No abstract

A fundamental issue concerning visual working memory is whether its capacity limits are better characterized in terms of a limited number of discrete slots (DSs) or a limited amount of a shared continuous resource. Rouder et al. (2008) found that a mixed-attention, fixed-capacity, DS model provided the best explanation of behavior in a change detection task, outperforming alternative continuous signal detection theory (SDT) models. Here, we extend their analysis in two ways: first, with experiments aimed at better distinguishing between the predictions of the DS and SDT models, and second, using a model-based analysis technique called landscaping, in which the functional-form complexity of the models is taken into account. We find that the balance of evidence supports a DS account of behavior in change detection tasks but that the SDT model is best when the visual displays always consist of the same number of items. In our General Discussion section, we outline, but ultimately reject, a number of potential explanations for the observed pattern of results. We finish by describing future research that is needed to pinpoint the basis for this observed pattern of results.

The slots model of visual working memory, despite its simplicity, has provided an excellent account of data across a number of change detection experiments. In the current research, we provide a new test of the slots model by investigating its ability to account for the increased prevalence of errors when there is a potential for confusion about the location in which items are presented during study. We assume that such location errors in the slots model occur when the feature information for an item in one location is swapped with the feature information for an item in another location. We show that such a model predicts two factors that will influence the extent to which location errors occur: (1) whether the test item changes to an "external" item not presented at study, or to an "internal" item presented at another location during study, and (2) the number of items in the study array. We manipulate these factors in an experiment, and show that the slots model with location errors fails to provide a satisfactory account of the observed data.

Sequential effects are ubiquitous in decision-making, but no more than in the absolute identification task where participants must identify stimuli from a set of items that vary on a single dimension. A number of competing explanations for these sequential effects have been proposed, and recently Matthews and Stewart [(2009a). The effect of inter-stimulus interval on sequential effects in absolute identification. The Quarterly Journal of Experimental Psychology, 62, 2014-2029] showed that manipulations of the time between decisions is useful in discriminating between these accounts. We use a Bayesian hierarchical regression model to show that inter-trial interval has an influence on behaviour when it varies across different blocks of trials, but not when it varies from trial to trial. We discuss the implications of both our and Matthews and Stewart's results on the effect of inter-trial interval for theories of sequential effects.

No abstract