Human movement (M) responses to the Rorschach are related to cognitive sophistication, creativity, and empathy. Recent studies also link Ms to EEG-mu suppression, an index of mirroring activity in the brain. In this article, we further investigate the link between Ms and mu suppression by testing some clinical interpretative distinctions. Previously collected EEG data recorded during the administration of the Rorschach were reanalyzed. We hypothesized that (a) among several responses investigated, only M would be associated with mu suppression, and (b) Ms with active movement, ordinary form quality, or whole human figures would be most strongly associated with mu suppression. Hypothesis 1 was fully confirmed, thus supporting that the traditional interpretation of M has a neurobiological foundation. Hypothesis 2 was partially confirmed; that is, active Ms were associated with mu suppression more strongly than passive Ms (p < .05), but no other significant differences emerged. Clinical implications are discussed.

It has been suggested that the Rorschach human movement (M) response could be associated with an embodied simulation mechanism mediated by the mirror neuron system (MNS). To date, evidence for this hypothesis comes from two electroencephalogram studies and one repetitive transcranial magnetic stimulation study. To provide additional data on this topic, the Rorschach was administered during fMRI to a sample of 26 healthy adult volunteers. Activity in MNS-related brain areas temporally associated with M responses was compared with such activity for other, non-M Rorschach responses. Data analyses focused on MNS regions of interest identified by Neurosynth, a web-based platform for large scale, automated meta-analysis of fMRI data. Consistent with the hypothesis that M responses involve embodied simulation and MNS activity, univariate region of interest analyses showed that production of M responses associated with significantly greater activity in MNS-related brain areas when compared with non-M Rorschach responses. This finding is consistent with the traditional interpretation of the M code.

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.