Attention to attributes and objects in working memory.

Abstract

It has been debated on the basis of change-detection procedures whether visual working memory is limited by the number of objects, task-relevant attributes within those objects, or bindings between attributes. This debate, however, has been hampered by several limitations, including the use of conditions that vary between studies and the absence of appropriate mathematical models to estimate the number of items in working memory in different stimulus conditions. We re-examined working memory limits in two experiments with a wide array of conditions involving color and shape attributes, relying on a set of new models to fit various stimulus situations. In Experiment 2, a new procedure allowed identical retrieval conditions across different conditions of attention at encoding. The results show that multiple attributes compete for attention, but that retaining the binding between attributes is accomplished only by retaining the attributes themselves. We propose a theoretical account in which a fixed object capacity limit contains within it the possibility of the incomplete retention of object attributes, depending on the direction of attention.

Visual working memory declines when more features must be remembered for each object

“…Moving beyond two features might have a more severe impact that is easier to detect experimentally even with large changes. This interpretation is consistent with the extant literature: The null effect of one versus two features per object has been replicated several times in experiments using large changes (Delvenne & Bruyer, 2004;Olson & Jiang, 2002;Riggs, Simpson, & Potts, 2011), although other experiments using equally large changes showed worse performance with two features than with one feature per object (Cowan, Blume, & Saults, 2012;Johnson, Hollingworth, & Luck, 2008; C. C. Morey & Bieler, 2012;Wheeler & Treisman, 2002;Wilson, Adamo, Barense, & Ferber, 2012). The effect of one versus two features appears to be fickle, suggesting that the effect is small on average, and probably modulated by as yet unidentified experimental details.…”

“…Because, for each object in WM, one of its features is maintained with certainty, whereas all other features are not, the chance that the tested feature is in memory, given that the tested object is in memory, decreases with the number of features in a decelerating fashion, as observed in our Experiments 1 and 3. In the Appendix, we show that the data of those two experiments are consistent with a formal model of the proposal of Cowan et al Cowan et al (2012) do not explain why people remember one feature per object with certainty but additional features only with a probability lower than one. One potential rationale for this assumption comes from the dimensionalweighting hypothesis raised in the context of visual search studies (Müller, Heller, & Ziegler, 1995;Müller, Reimann, & Krummenacher, 2003).…”

“…This fact rules out one major potential cause of failures to replicate: lack of power. Third, with regard to the comparison of single-feature and two-feature objects, the literature is already mixed, with some reports finding no difference (Delvenne & Bruyer, 2004;Olson & Jiang, 2002;Riggs et al, 2011;Vogel et al, 2001) and others finding worse performance when two (or more) features need to be remembered per object (Cowan et al, 2012;Johnson et al, 2008;C. C. Morey & Bieler, 2012;Stevanovski & Jolicoeur, 2011;Wheeler & Treisman, 2002;Wilson et al, 2012).…”

“…This multidimensional characterization of capacity is still compatible with the assumption that, on one dimension, there is a discrete capacity to hold a maximum number of objects in VWM. For instance, Cowan et al (2012) have proposed that VWM can hold a maximum of k objects (with k around 3), such that for each object, at least one feature is maintained. Additional features are maintained with independent probabilities smaller than one, such that when more than one feature must be retained for each object, memory for an object's feature can fail even if that object is in WM.…”

What limits working memory capacity?

“…At least for visual WM, Cowan and his colleagues have proposed an explanation of the effects of both the number of chunks and the number of features per chunk (i.e., one aspect of complexity) within a discrete-resource account (Cowan et al, 2013). Therefore, although the details of how different aspects of complexity affect different aspects of performance are not yet well understood, we argue that the resource account is able to offer a reasonable explanation for the finding that WM performance depends both on the number and the complexity of elements in the memory set (A1).…”