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.     

A BLACK FEMINIST MODEL OF RAPE MYTH ACCEPTANCE

A Black feminist model was used to investigate rape myth acceptance between African American antirape activists and a comparison group of nonactivists using Cross's (1991) racial identity model and Downing and Roush's (1985) feminist identity model. As predicted, activists rejected rape myths more than nonactivists; the earlier stages of both models were associated with rape myth acceptance; the later stages were associated with rape myth rejection; and activists evidenced more sociopolitical maturity (race and gender consciousness) than nonactivists. The findings suggest that researchers may need to investigate to what degree rape myth acceptance serves an overarching system of social domination where racism and sexism overlap.