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.     

Picking a maintenance service: A user’s view

A guide is provided for selecting small computer maintenance services. Types of available services are described, and a series of questions is answered concerning selection.     

The Public and Private Use of Consensus-Raising Excuses

ABSTRACT Two experiments tested the proposition that people use consensus-raising excuses more in private than in public when the audience has information that could refute subjects' claims about others In Experiment 1, subjects received success or failure feedback and made public or private attributions to ability, effort, task difficulty, and luck In Experiment 2, subjects received positive or negative feedback and evaluated themselves and others on the trait Task difficulty attributions and evaluations of others are consensus-raising measures Consistent with our hypothesis, subjects receiving negative feedback in Experiment 1 claimed that the task was more difficult, and in Experiment 2 evaluated the other more negatively in private than in public.