A large-scale study and six-month follow-up of an intervention to reduce causal illusions in high school students.

Causal illusions consist of believing that there is a causal relationship between events that are actually unrelated. This bias is associated with pseudoscience, stereotypes and other unjustified beliefs. Thus, it seems important to develop educational interventions to reduce them. To our knowledge, the only debiasing intervention designed to be used at schools was developed by Barberia et al. (Barberia et al. 2013 PLoS One 8, e71303 (doi:10.1371/journal.pone.0071303)), focusing on base rates, control conditions and confounding variables. Their assessment used an active causal illusion task where participants could manipulate the candidate cause. The intervention reduced causal illusions in adolescents but was only tested in a small experimental project. The present research evaluated it in a large-scale project through a collaboration with the Spanish Foundation for Science and Technology (FECYT), and was conducted in schools to make it ecologically valid. It included a pilot study (n = 287), a large-scale implementation (n = 1668; 40 schools) and a six-month follow-up (n = 353). Results showed medium-to-large and long-lasting effects on the reduction of causal illusions. To our knowledge, this is the first research showing the efficacy and long-term effects of a debiasing intervention against causal illusions that can be used on a large scale through the educational system.

Ten misconceptions regarding decision-making in critical care.

Diagnostic errors are prevalent in critical care practice and are associated with patient harm and costs for providers and the healthcare system. Patient complexity, illness severity, and the urgency in initiating proper treatment all contribute to decision-making errors. Clinician-related factors such as fatigue, cognitive overload, and inexperience further interfere with effective decision-making. Cognitive science has provided insight into the clinical decision-making process that can be used to reduce error. This evidence-based review discusses ten common misconceptions regarding critical care decision-making. By understanding how practitioners make clinical decisions and examining how errors occur, strategies may be developed and implemented to decrease errors in Decision-making and improve patient outcomes.

A Feasible Estimation of a "Corrected" EQ-5D Social Tariff.

OBJECTIVES: To demonstrate the feasibility of estimating a social tariff free of utility curvature and probability weighting biases and to test transferability between riskless and risky contexts. METHODS: Valuations for a selection of EQ-5D-3L health states were collected from a large and representative sample (N = 1676) of the Spanish general population through computer-assisted personal interviewing. Two elicitation methods were used: the traditional time trade-off (TTO) and a novel risky-TTO procedure. Both methods are equivalent for better than death states, which allowed us to test transferability of utilities across riskless and risky contexts. Corrective procedures applied are based on rank-dependent utility theory, identifying parameter estimates at the individual level. All corrections are health-state specific, which is a unique feature of our corrective approach. RESULTS: Two corrected value sets for the EQ-5D-3L system are estimated, highlighting the feasibility of developing national tariffs under nonexpected utility theories, such as rank-dependent utility. Furthermore, transferability was not supported for at least half of the health states valued by our sample. CONCLUSIONS: It is feasible to estimate a social tariff by using interviewing techniques, sample sizes, and sample representativeness equivalent to prior studies designed to generate national value sets for the EQ-5D. Utilities obtained in distinct contexts may not be interchangeable. Our findings caution against routinely taking transferability of utility for granted.

Adieu Bias: Debiasing Intuitions Among French Speakers.

Recent debiasing studies have shown that a short, plain-English explanation of the correct solution strategy can improve reasoning performance. However, these studies have predominantly focused on English-speaking populations, who were tested with problem contents designed for an English-speaking test environment. Here we explore whether the key findings of previous debiasing studies can be extended to native French speakers living in continental Europe (France). We ran a training session with a battery of three reasoning tasks (i.e., base-rate neglect, conjunction fallacy, and bat-and-ball) on 147 native French speakers. We used a two-response paradigm in which participants first gave an initial intuitive response, under time pressure and cognitive load, and then gave a final response after deliberation. Results showed a clear training effect, as early as the initial (intuitive) stage. Immediately after training, most participants solved the problems correctly, without the need for a deliberation process. The findings confirm that the intuitive debiasing training effect extends to native French speakers.

How Can Debiasing Research Aid Efforts to Reduce Discrimination?

PUBLIC ABSTRACT: Scientists studying intergroup biases are often concerned with lessening discrimination (unequal treatment of one social group versus another), but many interventions for reducing such biased behavior have weak or limited evidence. In this review article, we argue one productive avenue for reducing discrimination comes from adapting interventions in a separate field-judgment and decision-making-that has historically studied "debiasing": the ways people can lessen the unwanted influence of irrelevant information on decision-making. While debiasing research shares several commonalities with research on reducing intergroup discrimination, many debiasing interventions have relied on methods that differ from those deployed in the intergroup bias literature. We review several instances where debiasing principles have been successfully applied toward reducing intergroup biases in behavior and introduce other debiasing techniques that may be well-suited for future efforts in lessening discrimination.

Cognitive Bias in the Patient Encounter: Part II. Debiasing using an adaptive toolbox.

Cognitive bias may lead to medical error, and awareness of cognitive pitfalls is a potential first step to addressing the negative consequences of cognitive bias (see Part 1). For decision-making processes that occur under uncertainty, which encompass most physician decisions, a so-called "adaptive toolbox" is beneficial for good decisions. The adaptive toolbox is inclusive of broad strategies like cultural humility, emotional intelligence, and self-care that help combat implicit bias, negative consequences of affective bias, and optimize cognition. Additionally, the adaptive toolbox includes situational-specific tools such as heuristics, narratives, cognitive forcing functions, and fast and frugal trees. Such tools may mitigate against errors due to cultural, affective, and cognitive bias. Part 2 of this two-part series covers metacognition and cognitive bias in relation to broad and specific strategies aimed at better decision-making.

People see more of their biases in algorithms.

Algorithmic bias occurs when algorithms incorporate biases in the human decisions on which they are trained. We find that people see more of their biases (e.g., age, gender, race) in the decisions of algorithms than in their own decisions. Research participants saw more bias in the decisions of algorithms trained on their decisions than in their own decisions, even when those decisions were the same and participants were incentivized to reveal their true beliefs. By contrast, participants saw as much bias in the decisions of algorithms trained on their decisions as in the decisions of other participants and algorithms trained on the decisions of other participants. Cognitive psychological processes and motivated reasoning help explain why people see more of their biases in algorithms. Research participants most susceptible to bias blind spot were most likely to see more bias in algorithms than self. Participants were also more likely to perceive algorithms than themselves to have been influenced by irrelevant biasing attributes (e.g., race) but not by relevant attributes (e.g., user reviews). Because participants saw more of their biases in algorithms than themselves, they were more likely to make debiasing corrections to decisions attributed to an algorithm than to themselves. Our findings show that bias is more readily perceived in algorithms than in self and suggest how to use algorithms to reveal and correct biased human decisions.