Control' of reflexive and voluntary saccades in the gap effect.

In two experiments, we examined whether voluntary and reflexive saccades shared a common fixation disengagement mechanism. Participants were required to perform a variety of tasks, each requiring a different level of information processing of the display prior to execution of the saccade. In Experiment 1, participants executed either a prosaccade or an antisaccade upon detecting a stimulus array. In Experiment 2, participants executed a prosaccade to a stimulus array only if the array contained a target item. The target could be a line (easy search) or a digit (difficult search). The critical manipulation in both experiments was the relative timing between the removal of the fixation stimulus and the onset of the stimulus array. In both experiments, it was found that saccadic latencies were shortest when the fixation stimulus was removed before the onset of the stimulus array--a gap effect. It was concluded that reflexive and voluntary saccades share a common fixation disengagement mechanism that is largely independent of higher level cognitive processes.     

Control of reflexive and voluntary saccades in the gap effect

In two experiments, we examined whether voluntary and reflexive saccades shared a common fixation disengagement mechanism, Participants were required to perform a variety of tasks, each requiring a different level of information processing of the display prior to execution of the saccade. In Experiment 1, participants executed either a prosaccade or an antisaccade upon detecting a stimulus array. In Experiment 2, participants executed a prosaccade to a stimulus array only if the array contained a target item. The target could be a line (easy search) or a digit (difficult search). The critical manipulation in both experiments was the relative timing between the removal of the fixation stimulus and the onset of the stimulus array. In both experiments, it was found that saccadic latencies were shortest when the fixation stimulus was removed before the onset of the stimulus array—a gap effect. It was concluded that reflexive and voluntary saccades share a common fixation disengagement mechanism that is largely independent of higher level cognitive processes.     

Can we disregard the whole model? Omnibus non-inferiority testing for R2 in multi-variable linear regression and in ANOVA

Determining a lack of association between an outcome variable and a number of different explanatory variables is frequently necessary in order to disregard a proposed model (i.e., to confirm the lack of a meaningful association between an outcome and predictors). Despite this, the literature rarely offers information about, or technical recommendations concerning, the appropriate statistical methodology to be used to accomplish this task. This paper introduces non-inferiority tests for ANOVA and linear regression analyses, which correspond to the standard widely used F test for and R², respectively. A simulation study is conducted to examine the Type I error rates and statistical power of the tests, and a comparison is made with an alternative Bayesian testing approach. The results indicate that the proposed non-inferiority test is a potentially useful tool for ‘testing the null’.     

An examination of the roles of trauma exposure and posttraumatic stress disorder on emotion regulation strategies of Operation Iraqi Freedom,Operation Enduring Freedom,and Operation New Dawn veterans

Theories of posttraumatic stress disorder (PTSD) implicate emotional processes, including difficulties utilizing adaptive emotion regulation strategies, as critical to the etiology and maintenance of PTSD. Operation Iraqi Freedom, Operation Enduring Freedom, and Operation New Dawn (OIF/OEF/OND) veterans report high levels of combat exposure and PTSD. We aimed to extend findings suggesting that emotion regulation difficulties are a function of PTSD, rather than combat trauma exposure or common comorbidities, to OIF/OEF/OND veterans, in order to inform models of PTSD risk and recovery that can be applied to returning veterans. We tested differences in emotion regulation, measured with the Difficulties in Emotion Regulation Scale and Emotion Regulation Questionnaire, among trauma-exposed veterans with (n = 24) or without PTSD (n = 22) and healthy civilian comparison participants (n = 27) using multivariate analyses of covariance, adjusting for major depressive disorder, anxiety disorders, and demographic variables (age, sex, and ethnicity). Veterans with PTSD reported more use of expressive suppression and more difficulties with emotion regulation than veterans without PTSD and healthy comparison participants. Groups did not differ on cognitive reappraisal. Findings suggest the key role of PTSD above and beyond trauma exposure, depression, and anxiety in specific aspects of emotion dysregulation among OIF/OEF/OND veterans. Interventions that help veterans expand and diversify their emotion regulation skills may serve as helpful adjunctive treatments for PTSD among OIF/OEF/OND veterans.     

Prior knowledge enhances the category dimensionality effect

A study of the combined influence of prior knowledge and stimulus dimensionality on category learning was conducted. Subjects learned category structures with the same number of necessary dimensions but with more or fewer additional, redundant dimensions and with either knowledge-related or knowledge-unrelated features. Minimal-learning models predict that all subjects, regardless of condition, either should learn the same number of dimensions or should respond more slowly to each dimension. Despite similar learning rates and response times, subjects learned more features in the high-dimensional than in the low-dimensional condition. Furthermore, prior knowledge interacted with dimensionality, increasing what was learned, especially in the high-dimensional case. A second experiment confirmed that the participants did, in fact, learn more features during the training phase, rather than simply inferring them at test. These effects can be explained by direct associations among features (representing prior knowledge), combined with feedback between features and the category label, as was shown by simulations of the knowledge resonance, or KRES, model of category learning.