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.     

How a maximizing orientation affects trade-offs between desirability and feasibility: The role of outcome- versus process-focused decision making

This research examines how maximizers make decisions when they must trade-off between desirability and feasibility attributes. Across four studies, we demonstrate that maximizers tend to prefer choices offering more desirability to those offering more feasibility and respond more favorably to a product's advertising when it highlights desirability more than feasibility attributes. Furthermore, we show that maximizers' focus on outcomes rather than processes drives their preference for desirability, such that changing from an outcome to a process focus can redirect their interest from desirability to feasibility. By contrast, satisficers do not prefer products higher in desirability to those higher in feasibility and are not more receptive to ads highlighting desirability attributes. Furthermore, because satisficers may focus on both the outcome and the process, priming either one is redundant and does not alter their preference for desirability or feasibility.     

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.