Search Results

Now showing 1 - 8 of 8
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    Information Provision for Informed Consent Procedures in Psychological Research Under the General Data Protection Regulation: A Practical Guide
    (Thousand Oaks, CA : Sage Publishing, 2023) Hallinan, Dara; Boehm, Franziska; Külpmann, Annika Iris; Elson, Malte
    Psychological research often involves the collection and processing of personal data from human research participants. The European General Data Protection Regulation (GDPR) applies, as a rule, to psychological research conducted on personal data in the European Economic Area (EEA)—and even, in certain cases, to psychological research conducted on personal data outside the EEA. The GDPR elaborates requirements concerning the forms of information that should be communicated to research participants whenever personal data are collected directly from them. There is a general norm that informed consent should be obtained before psychological research involving the collection of personal data directly from research participants is conducted. The information required to be provided under the GDPR is normally communicated in the context of an informed consent procedure. There is reason to believe, however, that the information required by the GDPR may not always be provided. Our aim in this tutorial is thus to provide general practical guidance to psychological researchers allowing them to understand the forms of information that must be provided to research participants under the GDPR in informed consent procedures.
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    Inverse learning in Hilbert scales
    (Dordrecht [u.a.] : Springer Science + Business Media B.V, 2023) Rastogi, Abhishake; Mathé, Peter
    We study linear ill-posed inverse problems with noisy data in the framework of statistical learning. The corresponding linear operator equation is assumed to fit a given Hilbert scale, generated by some unbounded self-adjoint operator. Approximate reconstructions from random noisy data are obtained with general regularization schemes in such a way that these belong to the domain of the generator. The analysis has thus to distinguish two cases, the regular one, when the true solution also belongs to the domain of the generator, and the ‘oversmoothing’ one, when this is not the case. Rates of convergence for the regularized solutions will be expressed in terms of certain distance functions. For solutions with smoothness given in terms of source conditions with respect to the scale generating operator, then the error bounds can then be made explicit in terms of the sample size.
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    This looks More Like that: Enhancing Self-Explaining Models by prototypical relevance propagation: This Looks More Like That
    (Amsterdam : Elsevier, 2022) Gautam, Srishti; Höhne, Marina M.-C.; Hansen, Stine; Jenssen, Robert; Kampffmeyer, Michael
    Current machine learning models have shown high efficiency in solving a wide variety of real-world problems. However, their black box character poses a major challenge for the comprehensibility and traceability of the underlying decision-making strategies. As a remedy, numerous post-hoc and self-explanation methods have been developed to interpret the models’ behavior. Those methods, in addition, enable the identification of artifacts that, inherent in the training data, can be erroneously learned by the model as class-relevant features. In this work, we provide a detailed case study of a representative for the state-of-the-art self-explaining network, ProtoPNet, in the presence of a spectrum of artifacts. Accordingly, we identify the main drawbacks of ProtoPNet, especially its coarse and spatially imprecise explanations. We address these limitations by introducing Prototypical Relevance Propagation (PRP), a novel method for generating more precise model-aware explanations. Furthermore, in order to obtain a clean, artifact-free dataset, we propose to use multi-view clustering strategies for segregating the artifact images using the PRP explanations, thereby suppressing the potential artifact learning in the models.
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    Well-being in amyotrophic lateral sclerosis: A pilot experience sampling study
    (Lausanne : Frontiers Research Foundation, 2014) Real, R.G.; Dickhaus, T.; Ludolph, A.; Hautzinger, M.; Kübler, A.
    Objective: The aim of this longitudinal study was to identify predictors of instantaneous well-being in patients with amyotrophic lateral sclerosis (ALS). Based on flow theory well-being was expected to be highest when perceived demands and perceived control were in balance, and that thinking about the past would be a risk factor for rumination which would in turn reduce well-being. Methods: Using the experience sampling method, data on current activities, associated aspects of perceived demands, control, and well-being were collected from 10 patients with ALS three times a day for two weeks. Results: Results show that perceived control was uniformly and positively associated with well-being, but that demands were only positively associated with well-being when they were perceived as controllable. Mediation analysis confirmed thinking about the past, but not thinking about the future, to be a risk factor for rumination and reduced well-being. Discussion: Findings extend our knowledge of factors contributing to well-being in ALS as not only perceived control but also perceived demands can contribute to well-being. They further show that a focus on present experiences might contribute to increased well-being.
