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.     

A BLACK FEMINIST MODEL OF RAPE MYTH ACCEPTANCE

A Black feminist model was used to investigate rape myth acceptance between African American antirape activists and a comparison group of nonactivists using Cross's (1991) racial identity model and Downing and Roush's (1985) feminist identity model. As predicted, activists rejected rape myths more than nonactivists; the earlier stages of both models were associated with rape myth acceptance; the later stages were associated with rape myth rejection; and activists evidenced more sociopolitical maturity (race and gender consciousness) than nonactivists. The findings suggest that researchers may need to investigate to what degree rape myth acceptance serves an overarching system of social domination where racism and sexism overlap.     

Meta-analysis of psychological interventions for chronic low back pain.

The purpose of this meta-analysis of randomized controlled trials was to evaluate the efficacy of psychological interventions for adults with noncancerous chronic low back pain (CLBP). The authors updated and expanded upon prior meta-analyses by using broad definitions of CLBP and psychological intervention, a broad data search strategy, and state-of-the-art data analysis techniques. All relevant controlled clinical trials meeting the inclusion criteria were identified primarily through a computer-aided literature search. Two independent reviewers screened abstracts and articles for inclusion criteria and extracted relevant data. Cohen's d effect sizes were calculated by using a random effects model. Outcomes included pain intensity, emotional functioning, physical functioning (pain interference or pain-specific disability, health-related quality of life), participant ratings of global improvement, health care utilization, health care provider visits, pain medications, and employment/disability compensation status. A total of 205 effect sizes from 22 studies were pooled in 34 analyses. Positive effects of psychological interventions, contrasted with various control groups, were noted for pain intensity, pain-related interference, health-related quality of life, and depression. Cognitive-behavioral and self-regulatory treatments were specifically found to be efficacious. Multidisciplinary approaches that included a psychological component, when compared with active control conditions, were also noted to have positive short-term effects on pain interference and positive long-term effects on return to work. The results demonstrated positive effects of psychological interventions for CLBP. The rigor of the methods used, as well as the results that reflect mild to moderate heterogeneity and minimal publication bias, suggest confidence in the conclusions of this review.     

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.     

Influence of Connectives on Language Comprehension: Eye tracking Evidence for Incremental Interpretation

An eye-tracking experiment investigated whether incremental interpretation applies to interclausal relationships. According to Millis and Just's (1994) delayed-integration hypothesis, interclausal relationships are not computed until the end of the second clause, because the processor needs to have two full propositions before integration can occur. We investigated the processing of causal and diagnostic sentences (Sweetser, 1990; Tversky & Kahneman, 1982) that contained the connective because . Previous research (Traxler, Sanford, Aked, & Moxey, 1997) has demonstrated that readers have greater difficulty processing diagnostic sentences than causal sentences. Our results indicated that difficulty processing diagnostic sentences occurred well before the end of the second clause. Thus comprehenders appear to compute interclausal relationships incrementally.