Crosslinguistic Corpus Studies in Linguistic Typology

Corpus-based studies have become increasingly common in linguistic typology over recent years, amounting to the emergence of a new field that we call corpus-based typology. The core idea of corpus-based typology is to take languages as populations of utterances and to systematically investigate text production across languages in this sense. From a usage-based perspective, investigations of variation and preferences of use are at the core of understanding the distribution of conventionalized structures and their diachronic development across languages. Specific findings of corpus-based typological studies pertain to universals of text production, for example, in prosodic partitioning; to cognitive biases constraining diverse patterns of use, for example, in constituent order; and to correlations of diverse patterns of use with language-specific structures and conventions. We also consider remaining challenges for corpus-based typology, in particular the development of crosslinguistically more representative corpora that include spoken (or signed) texts, and its vast potential in the future.

Management of Medico-Legal Risks in Digital Health Era: A Scoping Review

Artificial intelligence needs big data to develop reliable predictions. Therefore, storing and processing health data is essential for the new diagnostic and decisional technologies but, at the same time, represents a risk for privacy protection. This scoping review is aimed at underlying the medico-legal and ethical implications of the main artificial intelligence applications to healthcare, also focusing on the issues of the COVID-19 era. Starting from a summary of the United States (US) and European Union (EU) regulatory frameworks, the current medico-legal and ethical challenges are discussed in general terms before focusing on the specific issues regarding informed consent, medical malpractice/cognitive biases, automation and interconnectedness of medical devices, diagnostic algorithms and telemedicine. We aim at underlying that education of physicians on the management of this (new) kind of clinical risks can enhance compliance with regulations and avoid legal risks for the healthcare professionals and institutions.

The Influence of Cognitive Biases and Financial Factors on Forecast Accuracy of Analysts

The objective of this study was to jointly analyze the importance of cognitive and financial factors in the accuracy of profit forecasting by analysts. Data from publicly traded Brazilian companies in 2019 were obtained. We used text analysis to assess the cognitive biases from the qualitative reports of analysts. Further, we analyzed the data using statistical regression learning methods and statistical classification learning methods, such as Multiple Linear Regression (MRL), k-dependence Bayesian (k-DB), and Random Forest (RF). The Bayesian inference and classification methods allow an expansion of the research line, especially in the area of machine learning, which can benefit from the examples of factors addressed in this research. The results indicated that, among cognitive biases, optimism had a negative relationship with forecasting accuracy while anchoring bias had a positive relationship. Commonality, to a lesser extent, also had a positive relationship with the analyst’s accuracy. Among financial factors, the most important aspects in the accuracy of analysts were volatility, indebtedness, and profitability. Age of the company, fair value, American Depositary Receipts (ADRs), performance, and loss were still important but on a smaller scale. The results of the RF models showed a greater explanatory power. This research sheds light on the cognitive as well as financial aspects that influence the analyst’s accuracy, jointly using text analysis and machine learning methods, capable of improving the explanatory power of predictive models, together with the use of training models followed by testing.

The Impact of Cognitive Biases on Professionals’ Decision-Making: A Review of Four Occupational Areas

The author reviewed the research on the impact of cognitive biases on professionals’ decision-making in four occupational areas (management, finance, medicine, and law). Two main findings emerged. First, the literature reviewed shows that a dozen of cognitive biases has an impact on professionals’ decisions in these four areas, overconfidence being the most recurrent bias. Second, the level of evidence supporting the claim that cognitive biases impact professional decision-making differs across the areas covered. Research in finance relied primarily upon secondary data while research in medicine and law relied mainly upon primary data from vignette studies (both levels of evidence are found in management). Two research gaps are highlighted. The first one is a potential lack of ecological validity of the findings from vignette studies, which are numerous. The second is the neglect of individual differences in cognitive biases, which might lead to the false idea that all professionals are susceptible to biases, to the same extent. To address that issue, we suggest that reliable, specific measures of cognitive biases need to be improved or developed.

Reducing medication errors in practice: part 2

In part 1 of this article, the authors looked at the enormous possibilities for medication errors to occur ( https://doi.org/10.12968/coan.2021.0033 ). In this second part, the authors consider what can be done to avoid medication errors happening in veterinary practice and how systems of work can be used to help. As identified in the Institute of Medicine's report To Err Is Human, most errors result from faulty systems and processes, not individuals. Before steps can be put in place to avoid medication errors, it must be acknowledged that we are all human and thus susceptible to cognitive biases and external influences that cause us to make mistakes. Hence, any interventions put in place should focus on adjusting systems of work to make it easier to do things right and more difficult to do things wrong.

Why Does Privacy Paradox Exist?

In this exploratory study, the main aim was to find, ‘why do people disclose information when they are concerned about their privacy?’. The reasons that provide a plausible explanation to the privacy paradox have been conjectural. From the analysis of the eighteen in-depth interviews using grounded theory, themes were then conceptualized. We found rational and irrational explanations in terms of cognitive biases and heuristics that explain the privacy paradox among mobile users. We figured out some reasons in this context of mobile computing which were not emphasized earlier in the privacy paradox literature such as Peanut Effect, Fear of Missing Out- FoMo, Learned Helplessness, and Neophiliac Personality. These results add to the privacy paradox discourse and provide implications for smartphone users for making privacy-related decisions more consciously rather than inconsiderately disclosing information. Also, the results would help marketers and policymakers design nudges and choice architectures that consider privacy decision-making hurdles.