a b s t r a c tIn a Turing test, a judge decides whether their conversation partner is either a machine or human. What cues does the judge use to determine this? In particular, are presumably unique features of human language actually perceived as humanlike? Participants rated the humanness of a set of sentences that were manipulated for grammatical construction: linear right-branching or hierarchical center-embedded and their plausibility with regard to world knowledge.We found that center-embedded sentences are perceived as less humanlike than right-branching sentences and more plausible sentences are regarded as more humanlike. However, the effect of plausibility of the sentence on perceived humanness is smaller for center-embedded sentences than for rightbranching sentences.Participants also rated a conversation with either correct or incorrect use of the context by the agent. No effect of context use was found. Also, participants rated a full transcript of either a real human or a real chatbot, and we found that chatbots were reliably perceived as less humanlike than real humans, in line with our expectation. We did, however, find individual differences between chatbots and humans.

Multiple studies have shown that women’s likelihood of receiving research funding is lower than that of their male colleagues. Thus far, all research on this gender gap in academia has focused on post-PhD academics, making it difficult to discern whether the female disadvantages in number of publications, previous grants, maternity leave, and h-indexes are at the root of the gender gap in received funding, or whether it is due to a more fundamental gender bias in academia. Therefore, we investigated whether female university students are already disadvantaged in receiving their first grant in their scientific career. We analysed data on applications (N = 2651) from 1995 to 2018 to the Leiden University International Study Fund (LISF), a fund dedicated to support students to study or conduct research abroad. We found that men and women applied equally often to the LISF. However, women had a lower success rate, which seemed to only get worse over recent years. Furthermore, male and female applications were assessed to be equal in quality when gender-related information was removed from them. The current study demonstrates that the factors that were assumed to contribute the most to the gender gap in more senior academics (e.g. previous grants, h-index) do not explain it fully: even when those factors do not yet play a role, such as in our student sample, women were found to have lower success rates than men. This underscores the importance of attacking gender biases at its roots.