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.     

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.     

Suicide and Homicide Rates in Canada and the United States

Suicide and homicide rates by age were analyzed for Canada and the United States, indicating that suicide is higher in Canada and that homicide is higher in the United States. Results indicated a positive association between homicide and suicide rates in the United States but these two rates of death were not significantly associated in Canada. Holinger (1987) had associated the relative size of the cohort to the rates of suicide and homicide in young people in the United States. The more current data in both countries did not support Holinger's results. Using the measure devised by Easterlin (1980) and Ahlburg and Schapiro (1984) –that is, the proportion of youths aged 15–24 relative to adults aged 25–64–the correlation between the size of the youth cohort and the suicide rate of youths aged 15–24 was negative. It is concluded that the two patterns in these two countries may be explained from a historical perspective.     

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.     

Mobilization of Cognitive Resources and the Generation Effect

The generation effect refers to the memory advantage of words that have been generated rather than read. Such a read-generate comparison confounds qualitative task differences and raises methodological problems. A revised methodology is proposed circumventing these problems in that the encoding task is held constant and all stimuli have to be generated, but the degree of generativeness (i.e. the amount of cueing) is varied. In Experiment 1, 1, the (refined version of the) generation effect is demonstrated in a within-subjects design; with increasing generation activity left to the subject, free recall performance increases. No effect is obtained for degree of target masking. The same finding is replicated and shown to be independent of self-paced study time when generative activity is manipulated between subjects (Experiment 2) or within subjects (Experiment 3). As all learning trials involve generation, encoding time is controlled statistically, and free recall is used as a measure of memory, this refined generation effect cannot be explained as an artifact of selective attention or elaboration. Rather, generative activity seems to increase the mobilization of cognitive resources. This motivational account is supported by Experiment 4 showing an enhanced generation effect for positive mood.