Modern computer-based applications often require the user to interact with avatars. Depending on the task at hand, spatial dissociation between the orientations of the user and the avatars might arise. As a consequence, the user has to adopt the avatar's perspective and identify herself/himself with the avatar, possibly changing the user's self-representation in the process. The present study aims to identify the conditions that benefit this change of perspective with objective performance measures and subjective self-estimations by integrating the idea of (...) avatar-ownership into the cognitive phenomenon of spatial compatibility. Two different instructions were used to manipulate a user's perceived ownership of an avatar used in otherwise identical situations. Users with the high-ownership instruction reported higher levels of perceived ownership of the avatar and showed larger spatial compatibility effects from the avatar's point of view in comparison to the low ownership instruction. This supports the hypothesis that perceived ownership benefits perspective taking. (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)