategies. The Indian students score higher on chance control, but are similar to the Canadian students on powerful others and internal control. The Indian students are less satisfied with social support than are their Canadian counterparts.

Animal learning researchers have argued that one example of a linearly nonseparable problem is negative patterning, and therefore they have used more complicated multilayer networks to study this kind of discriminant learning. However, it is shown in this paper that previous attempts to deWne negative patterning problems to artiWcial neural networks have speciWed the problem in such a way that it is much simpler than intended. The simulations described in this paper correct this problem by adding a "null" pattern to the training sets to make negative patterning problems truly nonseparable, and thus requiring a more complicated network than a perceptron. We show that with the elaborated training set, a hybrid multilayer network that treats reinforced patterns diVerently than nonreinforced patterns generates results more similar to those observed by Dalamater, Sosa, and Katz in animal experiments than do traditional multilayer networks.

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