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    A Leak in the Academic Pipeline : Identity and Health Among Postdoctoral Women
    (Lausanne : Frontiers Research Foundation, 2019) Ysseldyk, Renate; Greenaway, Katharine H.; Hassinger, Elena; Zutrauen, Sarah; Lintz, Jana; Bhatia, Maya P.; Frye, Margaret; Starkenburg, Else; Tai, Vera
    Several challenges (e.g., sexism, parental leave, the glass ceiling, etc.) disproportionately affect women in academia (and beyond), and thus perpetuate the leaky pipeline metaphor for women who opt-out of an academic career. Although this pattern can be seen at all levels of the academic hierarchy, a critical time for women facing such challenges is during the postdoctoral stage, when personal life transitions and professional ambitions collide. Using a social identity approach, we explore factors affecting the mental health of postdoctoral women, including identity development (e.g., as a mother, a scientist) and lack of control (uncertainty about one’s future personal and professional prospects), which likely contribute to the leak from academia. In this mixed-method research, Study 1 comprised interviews with postdoctoral women in North America (n = 13) and Europe (n = 8) across a range disciplines (e.g., psychology, physics, political science). Common themes included the negative impact of career uncertainty, gender-based challenges (especially sexism and maternity leave), and work-life balance on mental and physical health. However, interviewees also described attempts to overcome gender inequality and institutional barriers by drawing on support networks. Study 2 comprised an online survey of postdoctoral women (N = 146) from a range of countries and academic disciplines to assess the relationships between social identification (e.g., disciplinary, gender, social group), perceived control (i.e., over work and life), and mental health (i.e., depression, anxiety, stress, and life satisfaction). Postdoctoral women showed mild levels of stress and depression, and were only slightly satisfied with life. They also showed only moderate levels of perceived control over one’s life and work. However, hierarchical regression analyses revealed that strongly identifying with one’s discipline was most consistently positively associated with both perceived control and mental health. Collectively, these findings implicate the postdoctoral stage as being stressful and tenuous for women regardless of academic background or nationality. They also highlight the importance of disciplinary identity as a potentially protective factor for mental health that, in turn, may diminish the rate at which postdoctoral women leak from the academic pipeline.
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    From face to face: the contribution of facial mimicry to cognitive and emotional empathy
    (Abingdon : Routledge, 2019) Drimalla, Hanna; Landwehr, Niels; Hess, Ursula; Dziobek, Isabel
    Despite advances in the conceptualisation of facial mimicry, its role in the processing of social information is a matter of debate. In the present study, we investigated the relationship between mimicry and cognitive and emotional empathy. To assess mimicry, facial electromyography was recorded for 70 participants while they completed the Multifaceted Empathy Test, which presents complex context-embedded emotional expressions. As predicted, inter-individual differences in emotional and cognitive empathy were associated with the level of facial mimicry. For positive emotions, the intensity of the mimicry response scaled with the level of state emotional empathy. Mimicry was stronger for the emotional empathy task compared to the cognitive empathy task. The specific empathy condition could be successfully detected from facial muscle activity at the level of single individuals using machine learning techniques. These results support the view that mimicry occurs depending on the social context as a tool to affiliate and it is involved in cognitive as well as emotional empathy.
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    The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning
    (Lausanne : Frontiers Research Foundation, 2022) von Hoyer, Johannes; Hoppe, Anett; Kammerer, Yvonne; Otto, Christian; Pardi, Georg; Rokicki, Markus; Yu, Ran; Dietze, Stefan; Ewerth, Ralph; Holtz, Peter
    Using a Web search engine is one of today’s most frequent activities. Exploratory search activities which are carried out in order to gain knowledge are conceptualized and denoted as Search as Learning (SAL). In this paper, we introduce a novel framework model which incorporates the perspective of both psychology and computer science to describe the search as learning process by reviewing recent literature. The main entities of the model are the learner who is surrounded by a specific learning context, the interface that mediates between the learner and the information environment, the information retrieval (IR) backend which manages the processes between the interface and the set of Web resources, that is, the collective Web knowledge represented in resources of different modalities. At first, we provide an overview of the current state of the art with regard to the five main entities of our model, before we outline areas of future research to improve our understanding of search as learning processes.
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    Onymity promotes cooperation in social dilemma experiments
    (Washington : American Association for the Advancement of Science (A A A S), 2017) Wang, Z.; Jusup, M.; Wang, R.-W.; Shi, L.; Iwasa, Y.; Moreno, Y.; Kurths, J.
    One of the most elusive scientific challenges for over 150 years has been to explain why cooperation survives despite being a seemingly inferior strategy from an evolutionary point of view. Over the years, various theoretical scenarios aimed at solving the evolutionary puzzle of cooperation have been proposed, eventually identifying several cooperation-promoting mechanisms: kin selection, direct reciprocity, indirect reciprocity, network reciprocity, and group selection. We report the results of repeated Prisoner’s Dilemma experiments with anonymous and onymous pairwise interactions among individuals. We find that onymity significantly increases the frequency of cooperation and the median payoff per round relative to anonymity. Furthermore, we also show that the correlation between players’ ranks and the usage of strategies (cooperation, defection, or punishment) underwent a fundamental shift, whereby more prosocial actions are rewarded with a better ranking under onymity. Our findings prove that reducing anonymity is a valid promoter of cooperation, leading to higher payoffs for cooperators and thus suppressing an incentive—anonymity—that would ultimately favor defection.