Cerebral blood flow autoregulation is impaired in schizophrenia

Patients with schizophrenia have a higher risk of cardiovascular diseases and higher mortality from them than does the general population; however, the underlying mechanism remains unclear. Impaired cerebral autoregulation is associated with cerebrovascular diseases and their mortality. Increased or decreased cerebral blood flow in different brain regions has been reported in patients with schizophrenia, which implies impaired cerebral autoregulation. This study investigated the cerebral autoregulation in 21 patients with schizophrenia and 23 age- and sex-matched healthy controls. None of the participants had a history of cardiovascular diseases, hypertension, or diabetes. All participants underwent 10-min blood pressure and cerebral blood flow recording through finger plethysmography and Doppler ultrasonography, respectively. Cerebral autoregulation was assessed by analyzing two autoregulation indices: the mean blood pressure and cerebral blood flow correlation coefficient (Mx), and the phase shift between the waveforms of blood pressure and cerebral blood flow determined using transfer function analysis. Compared with the controls, the patients had a significantly higher Mx (0.257 vs. 0.399, p = 0.036) and lower phase shift (44.3° vs. 38.7° in the 0.07–0.20 Hz frequency band, p = 0.019), which indicated impaired maintenance of constant cerebral blood flow and a delayed cerebrovascular autoregulatory response. Impaired cerebral autoregulation may be caused by schizophrenia and may not be an artifact of coexisting medical conditions. The mechanism underlying impaired cerebral autoregulation in schizophrenia and its probable role in the development of cerebrovascular diseases require further investigation

Reactive Supervision: A New Method for Collecting Sarcasm Data

Sarcasm detection is an important task in affective computing, requiring large amounts of labeled data. We introduce reactive supervision, a novel data collection method that utilizes the dynamics of online conversations to overcome the limitations of existing data collection techniques. We use the new method to create and release a first-of-its-kind large dataset of tweets with sarcasm perspective labels and new contextual features. The dataset is expected to advance sarcasm detection research. Our method can be adapted to other affective computing domains, thus opening up new research opportunities.Comment: 7 pages, 2 figures, 8 tables. To be published in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020

LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis

Thematic analysis (TA) has been widely used for analyzing qualitative data in many disciplines and fields. To ensure reliable analysis, the same piece of data is typically assigned to at least two human coders. Moreover, to produce meaningful and useful analysis, human coders develop and deepen their data interpretation and coding over multiple iterations, making TA labor-intensive and time-consuming. Recently the emerging field of large language models (LLMs) research has shown that LLMs have the potential replicate human-like behavior in various tasks: in particular, LLMs outperform crowd workers on text-annotation tasks, suggesting an opportunity to leverage LLMs on TA. We propose a human-LLM collaboration framework (i.e., LLM-in-the-loop) to conduct TA with in-context learning (ICL). This framework provides the prompt to frame discussions with a LLM (e.g., GPT-3.5) to generate the final codebook for TA. We demonstrate the utility of this framework using survey datasets on the aspects of the music listening experience and the usage of a password manager. Results of the two case studies show that the proposed framework yields similar coding quality to that of human coders but reduces TA's labor and time demands.Comment: EMNLP 2023 Finding

Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing

The use of crowdworkers in NLP research is growing rapidly, in tandem with the exponential increase in research production in machine learning and AI. Ethical discussion regarding the use of crowdworkers within the NLP research community is typically confined in scope to issues related to labor conditions such as fair pay. We draw attention to the lack of ethical considerations related to the various tasks performed by workers, including labeling, evaluation, and production. We find that the Final Rule, the common ethical framework used by researchers, did not anticipate the use of online crowdsourcing platforms for data collection, resulting in gaps between the spirit and practice of human-subjects ethics in NLP research. We enumerate common scenarios where crowdworkers performing NLP tasks are at risk of harm. We thus recommend that researchers evaluate these risks by considering the three ethical principles set up by the Belmont Report. We also clarify some common misconceptions regarding the Institutional Review Board (IRB) application. We hope this paper will serve to reopen the discussion within our community regarding the ethical use of crowdworkers.Comment: To be published in NAACL-HLT 2021. 12 pages, 1 figure, 3 table