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.     

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.     

Effects of gender marking in pronominal coindexation.

The naming latency of a pronoun was measured when a single previously presented name in a discourse either agreed or did not agree with the pronoun in gender and person. An effect of agreement was found both under conditions in which subjects were likely to have engaged in strategic processing of the pronoun (Experiment 1) and under conditions in which this was unlikely (Experiment 3). The effect of gender agreement was also investigated when two noun phrases were present in the discourse. The results continued to show an immediate effect of gender agreement (naming latencies increased when a pronoun did not agree with one of two previously presented nouns) under experimental conditions likely to engender strategic processing (Experiment 2). This last effect was not significant under experimental conditions that were not likely to engender strategic processing (Experiment 3). The results are discussed in terms of models of the process of identifying the referent of a pronoun in a discourse.     

Multiattribute Decision Making in Context: A Dynamic Neural Network Methodology

A theoretical structure for multiattribute decision making is presented, based on a dynamical system for interactions in a neural network incorporating affective and rational variables. This enables modeling of problems that elude two prevailing economic decision theories: subjective expected utility theory and prospect theory. The network is unlike some that fit economic data by choosing optimal weights or coefficients within a predetermined mathematical framework. Rather, the framework itself is based on principles used elsewhere to model many other cognitive and behavioral data, in a manner approximating how humans perform behavioral functions. Different, interconnected modules within the network encode (a) attributes of objects among which choices are made, (b) object categories, (c) and goals of the decision maker. An example is utilized to simulate the actual consumer choice between old and new versions of Coca-Cola. Potential applications are also discussed to market decisions involving negotiations between participants, such as international petroleum traders.