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.     

Not all forms of misbehavior are created equal: Differential personality facet–counterproductive work behavior relations

We examined whether four personality traits—trait aggression, trait industriousness, trait deceptiveness, and trait self-control—were differentially related to 11 narrow-bandwidth CWB facets: (a) property destruction, (b) inappropriate verbal actions, (c) inappropriate physical actions, (d) poor attendance, (e) poor quality work, (f) unsafe behavior, (g) theft and related behavior, (h) misuse of information, (i) misuse of time and resources, (j) alcohol use, and (k) drug use. Based on responses from 404 employed participants recruited using Mechanical Turk, we found that each narrow-bandwidth personality trait often yielded stronger relationships with overall CWB than with narrow-bandwidth CWB facets.