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Language processing requires memory retrieval to integrate current input with previous context and making predictions about upcoming input. We propose that prediction and retrieval are two sides of the same coin, i.e. functionally the same, as they both activate memory representations. Under this assumption, memory retrieval and prediction should interact: Retrieval interference can only occur at a word that triggers retrieval and a fully predicted word would not do that. The present study investigated the proposed interaction with event-related potentials (ERPs) during the processing of sentence pairs in German. Predictability was measured via cloze probability. Memory retrieval was manipulated via the position of a distractor inducing proactive or retroactive similarity-based interference. Linear mixed model analyses provided evidence for the hypothesised interaction in a broadly distributed negativity, which we discuss in relation to the interference ERP literature. Our finding supports the proposal that memory retrieval and prediction are functionally the same.
Intrinsic decomposition refers to the problem of estimating scene characteristics, such as albedo and shading, when one view or multiple views of a scene are provided. The inverse problem setting, where multiple unknowns are solved given a single known pixel-value, is highly under-constrained. When provided with correlating image and depth data, intrinsic scene decomposition can be facilitated using depth-based priors, which nowadays is easy to acquire with high-end smartphones by utilizing their depth sensors. In this work, we present a system for intrinsic decomposition of RGB-D images on smartphones and the algorithmic as well as design choices therein. Unlike state-of-the-art methods that assume only diffuse reflectance, we consider both diffuse and specular pixels. For this purpose, we present a novel specularity extraction algorithm based on a multi-scale intensity decomposition and chroma inpainting. At this, the diffuse component is further decomposed into albedo and shading components. We use an inertial proximal algorithm for non-convex optimization (iPiano) to ensure albedo sparsity. Our GPU-based visual processing is implemented on iOS via the Metal API and enables interactive performance on an iPhone 11 Pro. Further, a qualitative evaluation shows that we are able to obtain high-quality outputs. Furthermore, our proposed approach for specularity removal outperforms state-of-the-art approaches for real-world images, while our albedo and shading layer decomposition is faster than the prior work at a comparable output quality. Manifold applications such as recoloring, retexturing, relighting, appearance editing, and stylization are shown, each using the intrinsic layers obtained with our method and/or the corresponding depth data.
Reflecting in written form on one's teaching enactments has been considered a facilitator for teachers' professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers' written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers' written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback.
Previous research has shown that heritage speakers struggle with inflectional morphology. 'Limitations of online resources' for processing a non-dominant language has been claimed as one possible reason for these difficulties. To date, however, there is very little experimental evidence on real-time language processing in heritage speakers. Here we report results from a masked priming experiment with 97 bilingual (Turkish/German) heritage speakers and a control group of 40 non-heritage speakers of Turkish examining regular and irregular forms of the Turkish aorist. We found that, for the regular aorist, heritage speakers use the same morphological decomposition mechanism ('affix stripping') as control speakers, whereas for processing irregularly inflected forms they exhibited more variability (i.e., less homogeneous performance) than the control group. Heritage speakers also demonstrated semantic priming effects. At a more general level, these results indicate that heritage speakers draw on multiple sources of information for recognizing morphologically complex words.
Pronouns can sometimes covary with a non c-commanding quantifier phrase (QP). To obtain such 'telescoping' readings, a semantic representation must be computed in which the QP's semantic scope extends beyond its surface scope. Non-native speakers have been claimed to have more difficulty than native speakers deriving such non-isomorphic syntax-semantics mappings, but evidence from processing studies is scarce. We report the results from an eye-movement monitoring experiment and an offline questionnaire investigating whether native and non-native speakers of German can link personal pronouns to non c-commanding QPs inside relative clauses. Our results show that both participant groups were able to obtain telescoping readings offline, but only the native speakers showed evidence of forming telescoping dependencies during incremental parsing. During processing the non-native speakers focused on a discourse-prominent, non-quantified alternative antecedent instead. The observed group differences indicate that non-native comprehenders have more difficulty than native comprehenders computing scope-shifted representations in real time.