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.     

The Rate and Characteristics of Suicide Attempters in the Native Hawaiian Adolescent Population

Native Hawaiian high school students, N = 1779, were surveyed for symptoms of psycho-pathology and suicide attempts in the previous 6 months. Seventy-seven (4.3%) of the students reported making a suicide attempt. There were no significant differences in prevalence rates for males and females. Depression, anxiety, aggression, substance abuse symptoms, and low family support, but not peer support, were significantly correlated with suicide attempts. On logistic regression, depression, substance abuse, and family support independently predicted attempts. The lack of gender difference may indicate a cultural characteristic of the Hawaiian population that differentiates it from mainstream American populations but likens it to the Native American population.     

Training Observers to Detect Deception Effects of Self-Monitoring and Rehearsal

It was predicted that trained observers would detect deception more accurately than untrained observers. More specifically, it was predicted that the highest deception detection accuracy would be found among trained observers judging the veracity of low self-monitors and unrehearsed liars, whereas the lowest detection accuracy would be found among untrained observers judging the veracity of high self-monitors and rehearsed deceivers. It also was hypothesized that the discrepancy between observers‘actual ability to detect deception and their certainty in the accuracy of their judgments would be smaller for trained observers than for untrained observers. Observers trained to detect deception used six behavioral cues based on research by deTurck and Miller (1985): (a) message duration, (b) response latency, (c) adaptors, (d) pauses, (e) nonfluencies, and (f) hand gestures. Results confirmed both hypotheses.