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The use of taboo words represents one of the most common and arguably universal linguistic behaviors, fulfilling a wide range of psychological and social functions. However, in the scientific literature, taboo language is poorly characterized, and how it is realized in different languages and populations remains largely unexplored. Here we provide a database of taboo words, collected from different linguistic communities (Study 1, N = 1046), along with their speaker-centered semantic characterization (Study 2, N = 455 for each of six rating dimensions), covering 13 languages and 17 countries from all five permanently inhabited continents. Our results show that, in all languages, taboo words are mainly characterized by extremely low valence and high arousal, and very low written frequency. However, a significant amount of cross-country variability in words’ tabooness and offensiveness proves the importance of community-specific sociocultural knowledge in the study of taboo language.

Pseudowords such as “knackets” or “spechy”—letter strings that are consistent with the orthotactical rules of a language but do not appear in its lexicon—are traditionally considered to be meaningless, and employed as such in empirical studies. However, recent studies that show specific semantic patterns associated with these words as well as semantic effects on human pseudoword processing have cast doubt on this view. While these studies suggest that pseudowords have meanings, they provide only extremely limited insight as to whether humans are able to ascribe explicit and declarative semantic content to unfamiliar word forms. In the present study, we employed an exploratory-confirmatory study design to examine this question. In a first exploratory study, we started from a pre-existing dataset of words and pseudowords alongside human-generated definitions for these items. Employing 18 different language models, we showed that the definitions actually produced for (pseudo)words were closer to their respective (pseudo)words than the definitions for the other items. Based on these initial results, we conducted a second, pre-registered, high-powered confirmatory study collecting a new, controlled set of (pseudo)word interpretations. This second study confirmed the results of the first one. Taken together, these findings support the idea that meaning construction is supported by a flexible form-to-meaning mapping system based on statistical regularities in the language environment that can accommodate novel lexical entries as soon as they are encountered.

Contains fulltext : 293316.pdf (Publisher’s version ) (Open Access), Language processing is influenced by sensorimotor experiences. Here, we review behavioral evidence for embodied and grounded influences in language processing across six linguistic levels of granularity. We examine (a) sub-word features, discussing grounded influences on iconicity (systematic associations between word form and meaning); (b) words, discussing boundary conditions and generalizations for the simulation of color, sensory modality, and spatial position; (c) sentences, discussing boundary conditions and applications of action direction simulation; (d) texts, discussing how the teaching of simulation can improve comprehension in beginning readers; (e) conversations, discussing how multi-modal cues improve turn taking and alignment; and (f) text corpora, discussing how distributional semantic models can reveal how grounded and embodied knowledge is encoded in texts. These approaches are converging on a convincing account of the psychology of language, but at the same time, there are important criticisms of the embodied approach and of specific experimental paradigms. The surest way forward requires the adoption of a wide array of scientific methods. By providing complimentary evidence, a combination of multiple methods on various levels of granularity can help us gain a more complete understanding of the role of embodiment and grounding in language processing.

Helical polyalanine (PA) molecules gathered a lot of interest as the propagation of electrons along the helical backbone structure comes along with spin polarization. Via liquid and solid scanning tunneling microscopy (STM) we studied the ordering of physisorbed and chemisorbed PA molecules on HOPG and Au surfaces. While enantiopure PA molecules adsorb in a hexagonally close-packed structure, we found heterochiral dimers with a rectangular unit cell for DL-PA. Despite the steric hindrance, the packing density of the DL-PA heterophase is increased by 25% compared to the enantiopure PA structure. Apparently, this is achieved by shifting D- and L-PA along their helical axis. Moreover, the alpha-helix structure of the PA molecules seems to be preserved; thus, electrostatic forces indeed play an important role for the formation and stabilization of the helical structure. In parallel, the interactions between PA homo- and heterochiral pairs were analyzed by van-der-Waals-corrected DFT-based tight binding calculations. Denser packing geometries can be reached by heterochiral PA pairs. Second, coarse-grained classical potentials were derived from the DFTB data, and the different PA phases seen in STM were also successfully obtained from Monte-Carlo simulations.

Language processing is influenced by sensorimotor experiences. Here, we review behavioral evidence for embodied and grounded influences in language processing across six linguistic levels of granularity. We examine (a) sub-word features, discussing grounded influences on iconicity (systematic associations between word form and meaning); (b) words, discussing boundary conditions and generalizations for the simulation of color, sensory modality, and spatial position; (c) sentences, discussing boundary conditions and applications of action direction simulation; (d) texts, discussing how the teaching of simulation can improve comprehension in beginning readers; (e) conversations, discussing how multi-modal cues improve turn taking and alignment; and (f) text corpora, discussing how distributional semantic models can reveal how grounded and embodied knowledge is encoded in texts. These approaches are converging on a convincing account of the psychology of language, but at the same time, there are important criticisms of the embodied approach and of specific experimental paradigms. The surest way forward requires the adoption of a wide array of scientific methods. By providing complimentary evidence, a combination of multiple methods on various levels of granularity can help us gain a more complete understanding of the role of embodiment and grounding in language processing.

