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This article provides some insights into the complex relationships between thinking and behavioral patterns, bio graphical aspects and teaching style. The data was analyzed in the Grounded Theory tradition and with the help of ATLAS.ti. The results presented here offer preliminary findings only since the research is still ongoing. The focus is on the ways teachers deal with mistakes. Based on two case examples, it will be shown how the fear of making mistakes can lead to teacher-centered lessons, and thereby limiting pupils' possibilities to learn autonomously.
Up to now pathological health anxiety has been classified primarily as a somatoform disorder or a somatic symptom disorder in ICD and DSM. Theoretical and empirical evidence, however, suggest that pathological health anxiety basically represents an anxiety disorder. In this paper, it is argued that deficits in the treatment and perception of patients with pathological health anxiety as "difficult patients" are partly attributable to a lack of clarity in terms of nosology and with respect to central mechanisms of etiology and pathogenesis. Based on novel theoretical approaches for the explanation of pathological health anxiety, suggestions for an improved therapeutic practice are outlined. This approach focuses on a more intensive use of exposure-based treatment elements that are oriented to the inhibitory learning approach, which has already proven its effectiveness for other anxiety disorders.
Background:
Under the new psychotherapy law in Germany, standardized patients (SPs) are to become a standard component inpsychotherapy training, even though little is known about their authenticity.Objective:The present pilot study explored whether, followingan exhaustive two-day SP training, psychotherapy trainees can distinguish SPs from real patients.
Methods:
Twenty-eight psychotherapytrainees (M= 28.54 years of age,SD= 3.19) participated as blind raters. They evaluated six video-recorded therapy segments of trained SPsand real patients using the Authenticity of Patient Demonstrations Scale.
Results:
The authenticity scores of real patients and SPs did notdiffer (p= .43). The descriptive results indicated that the highest score of authenticity was given to an SP. Further, the real patients did notdiffer significantly from the SPs concerning perceived impairment (p= .33) and the likelihood of being a real patient (p= .52).
Conclusions:
The current results suggest that psychotherapy trainees were unable to distinguish the SPs from real patients. We therefore stronglyrecommend incorporating training SPs before application. Limitations and future research directions are discussed.
The business problem of having inefficient processes, imprecise process analyses, and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating, and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS), and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.
Prediction is often regarded as a central and domain-general aspect of cognition. This proposal extends to language, where predictive processing might enable the comprehension of rapidly unfolding input by anticipating upcoming words or their semantic features. To make these predictions, the brain needs to form a representation of the predictive patterns in the environment. Predictive processing theories suggest a continuous learning process that is driven by prediction errors, but much is still to be learned about this mechanism in language comprehension. This thesis therefore combined three electroencephalography (EEG) experiments to explore the relationship between prediction and implicit learning at the level of meaning.
Results from Study 1 support the assumption that the brain constantly infers und updates probabilistic representations of the semantic context, potentially across multiple levels of complexity. N400 and P600 brain potentials could be predicted by semantic surprise based on a probabilistic estimate of previous exposure and a more complex probability representation, respectively.
Subsequent work investigated the influence of prediction errors on the update of semantic predictions during sentence comprehension. In line with error-based learning, unexpected sentence continuations in Study 2 ¬– characterized by large N400 amplitudes ¬– were associated with increased implicit memory compared to expected continuations. Further, Study 3 indicates that prediction errors not only strengthen the representation of the unexpected word, but also update specific predictions made from the respective sentence context. The study additionally provides initial evidence that the amount of unpredicted information as reflected in N400 amplitudes drives this update of predictions, irrespective of the strength of the original incorrect prediction.
Together, these results support a central assumption of predictive processing theories: A probabilistic predictive representation at the level of meaning that is updated by prediction errors. They further propose the N400 ERP component as a possible learning signal. The results also emphasize the need for further research regarding the role of the late positive ERP components in error-based learning. The continuous error-based adaptation described in this thesis allows the brain to improve its predictive representation with the aim to make better predictions in the future.