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Studies of word production often make use of picture-naming tasks, including the picture-word-interference task. In this task, participants name pictures with superimposed distractor words. They typically need more time to name pictures when the distractor word is semantically related to the picture than when it is unrelated (the semantic interference effect). The present study examines the distributional properties of this effect in a series of Bayesian meta-analyses. Meta-analytic estimates of the semantic interference effect first show that the effect is present throughout the reaction time distribution and that it increases throughout the distribution. Second, we find a correlation between a participant's mean semantic interference effect and the change in the effect in the tail of the reaction time distribution, which has been argued to reflect the involvement of selective inhibition in the naming task. Finally, we show with simulated data that this correlation emerges even when no inhibition is used to generate the data, which suggests that inhibition is not needed to explain this relationship.
The study of perceptual flexibility in speech depends on a variety of tasks that feature a large degree of variability between participants. Of critical interest is whether measures are consistent within an individual or across stimulus contexts. This is particularly key for individual difference designs that are deployed to examine the neural basis or clinical consequences of perceptual flexibility. In the present set of experiments, we assess the split-half reliability and construct validity of five measures of perceptual flexibility: three of learning in a native language context (e.g., understanding someone with a foreign accent) and two of learning in a non-native context (e.g., learning to categorize non-native speech sounds). We find that most of these tasks show an appreciable level of split-half reliability, although construct validity was sometimes weak. This provides good evidence for reliability for these tasks, while highlighting possible upper limits on expected effect sizes involving each measure.