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En diskussion av äldre och nyare tolkningar av Njals saga med utgångspunkt från ett antal böcker och ariklar från senare år.

Is there a universal hierarchy of the senses, such that some senses (e.g., vision) are more accessible to consciousness and linguistic description than others (e.g., smell)? The long-standing presumption in Western thought has been that vision and audition are more objective than the other senses, serving as the basis of knowledge and understanding, whereas touch, taste, and smell are crude and of little value. This predicts that humans ought to be better at communicating about sight and hearing than the other senses, and decades of work based on English and related languages certainly suggests this is true. However, how well does this reflect the diversity of languages and communities worldwide? To test whether there is a universal hierarchy of the senses, stimuli from the five basic senses were used to elicit descriptions in 20 diverse languages, including 3 unrelated sign languages. We found that languages differ fundamentally in which sensory domains they linguistically code systematically, and how they do so. The tendency for better coding in some domains can be explained in part by cultural preoccupations. Although languages seem free to elaborate specific sensory domains, some general tendencies emerge: for example, with some exceptions, smell is poorly coded. The surprise is that, despite the gradual phylogenetic accumulation of the senses, and the imbalances in the neural tissue dedicated to them, no single hierarchy of the senses imposes itself upon language.

Olfaction presents a particularly interesting arena to explore abstraction in language. Like other abstract domains, such as time, odours can be difficult to conceptualize. An odour cannot be seen or held, it can be difficult to locate in space, and for most people odours are difficult to verbalize. On the other hand, odours give rise to primary sensory experiences. Every time we inhale we are using olfaction to make sense of our environment. We present new experimental data from 30 Jahai hunter-gatherers from the Malay Peninsula and 30 matched Dutch participants from the Netherlands in an odour naming experiment. Participants smelled monomolecular odorants and named odours while reaction times, odour descriptors and facial expressions were measured. We show that while Dutch speakers relied on concrete descriptors, i.e. they referred to odour sources (e.g. smells like lemon), the Jahai used abstract vocabulary to name the same odours (e.g. musty). Despite this differential linguistic categorization, analysis of facial expressions showed that the two groups, nevertheless, had the same initial emotional reactions to odours. Critically, these cross-cultural data present a challenge for how to think about abstraction in language. This article is part of the theme issue ‘Varieties of abstract concepts: development, use and representation in the brain’.

Questions about the function of literature are highly relevant, especially considering today’s great concern for the status of literature and for young people’s reading. In this article we investigate easy-to-read, or easy reader, a text type rapidly progressing in Sweden, and increasingly highlighted as a solution to the weak interest in reading among young people. Easy reader books originate from the democratic argument that everyone has the right to be able to read well enough and understand information. Although a significant part of today’s Swedish book market consists of easy-to-read literature, we know very little about the attitudes towards this literature and its readers. While easy-to-read books are made accessible on the market through different channels, the authors and publishing houses play a particularly important role. This article describes and discusses Swedish authors’ and publishing houses’ values and attitudes to their books, their readers and their mission, through interviews with 15 authors and three publishing houses conducted during 2014–2015. The article shows that the will to do good is a major motivation to act within the easy-reading field. The authors and the publishing houses look upon their mission as very important for a group of struggling or reluctant readers. According to the authors and publishing houses, this group requires texts that are characterized by simplicity in terms of recognition of characters, settings and themes. They also want to remove the stigmatization of easy readers, at the same time as the books are intended to serve as a didactic tool. Our results also show that the majority of authors combine their writing with a large number of author visits in schools.

