Reflexive shifts of covert attention operate in an egocentric coordinate frame.

Covert shifts of attention have been shown to improve detection and discrimination thresholds for a range of visual stimuli. Although there is some evidence to suggest that the allocation of attention to a particular region of interest occurs in a retinotopic frame of reference, the importance of an allocentric, or object-based, framework has gained widespread empirical support. The current experiment investigates the nature of the spatial representation in which covert shifts of attention occur in response to a reflexive prime. Primes and targets were presented in four conditions designed to vary systematically the validity of the spatial relationship between the prime and target in egocentric or allocentric coordinate frameworks. A significant advantage, in terms of reaction time and correct identification, was found for targets located in positions previously primed in an egocentric (but not allocentric) framework whereas there was no advantage for locations primed in an allocentric (but not egocentric) framework. These results suggest that the allocation of covert spatial attention within an egocentric framework may be more important than previously thought.     

Confirmatory Factor Analysis (CFA), Exploratory Structural Equation Modeling (ESEM), and Set-ESEM: Optimal Balance Between Goodness of Fit and Parsimony

Abstract

CFAs of multidimensional constructs often fail to meet standards of good measurement (e.g., goodness-of-fit, measurement invariance, and well-differentiated factors). Exploratory structural equation modeling (ESEM) represents a compromise between exploratory factor analysis’ (EFA) flexibility, and CFA/SEM’s rigor and parsimony, but lacks parsimony (particularly in large models) and might confound constructs that need to be kept separate. In Set-ESEM, two or more a priori sets of constructs are modeled within a single model such that cross-loadings are permissible within the same set of factors (as in Full-ESEM) but are constrained to be zero for factors in different sets (as in CFA). The different sets can reflect the same set of constructs on multiple occasions, and/or different constructs measured within the same wave. Hence, Set-ESEM that represents a middle-ground between the flexibility of traditional-ESEM (hereafter referred to as Full-ESEM) and the rigor and parsimony of CFA/SEM. Thus, the purposes of this article are to provide an overview tutorial on Set-ESEM, juxtapose it with Full-ESEM, and to illustrate its application with simulated data and diverse “real” data applications with accessible, heuristic explanations of best practice.