Search Results
Information Provision for Informed Consent Procedures in Psychological Research Under the General Data Protection Regulation: A Practical Guide
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.
The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning
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.
Inverse learning in Hilbert scales
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.
This looks More Like that: Enhancing Self-Explaining Models by prototypical relevance propagation: This Looks More Like That
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.