Background Assessment of artificial intelligence (AI)-based models across languages is crucial to ensure equitable access and accuracy of information in multilingual contexts. This study aimed to compare AI model efficiency in English and Arabic for infectious disease queries. Methods The study employed the METRICS checklist for the design and reporting of AI-based studies in healthcare. The AI models tested included ChatGPT-3.5, ChatGPT-4, Bing, and Bard. The queries comprised 15 questions on HIV/AIDS, tuberculosis, malaria, COVID-19, and influenza. The AI-generated content was assessed by two bilingual experts using the validated CLEAR tool. Results In comparing AI models’ performance in English and Arabic for infectious disease queries, variability was noted. English queries showed consistently superior performance, with Bard leading, followed by Bing, ChatGPT-4, and ChatGPT-3.5 ( P  = .012). The same trend was observed in Arabic, albeit without statistical significance ( P  = .082). Stratified analysis revealed higher scores for English in most CLEAR components, notably in completeness, accuracy, appropriateness, and relevance, especially with ChatGPT-3.5 and Bard. Across the five infectious disease topics, English outperformed Arabic, except for flu queries in Bing and Bard. The four AI models’ performance in English was rated as “excellent”, significantly outperforming their “above-average” Arabic counterparts ( P  = .002). Conclusions Disparity in AI model performance was noticed between English and Arabic in response to infectious disease queries. This language variation can negatively impact the quality of health content delivered by AI models among native speakers of Arabic. This issue is recommended to be addressed by AI developers, with the ultimate goal of enhancing health outcomes.

Advances in computer-assisted linguistic research have been greatly influential in reshaping linguistic research. With the increasing availability of interconnected datasets created and curated by researchers, more and more interwoven questions can now be investigated. Such advances, however, are bringing high requirements in terms of rigorousness for preparing and curating datasets. Here we present CLICS, a Database of Cross-Linguistic Colexifications (CLICS). CLICS tackles interconnected interdisciplinary research questions about the colexification of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research. This is done by addressing shortcomings of an earlier version of the database, CLICS2, and by supplying an updated version with CLICS3, which massively increases the size and scope of the project. We provide tools and guidelines for this purpose and discuss insights resulting from organizing student tasks for database updates. Measurement(s) Meaning • word Technology Type(s) digital curation • computational modeling technique • Semantics Factor Type(s) source data • language Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.11317625

Recording eye movement data with high quality is often a prerequisite for producing valid and replicable results and for drawing well-founded conclusions about the oculomotor system. Today, many aspects of data quality are often informally discussed among researchers but are very seldom measured, quantified, and reported. Here we systematically investigated how the calibration method, aspects of participants’ eye physiologies, the influences of recording time and gaze direction, and the experience of operators affect the quality of data recorded with a common tower-mounted, video-based eyetracker. We quantified accuracy, precision, and the amount of valid data, and found an increase in data quality when the participant indicated that he or she was looking at a calibration target, as compared to leaving this decision to the operator or the eyetracker software. Moreover, our results provide statistical evidence of how factors such as glasses, contact lenses, eye color, eyelashes, and mascara influence data quality. This method and the results provide eye movement researchers with an understanding of what is required to record high-quality data, as well as providing manufacturers with the knowledge to build better eyetrackers.

Formants (vocal tract resonances) are increasingly analyzed not only by phoneticians in speech but also by behavioral scientists studying diverse phenomena such as acoustic size exaggeration and articulatory abilities of non-human animals. This often involves estimating vocal tract length acoustically and producing scale-invariant representations of formant patterns. We present a theoretical framework and practical tools for carrying out this work, including open-source software solutions included in R packages soundgen and phonTools . Automatic formant measurement with linear predictive coding is error-prone, but formant_app provides an integrated environment for formant annotation and correction with visual and auditory feedback. Once measured, formants can be normalized using a single recording (intrinsic methods) or multiple recordings from the same individual (extrinsic methods). Intrinsic speaker normalization can be as simple as taking formant ratios and calculating the geometric mean as a measure of overall scale. The regression method implemented in the function estimateVTL calculates the apparent vocal tract length assuming a single-tube model, while its residuals provide a scale-invariant vowel space based on how far each formant deviates from equal spacing (the schwa function). Extrinsic speaker normalization provides more accurate estimates of speaker- and vowel-specific scale factors by pooling information across recordings with simple averaging or mixed models, which we illustrate with example datasets and R code. The take-home messages are to record several calls or vowels per individual, measure at least three or four formants, check formant measurements manually, treat uncertain values as missing, and use the statistical tools best suited to each modeling context.