The Stroop effect has been shown to depend on the relative proportion of congruent and incongruent trials. This effect is commonly attributed to experiment-wide word-reading strategies that change as a function of proportion congruent. Recently, Jacoby, Lindsay, and Hessels (2003) reported an item-specific proportion congruent effect that cannot be due to these strategies and instead may reflect rapid, stimulus driven control over word-reading processes. However, an item-specific proportion congruent effect may also reflect learned associations between color word identities and responses. In two experiments, we demonstrate a context-specific proportion congruent effect that cannot be explained by such word-response associations. Our results suggest that processes other than learning of word-response associations can produce contextual control over Stroop interference.
The experiment reported here explored the importance of engaging 4-year-old children's interest in the print itself during storybook reading. We explored the effect of computer animation of the print in order to draw the child's attention to each word as it was read. We also investigated the influence of illustrating that not all visual displays are readable print on the child's print knowledge. The measures of interest were print concept knowledge and early reading skill. Results indicated that simply drawing children's attention to the print during shared reading was insufficient to facilitate children's learning of print conventions, but this attention to print while hearing stories read did improve children's letter reading. The child's active engagement with the print during shared story reading led to further improvements in written language skills, as illustrated by gains in knowledge about print concepts.
Multi-hop question answering (QA) requires an information retrieval (IR) system that can find multiple supporting evidence needed to answer the question, making the retrieval process very challenging. This paper introduces an IR technique that uses information of entities present in the initially retrieved evidence to learn to 'hop' to other relevant evidence. In a setting, with more than 5 million Wikipedia paragraphs, our approach leads to significant boost in retrieval performance. The retrieved evidence also increased the performance of an existing QA model (without any training) on the HOTPOTQA benchmark by 10.59 F1.
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