Effects of mixed versus blocked design on stimulus evaluation: combining underaddative effects.

(from the journal abstract) According to the asynchronous discrete coding model of Miller, two manipulations should display underadditive effects on reaction time if they slow down noncontingent stages associated with the processing of two separable dimensions of a stimulus. Underadditive effects are also predicted by a dual route model when a task variable is factorially varied with design type (mixed vs blocked). Interpretations of both underadditive effects and their combination were evaluated. Intact and degraded stimuli were presented to 18 young adults either in a single block (mixed) or in separate blocks (blocked). Spatial stimulus-response (S-R) compatibility was manipulated in all conditions. Stimulus degradation and S-R compatibility interacted underadditively, but only in blocked presentations. Both interpretations of underadditive effects were supported. Eye-movement registrations provided additional support for the alternative routes model

The cognitive mechanisms underlying deception: An event-related potential study

The cognitive view on deception proposes that lying comes with a cognitive cost. This view is supported by the finding that lying typically takes longer than truth telling. Event-related potentials (ERPs) provide a means to unravel the cognitive processes underlying this cost. Using a mock-crime design, the current study (n = 20) investigated the effects of deception on the Contingent Negative Variation (CNV), the Lateralized Readiness Potential (LRP), the Correct Response Negativity (CRN), and the stimulus-locked N200 and P300 components. In line with previous research, lying resulted in more errors, longer reaction times (RTs) and longer RT standard deviations compared to truthful responses. A marginally significant effect suggested a stronger CNV for the anticipation of lying compared to the anticipation of truth telling. There were no significant deception effects on the stimulus- and the response-locked LRPs. Unexpectedly, we found a significantly larger CRN for truth telling compared to lying. Additional analyses revealed an enhanced N200 and a decreased P300 for lying compared to truth telling. Our results support the cognitive load hypothesis for lying, yet are mixed regarding the response conflict hypothesis. Results are discussed with regard to the specific characteristics of our design and their theoretical and applied implications

Motion of Particles in Bends of Cicular Pipes

The appropriate definition and scaling of the magnitude of electroencephalogram (EEG) oscillations is an underdeveloped area. The aim of this study was to optimize the analysis of resting EEG alpha magnitude, focusing on alpha peak frequency and nonlinear transformation of alpha power. A family of nonlinear transforms, Box-Cox transforms, were applied to find the transform that (a) maximized a non-disputed effect: the increase in alpha magnitude when the eyes are closed (Berger effect), and (b) made the distribution of alpha magnitude closest to normal across epochs within each participant, or across participants. The transformations were performed either at the single epoch level or at the epoch-average level. Alpha peak frequency showed large individual differences, yet good correspondence between various ways to estimate it in 2min of eyes-closed and 2min of eyes-open resting EEG data. Both alpha magnitude and the Berger effect were larger for individual alpha than for a generic (8-12Hz) alpha band. The log-transform on single epochs (a) maximized the t-value of the contrast between the eyes-open and eyes-closed conditions when tested within each participant, and (b) rendered near-normally distributed alpha power across epochs and participants, thereby making further transformation of epoch averages superfluous. The results suggest that the log-normal distribution is a fundamental property of variations in alpha power across time in the order of seconds. Moreover, effects on alpha power appear to be multiplicative rather than additive. These findings support the use of the log-transform on single epochs to achieve appropriate scaling of alpha magnitude

Measuring automatic associations:Validation of algorithms for the Implicit Association Test (IAT) in a laboratory setting

<p>Background and objectives: In their paper, "Understanding and using the Implicit Association Test: I. An improved scoring algorithm", Greenwald, Nosek, and Banaji (2003) investigated different ways to calculate the IAT-effect. However, up to now, it remained unclear whether these findings based on internet data also generalize to laboratory settings. Therefore, the main goal of the present study was to cross-validate scoring algorithms for the IAT in a laboratory setting, specifically in the domain of psychopathology.</p><p>Methods: Four known IAT algorithms and seven alternative IAT algorithms were evaluated on several performance criteria in the large-scale laboratory sample of the Netherlands Study of Depression and Anxiety (N = 2981) in which two IATs were included to obtain measurements of automatic self-anxious and automatic self-depressed associations.</p><p>Results and conclusions: Results clearly demonstrated that the D-2SD-measure and the D-600-measure as well as an alternative algorithm based on the correct trials only (D-noEP-measure) are suitable to be used in a laboratory setting for IATs with a fixed order of category combinations. It remains important to further replicate these findings, especially in studies that include outcome measures of more spontaneous kinds of behaviors. (C) 2012 Elsevier Ltd. All rights reserved.</p>

Implicit measures of association in psychopathology research

Studies obtaining implicit measures of associations in Diagnostic and Statistical Manual of Menta