GENDER DIFFERENCES IN REASONING ABOUT MILITARY INTERVENTION

The question of gender associations with moral reasoning and values has received a great deal of attention since Gilligan first published In a Different Voice in 1982. Various authors have argued that women are less hierarchical, more relational, more caring, more empathetic, and more concerned about not harming others than men. Yet, these claims have been questioned by other scholars. Data from a small survey of college students are used to address this question. We found that, in examining students' narrative justifications of their opinion on a particular question of military intervention, gender was related to the type of justification used in a manner consistent with the arguments of Gilligan. We argue that "objective" tests are less likely to detect this difference than content analyses of narratives. The different bases of judgments have implications for political opinions as well as interpersonal interactions.     

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.