Item performance in visual word recognition.

Standard factorial designs in psycholinguistics have been complemented recently by large-scale databases providing empirical constraints at the level of item performance. At the same time, the development of precise computational architectures has led modelers to compare item-level performance with item-level predictions. It has been suggested, however, that item performance includes a large amount of undesirable error variance that should be quantified to determine the amount of reproducible variance that models should account for. In the present study, we provide a simple and tractable statistical analysis of this issue. We also report practical solutions for estimating the amount of reproducible variance for any database that conforms to the additive decomposition of the variance. A new empirical database consisting of the word identification times of 140 participants on 120 words is then used to test these practical solutions. Finally, we show that increases in the amount of reproducible variance are accompanied by the detection of new sources of variance.