Frontal encephalographic (EEG) alpha asymmetry has been proposed as a measure of the relative difference in average cortical activity between the right and left anterior cortex, where this difference is taken as a physiological marker of trait and state level variables associated with affect. The validity of asymmetry as an indicator of both physiological and psychological variables is in part determined by the psychometric properties of asymmetry scores. The present studies focus on the psychometric assessment of frontal alpha asymmetry measured during rest. The first study involves a novel approach in the assessment of the internal consistency reliability of asymmetry scores. Previous studies estimated internal consistency reliability via Cronbach's alpha, using a relatively small set of asymmetry score that summarized activity over segments of the EEG data (e.g. one minute). Such an approach, however, will create estimates dependent on the number of segments utilized rather than the total amount of data recorded. Thus in the first study, individual FFT epochs were treated as items, thereby maximizing the total number of items used to estimate internal consistency reliability. Results of this study suggest internal consistency reliability is greater than previously reported, and as such, the duration of resting EEG data necessary to achieve a reasonable reliability criterion may be shorter than the current standard. In the second study, asymmetry scores were assessed as a specific case of difference scores, which are susceptible to a statistical artifact associated with differences in true-score variance for the component measures. Predicted asymmetry scores associated with the statistical artifact were obtained by estimating the true-score variance of right and left alpha power. The use of hierarchical linear regression showed some influence of the statistical artifact on the relationship between asymmetry scores and a measure of depressive severity, suggesting that some caution may be warranted in interpreting asymmetry results with relatively small effect sizes.

Many important questions remain unanswered regarding how we recognize faces. Methodological inconsistencies have contributed to confusion regarding these questions, especially those surrounding three purported face processing mechanisms—featural, configural, and holistic—and the extent to which each play a role in face recognition. The work presented here aims to 1) empirically test the assumption that several face recognition tasks index the same underlying construct(s), and 2) contribute data to a number of ongoing debates concerning the reliability and validity of various methods for assessing integrative (i.e., holistic and/or configural) aspects of face processing. Experiment 1 tested the assumption that various tasks purporting to measure integrative face processing index the same construct(s). It is important to test this assumption because if these tasks are in fact measuring different things, then researchers should cease interpreting them as interchangeable measures. Using a within-subjects design (N = 223) we compared performance—as reflected by accuracy and reaction time measures, as well as two types of difference scores—across four of the most commonly used integrative face processing tasks: The Partial Composite Face Effect Task, the Face Inversion Effect Task, the Part Whole Effect Task, and the Configural/Featural Difference Detection Task. Analyses showed that within-task correlations were much stronger than those between-tasks. This suggests that the four conditions within each task are measuring something in common; In contrast, low correlations across tasks suggest that each is measuring something unique. This in turn suggests these tasks should not be seen as assessing the same integrative face-processing construct. Exploratory factor analyses corroborated the correlation data, finding that performance on most conditions loaded onto a single factor in unrotated solutions, but onto separate factors in direct oblimin-rotated solutions. In Experiment 2, we investigated the question of whether integrative face processing performance is related to face recognition ability. We did this by assessing the degree to which results from four widely-used integrative face processing tasks correlate with a measure of general face recognition ability, The Cambridge Face Memory Test (CFMT). The four integrative processing tasks used in this study only partly overlapped those from in Experiment 1. They were: The Complete Composite Face Effect Task, the Partial Composite Face Effect Task, the Part Whole Effect Task, and the Configural/Featural Difference Detection Task. As with Experiment 1, we used a within-subjects design (N = 260) and analyzed a variety of performance variables across these tasks. Analyses demonstrated low to moderate positive correlations between performance on the task conditions and performance on the CFMT. This suggests that the constructs the tasks reflect do contribute to face recognition ability to a modest degree. These analyses also replicated parts of Experiment 1, showing weak correlations between tasks. Also similar to Experiment 1, factor analyses generally revealed task conditions loading onto a common first factor in the unrotated factor matrix, but loading separately in the rotated factor solution. In addition to providing evidence regarding the nature of integrative face processing tasks, the data presented here speak to a number of other questions in this domain. For instance, they contribute to the debate regarding which kinds of difference scores (subtraction-based or regression-based) are more reliable, as well as the reliability of the various tasks used to investigate integrative face processing. In addition, the data inform the debate over whether the Complete or the Partial version of the Composite Face Effect Task is the superior measure of integrative face processing. In summary, the studies presented here indicate that the previous literature in face recognition needs to be interpreted with care, with an eye to differences in methodology and the problems of low measurement reliability. The various methods used to investigate integrative face processing are not assessing the same thing and cannot be taken as reflecting the same underlying construct.