This paper presents a comprehensive experimental study on the effect of extreme waves on a LNG carrier. The LNG carrier model was equipped with a variety of sensors to measure motions, green water height on deck as well as local and global loads. Experiments in transient wave packets provided the general performance in waves in terms of response amplitude operators and were accompanied by tests in regular waves with two different wave steepness. These tests allowed detailed insights into the nonlinear behavior of the vertical wave bending moment in steep waves showing that green water on deck can contribute to a decrease of vertical wave bending moment. Afterwards, systematic model tests in irregular waves were performed to provide the basis for statistical analysis. It is shown that the generalized extreme value distribution model is suitable for the estimation of the extreme peak values of motions and loads. Finally, model tests in tailored extreme wave sequences were conducted comparing the results with the statistical analysis. For this purpose, analytical breather solutions of the nonlinear Schrödinger equation were applied to generate tailored extreme waves of certain critical wave lengths in terms of ship response. Besides these design extreme waves, the LGN carrier was also investigated in the model scale reproduction of the real-world Draupner wave. By comparing the motions, vertical wave bending moment, green water column and slamming pressures it is concluded that the breather solutions are a powerful and efficient tool for the generation of design extreme waves of certain critical wave lengths for wave/structure investigations on different subjects.

Quantitative, data-driven models for mental representations have long enjoyed popularity and success in psychology (e.g., distributional semantic models in the language domain), but have largely been missing for the visual domain. To overcome this, we present ViSpa (Vision Spaces), high-dimensional vector spaces that include vision-based representation for naturalistic images as well as concept prototypes. These vectors are derived directly from visual stimuli through a deep convolutional neural network trained to classify images and allow us to compute vision-based similarity scores between any pair of images and/or concept prototypes. We successfully evaluate these similarities against human behavioral data in a series of large-scale studies, including off-line judgments—visual similarity judgments for the referents of word pairs (Study 1) and for image pairs (Study 2), and typicality judgments for images given a label (Study 3)—as well as online processing times and error rates in a discrimination (Study 4) and priming task (Study 5) with naturalistic image material. ViSpa similarities predict behavioral data across all tasks, which renders ViSpa a theoretically appealing model for vision-based representations and a valuable research tool for data analysis and the construction of experimental material: ViSpa allows for precise control over experimental material consisting of images and/or words denoting imageable concepts and introduces a specifically vision-based similarity for word pairs. To make ViSpa available to a wide audience, this article (a) includes (video) tutorials on how to use ViSpa in R and (b) presents a user-friendly web interface at http://vispa.fritzguenther.de.

ACNS 2022, Item does not contain fulltext

The electrical properties of single molecules can be investigated using atomically sharp metallic electrodes in mechanically controllable break junctions (MCBJs). The current-voltage (IV) characteristics of single molecules in such junctions are affected by the binding positions of the end groups on the tip-facets and tip-tip separation. In this poster, we present MCBJ experiments on N,N’-Bis(5-ethynylbenzenethiol-salicylidene)ethylenediamine (Salen). We discuss the evolution of the single level model (SLM) parameters namely, a) the energetic level (epsilon) of the dominant conducting channel and b) the coupling (Gamma) of the dominant conducting channel to the metallic electrodes. The SLM-parameters were evaluated for IV-curves recorded during opening measurements and fitted to the single level model. We explain the recurring peak-like features/protusions in the experimentally measured evolution of Gamma with increasing tip-tip separation, which we relate not only to the deformation of the molecule but also to the sliding of the anchor group above the electrode surface. We propose a novel, high-throughput approach to model the evolution of the SLM-parameters and perform transport calculations using the self-consistent charge scheme of the density-functional-based tight binding (SCC-DFTB) approach and the Green’s function formalism. Thereby, we consider many thermodynamically relevant configurations and assess the evolution of SLM-parameters using the SLM-curve fitting of the zero-bias transmission. The SLM-parameters are averaged using statistical weights obtained from a Metropolis simulation considering up to 200 000 configurations for selected tip-tip separations. The behavior of the averaged quantities with respect to the tip-tip separation reflects the experimentally observed evolution of the SLM-parameters astonishingly well.

