Emotion regulation meets emotional attention: the influence of emotion suppression on emotional attention depends on the nature of the distracters

Recent evidence has suggested a crucial role of people’s current goals in attention to emotional information. This asks for research investigating how and what kinds of goals shape emotional attention. The present study investigated how the goal to suppress a negative emotional state influences attention to emotion-congruent events. After inducing disgust, we instructed participants to suppress all feelings of disgust during a subsequent dot probe task. Attention to disgusting images was modulated by the sort of distracter that was presented in parallel with disgusting imagery. When disgusting images were presented together with neutral images, emotion suppression was accompanied by a tendency to attend to disgusting images. However, when disgusting images were shown with positive images that allow coping with disgust (i.e., images representing cleanliness), attention tended away from disgusting images and toward images representing cleanliness. These findings show that emotion suppression influences the allocation of attention but that the successful avoidance of emotion-congruent events depends on the availability of effective distracters

Interpretability and Explainability: A Machine Learning Zoo Mini-tour

In this review, we examine the problem of designing interpretable and explainable machine learning models. Interpretability and explainability lie at the core of many machine learning and statistical applications in medicine, economics, law, and natural sciences. Although interpretability and explainability have escaped a clear universal definition, many techniques motivated by these properties have been developed over the recent 30 years with the focus currently shifting towards deep learning methods. In this review, we emphasise the divide between interpretability and explainability and illustrate these two different research directions with concrete examples of the state-of-the-art. The review is intended for a general machine learning audience with interest in exploring the problems of interpretation and explanation beyond logistic regression or random forest variable importance. This work is not an exhaustive literature survey, but rather a primer focusing selectively on certain lines of research which the authors found interesting or informative

(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Explainability

Ante-hoc interpretability has become the holy grail of explainable machine learning for high-stakes domains such as healthcare; however, this notion is elusive, lacks a widely-accepted definition and depends on the deployment context. It can refer to predictive models whose structure adheres to domain-specific constraints, or ones that are inherently transparent. The latter notion assumes observers who judge this quality, whereas the former presupposes them to have technical and domain expertise, in certain cases rendering such models unintelligible. Additionally, its distinction from the less desirable post-hoc explainability, which refers to methods that construct a separate explanatory model, is vague given that transparent predictors may still require (post-)processing to yield satisfactory explanatory insights. Ante-hoc interpretability is thus an overloaded concept that comprises a range of implicit properties, which we unpack in this paper to better understand what is needed for its safe deployment across high-stakes domains. To this end, we outline model- and explainer-specific desiderata that allow us to navigate its distinct realisations in view of the envisaged application and audience

Doing et undoing gender dans les crèches: une analyse desinteractions des éducateurs/éducatrices avec les enfants

Kinderkrippen sind für viele Kinder die erste Bildungsinstitution und für die Gleichstellung der Geschlechter bedeutsam. Im Beitrag wird auf der Basis einer ethnographischen Videostudie in vier Deutschschweizer Kinderkrippen untersucht, wie Gender in der pädagogischen Alltagspraxis der Kinderbetreuenden relevant wird. Für die Kodierung der Videodaten werden Interaktionsverläufe in Bezug auf doing und undoing gender, Dramatisierung und Dethematisierung analysiert. Die Ergebnisse zeigen, dass die Kinderbetreuenden das von Kindern gezeigte Verhalten, sei es doing oder undoing gender, verstärken, jedoch selten intervenieren um Gleichstellung herzustellen. Zur Förderung der Gleichstellung in der Kita sind die Organisationskultur und die pädagogische Qualität entscheidend. (DIPF/Orig.)Pour nombreux enfants, la crèche est la première institution éducative qu’ils fréquentent. La crèche donc est importante par rapport à la question de l’éducation à l’égalité des sexes. Dans cet article, sur la base d’une étude ethnographique et de données vidéo effectuées dans quatre crèches, nous examinons comment les questions de genre s’actualisent dans les pratiques pédagogiques quotidiennes. Le codage des interactions a été réalisé à partir des concepts de doing gender, undoing gender, dramatisation et dé-thématisation. Les résultats montrent que les éducateurs et éducatrices renforcent les comportements des enfants et exigent rarement l’égalité. Pour soutenir la question de l’éducation à l’égalité dans les crèches, la culture d’organisation et la qualité de l’éducation s’avèrent être primordiales. (DIPF/Orig.

