563 research outputs found
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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 peopleâs 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 crĂšches: une analyse desinteractions des Ă©ducateurs/Ă©ducatrices avec les enfants
Kinderkrippen sind fĂŒr viele Kinder die erste Bildungsinstitution und fĂŒr die Gleichstellung der Geschlechter bedeutsam. Im Beitrag wird auf der Basis einer ethnographischen Videostudie in vier Deutschschweizer Kinderkrippen untersucht, wie Gender in der pĂ€dagogischen Alltagspraxis der Kinderbetreuenden relevant wird. FĂŒr die Kodierung der Videodaten werden InteraktionsverlĂ€ufe 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, verstĂ€rken, jedoch selten intervenieren um Gleichstellung herzustellen. Zur Förderung der Gleichstellung in der Kita sind die Organisationskultur und die pĂ€dagogische QualitĂ€t entscheidend. (DIPF/Orig.)Pour nombreux enfants, la crĂšche est la premiĂšre institution Ă©ducative quâils frĂ©quentent. La crĂšche donc est importante par rapport Ă la question de lâĂ©ducation Ă lâĂ©galitĂ© des sexes. Dans cet article, sur la base dâune Ă©tude ethnographique et de donnĂ©es vidĂ©o effectuĂ©es dans quatre crĂšches, nous examinons comment les questions de genre sâactualisent dans les pratiques pĂ©dagogiques quotidiennes. Le codage des interactions a Ă©tĂ© rĂ©alisĂ© Ă partir des concepts de doing gender, undoing gender, dramatisation et dĂ©-thĂ©matisation. Les rĂ©sultats montrent que les Ă©ducateurs et Ă©ducatrices renforcent les comportements des enfants et exigent rarement lâĂ©galitĂ©. Pour soutenir la question de lâĂ©ducation Ă lâĂ©galitĂ© dans les crĂšches, la culture dâorganisation et la qualitĂ© de lâĂ©ducation sâavĂšrent ĂȘtre primordiales. (DIPF/Orig.
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Unintended allocation of spatial attention to goal-relevant but not to goal-related events
We investigated whether words relevant to a personâs 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
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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
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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
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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 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
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