Event-related potentials elicited by automatic targets: a dual-task analysis

The role of limited capacity processes in the detection of automatic targets was investigated in a dual-task paradigm using both behavioral and event-related brain potential (ERP) measures. An automatic detection task was paired with another concurrent discrimination while the relative importance of each task was systematically varied. The resulting performance operating characteristic (POC) showed that both the speed and accuracy of automatic detection responses were affected by the allocation of attention. Reductions in the accuracy of each task were accompanied by reductions in the amplitude of a late-positive component of the ERP (P300). In addition, the latency of the P300 component elicited by automatic targets was increased in dual-task conditions. A comparison of behavioral and ERP measures suggested the involvement of two separate limited-capacity processes in automatic detection: one concerned with the formation of an episodic representation of target occurrence and the other with the execution of rapid motor responses.     

Generating variables with arbitrary properties

There are occasions in psychological research where it is desirable to have available sets of variables with arbitrary intercorrelations. A quite simple procedure is described for generating pairs of such variables.     

Views on the NAS/NAE/IM convocation on scientific conduct

The author is the recipient of the Herman Halperin Electric Transmission and Distribution Award, IEEE; United States Activities Board Professional Leadership Award, IEEE; United States Activities Board Citation of Honor, IEEE; Philip Sporn Award, Cigré; Atwood Associate, Cigré. J.A. Casazza has established the Peter Cooper Fund for Advancing Government Technical Competitions at Cooper Union.     

The Power to Detect and Predict Individual Differences in Intra-Individual Variability Using the Mixed-Effects Location-Scale Model

Our goal is to provide empirical scientists with practical tools and advice with which to test hypotheses related to individual differences in intra-individual variability using the mixed-effects location-scale model. To that end, we evaluate Type I error rates and power to detect and predict individual differences in intra-individual variability using this model and provide empirically-based guidelines for building scale models that include random and/or systematically-varying fixed effects. We also provide two power simulation programs that allow researchers to conduct a priori empirical power analyses. Our results aligned with statistical power theory, in that, greater power was observed for designs with more individuals, more repeated occasions, greater proportions of variance available to be explained, and larger effect sizes. In addition, our results indicated that Type I error rates were acceptable in situations when individual differences in intra-individual variability were not initially detectable as well as when the scale-model individual-level predictor explained all initially detectable individual differences in intra-individual variability. We conclude our paper by providing study design and model building advice for those interested in using the mixed-effects location-scale model in practice.