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.     

“To hope was to expect”: The impact of perspective taking and forecast type on wishful thinking

When forecasting future outcomes, people tend to believe that the outcomes they want to happen are also likely to happen. Despite numerous attempts, few systematic factors have been identified that consistently and robustly reduce wishful thinking (WT) effects. Using elections and sporting event outcomes as contexts, three experiments examined whether taking the perspective of a political rival or opposing fan reduced WT effects. We also examined whether making deliberative (vs. intuitive-based) forecasts was associated with lower WT effects. Online adult samples of U.S. citizens from Mechanical Turk and U.S. college students provided their preferences and forecasts for the U.S. presidential election (Experiments 1 and 2) and a sports competition outcome (Experiment 3). Critically, some participants received perspective taking prompts immediately before providing forecasts. First, results revealed reductions in WT effects when participants engaged in perspective taking. Interestingly, this effect only emerged when intuitive-based forecasts were made first (Experiment 3). Second, intuitive-based forecasts revealed stronger evidence of WT effects. Finally, we found that perspective taking and forming forecasts deliberately promoted a shift in focus away from preferences and toward a consideration of the relative strengths and weaknesses of the entities (i.e., candidates and teams). Theoretical implications for understanding WT effects and applied implications for developing interventions are discussed.     

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.