A new method, with an application program in Matlab code, is proposed for testing item performance models on empirical databases. This method uses data intraclass correlation statistics as expected correlations to which one compares simple functions of correlations between model predictions and observed item performance. The method rests on a data population model whose validity for the considered data is suitably tested and has been verified for three behavioural measure databases. Contrarily to usual model selection criteria, this method provides an effective way of testing under-fitting and overfitting, answering the usually neglected question "does this model suitably account for these data?"

Load theory predictions for the effects of task coordination between and within sensory modalities (vision and hearing or vision only) on the level of distraction were tested. Response competition effects in a visual flanker task when it was coordinated with an auditory discrimination task (between-modality conditions) or a visual discrimination task (within-modality conditions) were compared with single-task conditions. In the between-modality conditions, response competition effects were greater in the two-(vs. single-) task conditions irrespective of the level of discrimination task difficulty. In the within-modality conditions, response competition effects were greater in the two-task (vs. single-task) conditions only when these involved a more difficult visual discrimination task. The results provided support for the load theory prediction that executive control load leads to greater distractor interference while highlighting the effects of task modality.

The perceptual load model of attention (Lavie, 1995) suggests that processing of irrelevant distractors depends on the extent to which a relevant task engages full perceptual capacity. Word recognition models suggest that letter perception is facilitated in words relative to nonwords. These models led us to hypothesize that increasing the number of letters would increase perceptual load more for nonwords than for words, and thus would be more likely to exhaust capacity and eliminate distractor processing for nonwords than for words. In support of this hypothesis, we found that increasing the number of search letters increases RTs more for nonwords than for words and only reduces distractor interference for nonwords. Thus, although readers process words more efficiently than nonwords, they also become more prone to distraction when processing words.