Unintended allocation of spatial attention to goal-relevant but not to goal-related events

We investigated whether words relevant to a person’s current goal and words related to that goal influence the orienting of attention even when an intention to attend to the goal-relevant and goal-related stimuli is not present. Participants performed a modified spatial cueing paradigm combined with a second task that induced a goal. The results showed that the induced goal led to the orienting of attention to goal-relevant words in the spatial cueing task. This effect was not found for goal-related words. The results provide evidence or accounts of automatic goal pursuit, which state that goals automatically guide attention to goal-relevant events

Multiple goal management starts with attention: goal prioritizing affects the allocation of spatial attention to goal-relevant events

Prior studies have shown that attention is allocated to events relevant to the current goal of a person. Until now, research has focused on the implementation of a single goal leaving open the question of how attention is allocated when multiple goals are activated. We examined whether the allocation of spatial attention is affected by the prioritizing of one goal over another. The results of two dot probe studies showed that attention is oriented to stimuli relevant to a goal with high value when simultaneously presented with stimuli relevant to a goal with low value (Experiment 1) and to stimuli relevant to a goal with high expectancy of success that were simultaneously presented with stimuli relevant to a goal with low expectancy of success (Experiment 2). These findings demonstrate that the allocation of spatial attention is dependent on the motivational strength of goal pursuit

Me first? Positioning self in the attentional hierarchy

The attention system that allocates resources across competing aspects of the environment is influenced by biases toward particular types of stimuli, such as cues of threat (e.g., angry-face image), self-reference (e.g., own-face image) and current goals (e.g., food image when hungry). Here, we used dot probe tasks to investigate which of these stimulus types are prioritized in the attentional hierarchy, measuring response latency to dot probes presented in the same location as different face types. In Experiment 1, participants (N = 42) were presented with self, angry and neutral face images in the dot probe task, which revealed a clear attentional bias for self-images over both angry and neutral images. In Experiment 2, each participant (N = 69) was assigned a self, angry or neutral goal image for a secondary monitoring task designed to induce a temporary goal, and this image was included in the stimuli presented in the dot probe task. Again, self-cues were found to produce a strong attentional bias, but images associated with temporary goals were found to be the most effective source of attentional bias. Results are discussed in relation to the relative importance of self, threat and temporary goal cues in the attentional hierarchy

Safety first: instrumentality for reaching safety determines attention allocation under threat

Theories of attention to emotional information suggest that attentional processes prioritize threatening information. Here, we suggest that attention will prioritize the events that are most instrumental to a goal in a given context, which in threatening situations typically is reaching safety. To test our hypotheses, we used an attentional cueing paradigm that contained cues signaling imminent threat (i.e., aversive noises) as well as cues that allowed to avoid threat (instrumental safety signals). Correct reactions to instrumental safety signals seemingly allowed participants to lower the presentation rate of the threat. Experiment 1 demonstrates that attention prioritizes instrumental safety signals over threat signals. Experiment 2 replicates this finding and additionally compares instrumental safety signals to other action-relevant signals controlling for action relevance as cause of the effects. Experiment 3 demonstrates that when actions towards threat signals permit to avoid threat, attention prioritizes threat signals. Taken together, these re-sults support the view that instrumentality for reaching safety determines the allocation of attention under threat

Generation of Differentially Private Heterogeneous Electronic Health Records

Electronic Health Records (EHRs) are commonly used by the machine learning community for research on problems specifically related to health care and medicine. EHRs have the advantages that they can be easily distributed and contain many features useful for e.g. classification problems. What makes EHR data sets different from typical machine learning data sets is that they are often very sparse, due to their high dimensionality, and often contain heterogeneous (mixed) data types. Furthermore, the data sets deal with sensitive information, which limits the distribution of any models learned using them, due to privacy concerns. For these reasons, using EHR data in practice presents a real challenge. In this work, we explore using Generative Adversarial Networks to generate synthetic, heterogeneous EHRs with the goal of using these synthetic records in place of existing data sets for downstream classification tasks. We will further explore applying differential privacy (DP) preserving optimization in order to produce DP synthetic EHR data sets, which provide rigorous privacy guarantees, and are therefore shareable and usable in the real world. The performance (measured by AUROC, AUPRC and accuracy) of our model's synthetic, heterogeneous data is very close to the original data set (within 3 - 5% of the baseline) for the non-DP model when tested in a binary classification task. Using strong $(1, 10^{-5})$ DP, our model still produces data useful for machine learning tasks, albeit incurring a roughly 17% performance penalty in our tested classification task. We additionally perform a sub-population analysis and find that our model does not introduce any bias into the synthetic EHR data compared to the baseline in either male/female populations, or the 0-18, 19-50 and 51+ age groups in terms of classification performance for either the non-DP or DP variant