Compound words (e.g., “catfish”) are combinations of constituent words that themselves possess free-standing meanings. Since the meaning of a compound is often related to the meaning of its constituents (i.e., the compound is semantically transparent; e.g., “snowball”), most compounds can be readily understood by a process of automatic composition. Indeed, evidence suggests that compound word processing involves both the attempt to retrieve its meaning from long-term memory (if the word is familiar) and to derive it from automatic constituent combination. In this context, many studies have relied on the behavioral effects of semantic transparency which, however, have been inconsistent, with some studies reporting facilitatory effects of transparency and others failing to detect any effect. To explain these inconsistencies, a recent study [1] showed that transparency effects may depend on participants reading experience. Concurrently, other studies suggest that transparency effects can be detected when defined “compositionally”, i.e., when transparency measures are obtained from computational models that model meaning composition explicitly. The present work brings together these perspectives by testing the interaction between compositional measures of semantic transparency and measures of reading experience. Measures of transparency were obtained from the CAOSS model [2], a computational model of compounding based on distributional semantics representations. This model learns to exploit the statistical regularities of compounding to generate a distributed representation of the predicted compound. Importantly, the CAOSS model allowed us to dissociate two factors underlying transparency: semantic composition and semantic consistency. Semantic composition captures the extent to which compound meaning can be predicted by constituent combination, while consistency captures the amount of meaning transformation required to turn the original constituent meaning into one tha

While distributional semantic models that represent word meanings as high-dimensional vectors induced from large text corpora have been shown to successfully predict human behavior across a wide range of tasks, they have also received criticism from different directions. These include concerns over their interpretability (how can numbers specifying abstract, latent dimensions represent meaning?) and their ability to capture variation in meaning (how can a single vector representation capture multiple different interpretations for the same expression?). Here, we demonstrate that semantic vectors can indeed rise up to these challenges, by training a mapping system (a simple linear regression) that predicts inter-individual variation in relational interpretations for compounds such as wood brush (for example brush FOR wood, or brush MADE OF wood) from (compositional) semantic vectors representing the meanings of these compounds. These predictions consistently beat different random baselines, both for familiar compounds (moon light, Experiment 1) as well as novel compounds (wood brush, Experiment 2), demonstrating that distributional semantic vectors encode variations in qualitative interpretations that can be decoded using techniques as simple as linear regression.

Word frequency is one of the best predictors of language processing. Typically, word frequency norms are entirely based on natural-language text data, thus representing what the literature typically refers to as purely linguistic experience. This study presents Flickr frequency norms as a novel word frequency measure from a domain-specific corpus inherently tied to extra-linguistic information: words used as image tags on social media. To obtain Flickr frequency measures, we exploited the photo-sharing platform Flickr Image (containing billions of photos) and extracted the number of uploaded images tagged with each of the words considered in the lexicon. Here, we systematically examine the peculiarities of Flickr frequency norms and show that Flickr frequency is a hybrid metrics, lying at the intersection between language and visual experience and with specific biases induced by being based on image-focused social media. Moreover, regression analyses indicate that Flickr frequency captures additional information beyond what is already encoded in existing norms of linguistic, sensorimotor, and affective experience. Therefore, these new norms capture aspects of language usage that are missing from traditional frequency measures: a portion of language usage capturing the interplay between language and vision, which – this study demonstrates – has its own impact on word processing. The Flickr frequency norms are openly available on the Open Science Framework (https://osf.io/2zfs3/).

BACKGROUND Improving health-related quality of life (HRQoL), disease severity, and treatment adherence through patient education is an increasingly important, yet relatively new area in dermatology. This randomized controlled trial aims to contribute to this growing area of research by exploring the effects of a 9-week educational program for patients with chronic skin diseases. OBJECTIVE The aim of the study was to evaluate the effect of a multidisciplinary educational program on HRQoL and disease severity in patients with psoriasis or atopic dermatitis (AD). METHODS Sixty-four patients with diagnosed psoriasis or AD were recruited from University Hospital Zurich and randomized (1:1) to the intervention or control group. To assess HRQoL, the following self-reported questionnaires were used: Dermatology Life Quality Index (DLQI), Skindex-29, EuroQol-5D (EQ-5D), RAND 36-Item Short Form Survey (SF-36), and Beck Depression Inventory (BDI) to measure depression symptoms. Psoriasis Area and Severity Index (PASI) and the Eczema Area and Severity Index (EASI) were used to capture disease extent. These scores were assessed at four study visits, which were performed at baseline and 3, 6, and 9 months after the start of the program. RESULTS At month 6, an improvement of at least 25% in BDI was recorded in 15 (68.2%) of 22 patients in the intervention group and 6 (27.3%) of 22 patients in the control group (difference 40.9%, p = 0.016). 53.3% (16 of 30) of patients achieved an improvement in one subdomain of the SF-36 score (role limitations due to emotional problems) at 6-month follow-up, compared with 23.1% (6 of 26) of those not attending the educational program (difference 30.2%; p = 0.042). No significant differences in DLQI, Skindex-29, EQ-5D, PASI, and EASI between both groups at the three time points were found. CONCLUSION An educational program may improve HRQoL and depression status of patients with psoriasis or AD.