Feeling of knowing and restudy choices

Feeling-of-knowing judgments (FOK-Js) reflect people’s confidence that they would be able to recognize a currently unrecallable item. Although much research has been devoted to the factors determining the magnitude and accuracy of FOK-Js, much less work has addressed the issue of whether FOK-Js are related to any form of metacognitive control over memory processes. In the present study, we tested the hypothesis that FOK-Js are related to participants’ choices of which unrecallable items should be restudied. In three experiments, we showed that participants tend to choose for restudy items with high FOK-Js, both when they are explicitly asked to choose for restudy items that can be mastered in the restudy session (Exps. 1a and 2) and when such specific instructions are omitted (Exp. 1b). The study further demonstrated that increasing FOK-Js via priming cues affects restudy choices, even though it does not affect recall directly. Finally, Experiment 2 showed the strategy of restudying unrecalled items with high FOK-Js to be adaptive, because the efficacy of restudy is greater for these items than for items with low FOK-Js. Altogether, the present findings underscore an important role of FOK-Js for the metacognitive control of study operations

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe