The last decade has seen great progress in the study of the nature of crossmodal links in exogenous and endogenous spatial attention . Exogenous spatial cuing studies of human crossmodal attention and multisensory integration. In C. Spence, & J. Driver , Crossmodal space and crossmodal attention . Oxford, UK: Oxford University Press.], for a recent review). A growing body of research now highlights the existence of robust crossmodal links between auditory, visual, and tactile spatial attention. However, until recently, studies of (...) exogenous and endogenous attention have proceeded relatively independently. In daily life, however, these two forms of attentional orienting continuously compete for the control of our attentional resources, and ultimately, our awareness. It is therefore critical to try and understand how exogenous and endogenous attention interact in both the unimodal context of the laboratory and the multisensory contexts that are more representative of everyday life. To date, progress in understanding the interaction between these two forms of orienting has primarily come from unimodal studies of visual attention. We therefore start by summarizing what has been learned from this large body of empirical research, before going on to review more recent studies that have started to investigate the interaction between endogenous and exogenous orienting in a multisensory setting. We also discuss the evidence suggesting that exogenous spatial orienting is not truly automatic, at least when assessed in a crossmodal context. Several possible models describing the interaction between endogenous and exogenous orienting are outlined and then evaluated in terms of the extant data. (shrink)

Linear mixed-effect models (LMMs) are being increasingly widely used in psychology to analyse multi-level research designs. This feature allows LMMs to address some of the problems identified by Speelman and McGann ( 2013 ) about the use of mean data, because they do not average across individual responses. However, recent guidelines for using LMM to analyse skewed reaction time (RT) data collected in many cognitive psychological studies recommend the application of non-linear transformations to satisfy assumptions of normality. Uncritical adoption of (...) this recommendation has important theoretical implications which can yield misleading conclusions. For example, Balota et al. ( 2013 ) showed that analyses of raw RT produced additive effects of word frequency and stimulus quality on word identification, which conflicted with the interactive effects observed in analyses of transformed RT. Generalized linear mixed-effect models (GLMM) provide a solution to this problem by satisfying normality assumptions without the need for transformation. This allows differences between individuals to be properly assessed, using the metric most appropriate to the researcher's theoretical context. We outline the major theoretical decisions involved in specifying a GLMM, and illustrate them by reanalysing Balota et al.'s datasets. We then consider the broader benefits of using GLMM to investigate individual differences. (shrink)