Abstract
Luck and Vogel (1997) found that working memory capacity for objects defined by a single feature (e.g., color or orientation) was equivalent to capacity for multi-featured objects (e.g., colored lines of varying orientations). They concluded that capacity is determined by the number of objects, and not the number of features that are stored. By contrast, Alvarez and Cavanagh (2004) demonstrated that change detection performance declined monotonically as object complexity increased, suggesting that “informational load” also constrains the number of items that can be represented. We also found that capacity estimates dropped as complexity increased. However, these apparent capacity reductions were strongly correlated with increases in sample-test similarity (operationalized by RT in a one-item change detection task). This raised the possibility that change detection was limited by errors in comparing the sample and test rather than by a drop in the number of items that were maintained. In line with this, when comparison errors were minimized by reducing sample/test similarity, capacity estimates for even the most complex objects were equivalent to that of the simplest objects (r = .84). We conclude that visual working memory holds a fixed number of items, regardless of complexity. These slots have limited resolving power, however, such that high similarity between sample and test items will elicit errors during the comparison stage of the task. Thus, although complexity strongly determines change detection performance, it does so by influencing the probability of comparison errors rather than the number of items that are represented. When low similarity prevents comparison errors, capacity estimates for complex and simple objects are equivalent. Finally, a correlational analysis suggested a two-factor model of working memory ability, in which the number and resolution of representations in working memory correspond to distinct dimensions of memory ability.