Removing the effects of the site in brain imaging machine-learning Measurement and extendable benchmark

Multisite machine-learning neuroimaging studies, such as those conducted by the ENIGMA Consortium, need to remove the differences between sites to avoid effects of the site (EoS) that may prevent or fraudulently help the creation of prediction models, leading to impoverished or inflated prediction accuracy. Unfortunately, we have shown earlier that current Methods Aiming to Remove the EoS (MAREoS, e.g., ComBat) cannot remove complex EoS (e.g., including interactions between regions). And complex EoS may bias the accuracy. To overcome this hurdle, groups worldwide are developing novel MAREoS. However, we cannot assess their effectiveness because EoS may either inflate or shrink the accuracy, and MAREoS may both remove the EoS and degrade the data. In this work, we propose a strategy to measure the effectiveness of a MAREoS in removing different types of EoS. FOR MAREOS DEVELOPERS, we provide two multisite MRI datasets with only simple true effects (i.e., detectable by most machine-learning algorithms) and two with only simple EoS (i.e., removable by most MAREoS). First, they should use these datasets to fit machine-learning algorithms after applying the MAREoS. Second, they should use the formulas we provide to calculate the relative accuracy change associated with the MAREoS in each dataset and derive an EoS-removal effectiveness statistic. We also offer similar datasets and formulas for complex true effects and EoS that include first-order interactions. FOR MACHINE-LEARNING RESEARCHERS, we provide an extendable benchmark website to show: a) the types of EoS they should remove for each given machine-learning algorithm and b) the effectiveness of each MAREoS for removing each type of EoS. Relevantly, a MAREoS only able to remove the simple EoS may suffice for simple machine-learning algorithms, whereas more complex algorithms need a MAREoS that can remove more complex EoS. For instance, ComBat removes all simple EoS as needed for predictions based on simple lasso algorithms, but it leaves residual complex EoS that may bias the predictions based on standard support vector machine algorithms

Reliability of moral decision-making: Evidence from the trolley dilemma

International audienceThe application of framing effects in the field of moral judgement has offered a golden opportunity to assess the reliability of people’s moral judgements and decisions. To date, however, these studies are still scarce and they suffer from multiple methodological issues. Therefore, this study aims to provide further insights into the reliability of moral judgements while fixing these methodological shortcomings. In this study, we employed the classic trolley dilemma moral decision-making paradigm to determine the extent to which moral decisions are susceptible to framing effects. A total of 1,040 participants were included in the study. The data revealed that choices of participants did not significantly differ between the two frames. Equivalence tests confirmed that the associated effect size was very small. Further exploratory analyses revealed an unplanned interaction between the framing effect and the target of the framing manipulation. This result became from marginally statistically significant to insignificant following different sensitivity analyses. The implications and limitations of these findings and directions for future research are discussed

Association of sleep with moral severity to accidental harm transgressions: A cross‐sectional study

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The interplay between the importance of a decision and emotion in decision-making

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Tell Me about Regret: Studying Regret Inferences from Written Stories in Adults and Children

International audienceEmotional development relies on the development of emotional concepts and emotion labels. We examined 3rd-5th grade children’s ability to label and identify regret and disappointment – i.e., choosing an emotional label or reporting an emotional intensity among a pre-established list of emotions. Sixty-one 3rd-grade children, 63 4th-grade children, 71 5th-grade children and 80 adults (18-30 years) read short stories (116 words on average) designed to elicit sadness, anger, shame, guilt, disappointment and regret. We assessed emotion labeling by asking participants to label the emotions felt after each story, and we assessed emotional identification through the reporting of the intensity with which they felt listed emotions. Children identified disappointment at 8-9 years, and regret at 10-11 years. However, regret and disappointment labeling remained rare at 10-11 years. Our results indicate that the identification of these emotions precedes their labeling and show a developmental increase in regret and disappointment recognition from 3rd to 5th grade

Tell Me about Regret: Studying Regret Inferences from Written Stories in Adults and Children

International audienceEmotional development relies on the development of emotional concepts and emotion labels. We examined 3rd-5th grade children’s ability to label and identify regret and disappointment – i.e., choosing an emotional label or reporting an emotional intensity among a pre-established list of emotions. Sixty-one 3rd-grade children, 63 4th-grade children, 71 5th-grade children and 80 adults (18-30 years) read short stories (116 words on average) designed to elicit sadness, anger, shame, guilt, disappointment and regret. We assessed emotion labeling by asking participants to label the emotions felt after each story, and we assessed emotional identification through the reporting of the intensity with which they felt listed emotions. Children identified disappointment at 8-9 years, and regret at 10-11 years. However, regret and disappointment labeling remained rare at 10-11 years. Our results indicate that the identification of these emotions precedes their labeling and show a developmental increase in regret and disappointment recognition from 3rd to 5th grade