From the conception of Baddeley’s visuospatial sketchpad, visual working memory and visual attention have been closely linked concepts. An attractive model has advocated unity of the two cognitive functions, with attention serving the active maintenance of sensory representations. However, empirical evidence from various paradigms and dependent measures has now firmly established an at least partial dissociation between visual attention and visual working memory maintenance e thus leaving unclear what the relationship between the two concepts is. Moreover, a focus on sensory (...) storage has treated visual working memory as a reflection of the past, with attention as a limiting resource. This view ignores what storage is for: immediate or future action. We argue that rather than serving sensory storage, attention emerges from coupling relevant sensory and action representations within working memory. Importantly, this coupling is bidirectional: First, through recurrent feedback mechanisms, action coupling results in the enhancement of the appropriate sensory memory representation. Under this view, unattended memories are currently not coupled to an action plan, but are not necessarily lost and remain available for future tasks when necessary. Second, through the very same feedback projections, attention serves as the credit assignment mechanism for the action’s outcome. When the action is successful, the associated representations are being reinforced, leading to more robust consolidation and more rapid retrieval in the future e thus explaining performance benefits for attended memories without assuming that attention serves as the maintenance mechanism. By firmly grounding VWM in the action system, the new framework integrates a range of behavioural and neurophysiological findings and avoids circularity in explaining the role of attention in working memory. (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)

Traditional approaches to human information processing tend to deal with perception and action planning in isolation, so that an adequate account of the perception-action interface is still missing. On the perceptual side, the dominant cognitive view largely underestimates, and thus fails to account for, the impact of action-related processes on both the processing of perceptual information and on perceptual learning. On the action side, most approaches conceive of action planning as a mere continuation of stimulus processing, thus failing to account (...) for the goal-directedness of even the simplest reaction in an experimental task. We propose a new framework for a more adequate theoretical treatment of perception and action planning, in which perceptual contents and action plans are coded in a common representational medium by feature codes with distal reference. Perceived events (perceptions) and to-be-produced events (actions) are equally represented by integrated, task-tuned networks of feature codes – cognitive structures we call event codes. We give an overview of evidence from a wide variety of empirical domains, such as spatial stimulus-response compatibility, sensorimotor synchronization, and ideomotor action, showing that our main assumptions are well supported by the data. Key Words: action planning; binding; common coding; event coding; feature integration; perception; perception-action interface. (shrink)

The neural bases of behavior are often discussed in terms of perceptual, cognitive, and motor stages, defined within an information processing framework that was originally inspired by models of human abstract problem solving. Here, we review a growing body of neurophysiological data that is difficult to reconcile with this influential theoretical perspective. As an alternative foundation for interpreting neural data, we consider frameworks borrowed from ethology, which emphasize the kinds of real-time interactive behaviors that animals have engaged in for millions (...) of years. In particular, we discuss an ethologically-inspired view of interactive behavior as simultaneous processes that specify potential motor actions and select between them. We review how recent neurophysiological data from diverse cortical and subcortical regions appear more compatible with this parallel view than with the classical view of serial information processing stages. (shrink)