Learning to discriminate interaural time differences at low and high frequencies

This study investigated learning, in normal-hearing adults, associated with training (i.e. repeated practice) on the discrimination of ongoing interaural time difference (ITD). Specifically, the study addressed an apparent disparity in the conclusions of previous studies, which reported training-induced learning at high frequencies but not at low frequencies. Twenty normal-hearing adults were trained with either low- or high-frequency stimuli, associated with comparable asymptotic thresholds, or served as untrained controls. Overall, trained listeners learnt more than controls and over multiple sessions. The magnitudes and time-courses of learning with the lowand high-frequency stimuli were similar. While this is inconsistent with the conclusion of a previous study with low-frequency ITD, this previous conclusion may not be justified by the results reported. Generalization of learning across frequency was found, although more detailed investigations of stimulus-specific learning are warranted. Overall, the results are consistent with the notion that ongoing ITD processing is functionally uniform across frequency. These results may have implications for clinical populations, such as users of bilateral cochlear implants

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