Empirical validation and psychometric evaluation of the Brief Fear of Negative Evaluation Scale in patients with social anxiety disorder

The Brief Fear of Negative Evaluation Scale (BFNE; M. R. Leary, 1983a) is often used to assess fear of negative evaluation, the core feature of social anxiety disorder. However, few studies have examined its psychometric properties in large samples of socially anxious patients. Although the BFNE yields a single total score, confirmatory factor analysis indicated a 2-factor solution to be more appropriate, with the 1st factor consisting of all straightforwardly worded items (BFNE-S) and the 2nd of all reverse-scored items (BFNE-R). Support was obtained for the convergent and discriminant validity of the BFNE and BFNE-S, but not the BFNE-R. These results suggest that standard scoring of the BFNE may not be optimal for patients with social anxiety disorder.     

Response reversals in recognition memory

Using a dynamic sequential sampling model and a recently proposed model for confidence judgments in recognition memory (T. Van Zandt, 2000b), the authors examine the tendency for rememberers to reverse their responses after a primary decision. In 4 experiments, speeded "old"-"new" decisions were made under bias followed by a 2nd response', either a confidence judgment or another simple choice. The data from these experiments showed that participants know when they have made a mistake and that they respond to this knowledge by reversing their responses. Response reversals are thus shown to be important for constructing models of the response-selection process in recognition memory.     

Cognitive-behavioral therapy for social anxiety disorder: supporting evidence and future directions

The present paper examines the role of cognitive-behavioral therapy (CBT) in the treatment of social anxiety disorder (SAD). A cognitive-behavioral model of SAD is first presented. Different modalities of CBT for SAD are then described, including exposure, cognitive restructuring, relaxation training, and social skills training, and evidence supporting their efficacy is reviewed. The comparative and combined impact of CBT and pharmacotherapeutic interventions is also explored. CBT appears to be an efficacious treatment for SAD. However, the overall efficacy CBT may be increased by closer examination of the active ingredients of treatment. Such analyses may also enable more successful integration of the different CBT techniques and of CBT and pharmacotherapy in the treatment of SAD.     

Stimulus-Response Compatibility for Vertically Oriented Stimuli and Horizontally Oriented Responses: Evidence for Spatial Coding

It has previously been shown that, when stimuli positioned above or below a central fixation point (“up” and “down” stimuli) are assigned to left and right responses, the stimulus-response mapping up-left/down-right is more compatible than the mapping up-right/down-left for responses executed by the left hand in the left hemispace, but this relation is reversed for responses executed by the right hand in the right hemispace. In Experiment 1, each hand responded at locations in both hemispaces to dissociate the influence of hand identity from response location, and response location was found to be the determinant of relative compatibility. In Experiment 2 responses were made at the sagittal midline, and an inactive response switch was placed to the left or right to induce coding of the active switch as right or left, respectively. This manipulation of relative location had an effect similar to, although of lesser magnitude than, that produced by physically changing location of the response switch in Experiment 1. It is argued that these results are counter to predictions of a movement-preference account and consistent with the view that spatial coding underlies compatibility effects for orthogonally oriented stimulus and response sets.     

Hierarchical Approximate Bayesian Computation

Approximate Bayesian computation (ABC) is a powerful technique for estimating the posterior distribution of a model’s parameters. It is especially important when the model to be fit has no explicit likelihood function, which happens for computational (or simulation-based) models such as those that are popular in cognitive neuroscience and other areas in psychology. However, ABC is usually applied only to models with few parameters. Extending ABC to hierarchical models has been difficult because high-dimensional hierarchical models add computational complexity that conventional ABC cannot accommodate. In this paper, we summarize some current approaches for performing hierarchical ABC and introduce a new algorithm called Gibbs ABC. This new algorithm incorporates well-known Bayesian techniques to improve the accuracy and efficiency of the ABC approach for estimation of hierarchical models. We then use the Gibbs ABC algorithm to estimate the parameters of two models of signal detection, one with and one without a tractable likelihood function.