Input monitoring and response selection as components of executive control in pro-saccades and anti-saccades

Several studies have shown that anti-saccades, more than pro-saccades, are executed under executive control. It is argued that executive control subsumes a variety of controlled processes. The present study tested whether some of these underlying processes are involved in the execution of anti-saccades. An experiment is reported in which two such processes were parametrically varied, namely input monitoring and response selection. This resulted in four selective interference conditions obtained by factorially combining the degree of input monitoring and the presence of response selection in the interference task. The four tasks were combined with a primary task which required the participants to perform either pro-saccades or anti-saccades. By comparison of performance in these dual-task conditions and performance in single-task conditions, it was shown that anti-saccades, but not pro-saccades, were delayed when the secondary task required input monitoring or response selection. The results are discussed with respect to theoretical attempts to deconstruct the concept of executive control

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 science. © The Author(s) 2019. Published by Oxford University Press