Cognitive Biases
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2022 ◽  
Vol 8 (1) ◽  
pp. 171-191
Stefan Schnell ◽  
Nils Norman Schiborr

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.

2022 ◽  
Vol 8 ◽  
Antonio Oliva ◽  
Simone Grassi ◽  
Giuseppe Vetrugno ◽  
Riccardo Rossi ◽  
Gabriele Della Morte ◽  

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.

Matteo Coen ◽  
Julia Sader ◽  
Noëlle Junod-Perron ◽  
Marie-Claude Audétat ◽  
Mathieu Nendaz

2022 ◽  
Vol 12 ◽  
Paula Carolina Ciampaglia Nardi ◽  
Evandro Marcos Saidel Ribeiro ◽  
José Lino Oliveira Bueno ◽  
Ishani Aggarwal

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.

2022 ◽  
Vol 12 ◽  
Vincent Berthet

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.

2022 ◽  
pp. 105339
Felipe Mendonça de Santana ◽  
Jayme Fogagnolo Cobra ◽  
Camille Pinto Figueiredo

2021 ◽  
pp. 155005942110701
Joshua BB Garfield ◽  
Ali Cheetham ◽  
Nicholas B Allen ◽  
Paul G Sanfilippo ◽  
Dan I Lubman

Opioid use disorder (OUD) has been linked to exaggerated attentional, affective, and arousal responses to opioid-related stimuli, as well as altered responses to other affective (eg, naturally rewarding or aversive) stimuli, particularly blunted responses to pleasant/rewarding stimuli. Both exaggerated responses to drug-related stimuli and reduced response to pleasant stimuli may influence the course of OUD and its treatment, however interpretation of studies thus far is limited by methodological issues. In the present study, we examined subjective ratings, and attenuation of the P3 component of the acoustic startle-evoked event-related potential (as a measure of attention), while viewing neutral, pleasant, unpleasant, and drug-related images. Participants prescribed opioid agonist treatment (OAT) for OUD (n = 82) were compared to a carefully-matched control group (n = 33) and to recently-abstinent participants with OUD (n = 22). Relative to controls, participants prescribed OAT gave higher positive valence ratings of drug images, and blunted valence responses to other affective images, but groups did not differ in terms of arousal ratings or P3 amplitude. Within the OAT group, linear modeling of associations between frequency of recent illicit opioid use and startle P3 amplitude found an association between increased recent illicit opioid use and reduced attention to pleasant, relative to unpleasant, images. The latter finding may have implications for interventions targeting cognitive biases in people with substance use disorder. In particular, they suggest that enhancing attention to pleasant stimuli may be as, if not more important, than the typical approach of trying to reduce attentional bias to drug-related stimuli.

2021 ◽  
Mengyin Jiang ◽  
Jie Sui

Abstract The self-bias is a robust effect where self-related information is processed with greater priority than other-related information. Interestingly, the advantages of self-bias can be extended to close others – faster and more accurate responses for one’s mother and best friend have been observed compared to strangers – suggesting that significant others play an important role in the formation of one’s self-concept. Moreover, important life experiences such as childbirth can also impact the self-concept. Motherhood is a major transformation for women as one prepares to become a mother while maintaining the integrity of the pre-pregnant self-concept to achieve an ideal maternal self. The current study explored how the transition into motherhood changes the self-concept and subsequently impact the categorization of information for family members in postpartum mothers. In two experiments, results consistently revealed biases towards the self and close kin (one’s baby and mother) regardless of stimulus type (names in Experiment 1, faces in Experiment 2) and response category (self/other, family/non-family, familiar/non-familiar). A family bias (for baby and mother) over friend was observed in the family/non-family but not in the familiar/non-familiar categorization task, suggesting that motherhood may enhance the boundary between family and non-family to facilitate the processing of family-related information.

2022 ◽  
Vol 68 ◽  
pp. 104-106
Angela Barskaya ◽  
David S. Wang ◽  
Vivek K. Moitra

2022 ◽  
Vol 27 (1) ◽  
pp. 1-5
Pamela Mosedale ◽  
Kathrine Blackie

In part 1 of this article, the authors looked at the enormous possibilities for medication errors to occur ( ). 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.

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