Do You Think You're Sexy,Tall, and Thin? The Prediction of Self‐Rated Attractiveness,Height, and Weight

Research suggests that self‐reports of personal attributes, including physical characteristics, are often distorted. The present study examined discrepancies between self‐ and observer or actual reports of attractiveness, height, and weight, and investigated the role of esteem and gender role scales in the prediction of such discrepancies. Results indicated that attractiveness discrepancies were predicted by high esteem and traditional gender roles, height discrepancies were predicted by agency, and weight discrepancies were predicted by personal efficacy. Results also indicated that esteem and gender role characteristics are important in the perception of self‐relevant information.     

Understanding recovery from object substitution masking

When we look at a scene, we are conscious of only a small fraction of the available visual information at any given point in time. This raises profound questions regarding how information is selected, when awareness occurs, and the nature of the mechanisms underlying these processes. One tool that may be used to probe these issues is object-substitution masking (OSM). In OSM, a sparse, temporally-trailing four dot mask can interfere with target perception, even though the target and mask have different contours and do not spatially overlap (Enns & Di Lollo, 1997). Here, we investigate the mechanisms underlying the recently discovered recovery from OSM observed with prolonged mask exposure (Goodhew, Visser, Lipp, & Dux, 2011). In three experiments, we demonstrate that recovery is unaffected by mask offset, and that prolonged physical exposure of the mask is not necessary for recovery. These findings confirm that recovery is not due to: (a) an offset transient impairing the visibility of other stimuli that are nearby in space and time, or (b) mask adaptation or temporal object-individuation cues resulting from prolonged mask exposure. Instead, our results confirm recovery as a high-level visual-cognitive phenomenon, which is inherently tied to target-processing time. This reveals the prolonged iterative temporal dynamics of conscious object perception.     

Training and the attentional blink: Limits overcome or expectations raised?

The attentional blink (AB) refers to a deficit in reporting the second of two sequentially presented targets when they are separated by less than 500 ms. Two decades of research has suggested that the AB is a robust phenomenon that is likely attributable to a fundamental limit in sequential object processing. This assumption, however, has recently been undermined by a demonstration that the AB can be eliminated after only a few hundred training trials (Choi, Chang, Shibata, Sasaki, & Watanabe in Proceedings of the National Academy of Sciences 109:12242–12247, 2012). In the present work, we examined whether this training benefited performance directly, by eliminating processing limitations as claimed, or indirectly, by creating expectations about when targets would appear. Consistent with the latter option, when temporal expectations were reduced, training-related improvements declined significantly. This suggests that whereas training may ameliorate the AB indirectly, the processing limits evidenced in the AB cannot be directly eliminated by brief exposure to the task.     

Testing overall and moderator effects in random effects meta‐regression

Random effects meta‐regression is a technique to synthesize results of multiple studies. It allows for a test of an overall effect, as well as for tests of effects of study characteristics, that is, (discrete or continuous) moderator effects. We describe various procedures to test moderator effects: the z, t, likelihood ratio (LR), Bartlett‐corrected LR (BcLR), and resampling tests. We compare the Type I error of these tests, and conclude that the common z test, and to a lesser extent the LR test, do not perform well since they may yield Type I error rates appreciably larger than the chosen alpha. The error rate of the resampling test is accurate, closely followed by the BcLR test. The error rate of the t test is less accurate but arguably tolerable. With respect to statistical power, the BcLR and t tests slightly outperform the resampling test. Therefore, our recommendation is to use either the resampling or the BcLR test. If these statistics are unavailable, then the t test should be used since it is certainly superior to the z test.