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Evaluating and excluding swap errors in analogue report.

When observers retrieve simple visual features from working memory, two kinds of error are typically confounded in their recall. First, responses reflect noise or variability within the feature dimension they were asked to report. Second, responses are corrupted by ``swap errors'', in which a different item from the memory set is reported in place of the one that was probed. Independent evaluation of these error sources is vital for understanding the structure of internal representations and, in particular, how binding information is maintained. However, previous methods for disentangling these errors have been critically dependent on assumptions about the noise distribution, which is a priori unknown. Here I address this question with novel non-parametric methods, which estimate swap frequency and feature variability with the minimum of prior assumptions, and without a fitting procedure. The results indicate that swap errors are considerably more prevalent than previously appreciated (accounting for more than a third of responses at set size 8). These methods also identify which items are swapped in for targets: when the target item is cued by location, the items in closest spatial proximity are most likely to be incorrectly reported, thus implicating noise in the probe feature dimension as a source of swap errors. Finally I show how swap errors can be excluded from the raw histogram of observer responses to uncover the true distribution of error in the reported feature dimension. I will discuss the implications of these results for neural models of working memory storage. Meeting abstract presented at VSS 2015.