Attention capture by own name decreases with speech compression.

Auditory stimuli that are relevant to a listener have the potential to capture focal attention even when unattended, the listener's own name being a particularly effective stimulus. We report two experiments to test the attention-capturing potential of the listener's own name in normal speech and time-compressed speech. In Experiment 1, 39 participants were tested with a visual word categorization task with uncompressed spoken names as background auditory distractors. Participants' word categorization performance was slower when hearing their own name rather than other names, and in a final test, they were faster at detecting their own name than other names. Experiment 2 used the same task paradigm, but the auditory distractors were time-compressed names. Three compression levels were tested with 25 participants in each condition. Participants' word categorization performance was again slower when hearing their own name than when hearing other names; the slowing was strongest with slight compression and weakest with intense compression. Personally relevant time-compressed speech has the potential to capture attention, but the degree of capture depends on the level of compression. Attention capture by time-compressed speech has practical significance and provides partial evidence for the duplex-mechanism account of auditory distraction.

"Why can't I say people's names?" Alexinomia as a widespread psychological phenomenon.

Psychological research has recently proposed alexinomia, characterised by an inhibited behaviour in saying names, as a distinct psychosocial phenomenon. Alexinomia is associated with anxiety and avoidance behaviours with regards to saying names and thus severely impacts every day social interactions and relationships. This study aimed to explore the prevalence of this newly established and poorly understood psychological phenomenon and to further determine its impact on everyday life. For this purpose, online advice and discussion forums were systematically searched for threads on and mentions of problems with saying names. We analysed a broad dataset from English-language comments discussing alexinomia-related experiences and behaviours, inclusive of varied demographics and geographical regions. The findings based on the qualitative analysis of 257 unique sources show that alexinomia is a widespread phenomenon. Moreover, the analysed online materials showed affected individual's use of a variety of effective and ineffective coping strategies and experience varying degrees of severity, which can potentially diminish with training. The study's results therefore highlight alexinomia as a relevant, yet highly under researched, field of study, and add to our knowledge on the experience of alexinomia in everyday life and its potential origins, especially relating to social anxiety and early-life familial dynamic.

Semantic facilitation in blocked picture categorization: Some data and considerations regarding task selection.

Semantic context effects in picture naming and categorization tasks are central to the development and evaluation of current models of word production. When pictures are named in a semantically blocked context, response latencies are delayed. Belke (2013) found that when the naming task was replaced with a semantic categorization task (natural vs. man-made), response latencies were facilitated. From this pattern, she concluded that semantic interference in blocked picture naming has its locus at the lexical level but its origin at the preceding semantic level. However, other studies using the blocking procedure have failed to find facilitation in semantic categorization tasks (Damian et al., 2001; Riley et al., 2015), calling this conclusion into question. In three blocked picture naming and categorization experiments, we investigated different variables that might account for the discrepant results in semantic categorization. We used different semantic categorization tasks, different response modalities, different response set sizes, and different blocking procedures. Semantic facilitation was reliably found in naturalness categorization, but there was no semantic effect in natural size categorization. We discuss the implications of these findings for appropriate task selection. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A deep learning approach for Named Entity Recognition in Urdu language.

Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies.

Multiple names for multi-scope analytical methods, more than a semantic issue.

The use of multiple names for multi-scope analytical methods is often confusing. More precise classification to better accommodate the scope of analytical validation and application is needed.

Surname order and revaccination intentions during the COVID-19 pandemic.

Teachers in Japanese schools employ alphabetical surname lists that call students sooner, with surnames appearing early on these lists. We conducted Internet surveys nearly every month from March 2020 to September 2022 with the same participants, wherein we asked participants where the alphabetical columns of their childhood and adult surnames were located. We aimed to identify how surname order is important for the formation of noncognitive skills. During the data collection period, the COVID-19 vaccines became available; Japanese people could receive their third dose starting in December 2021. The 19th wave of the survey was conducted in January 2022. Therefore, to examine how a surname's alphabetical order could influence intention to revaccinate, we used a subsample of data from December 2021 to September 2022. The major findings were as follows. Women with early surnames had an approximately 4% stronger likelihood of having such intentions than men with early surnames. Early name order was more strongly correlated with revaccination intention among women than among men. The surname effect for women was larger when a mixed-gender list was used compared with when it was not used. This effect was only observed for childhood surnames and not for adult surnames.

Correcting sponge names: nomenclatural update of lower taxa level Porifera.

The online World Porifera Database (WPD), the Porifera part of the World Register of Marine Species (WoRMS), lists virtually all published scientific names of sponges. The names of the WPD (as indeed all names in WoRMS) are guided by the Code of the International Comnission on Zoological Nomenclature (ICZN). The WPD names include all currently accepted as well as original combinations, and a majority of non-accepted non-original combinations. Currently, among the accepted names about 200 original lower taxa combinations were found to be accepted by default, usually varieties or formae, which were given that status in the WPD because there was no sufficient published information to support arguments for or against the accepted status. After 1961, varieties and formae are considered infrasubspecific taxa whose names are not regulated by the Code and the names are unavailable, but prior to that date these trinominal taxa are potential available names. It is the purpose of the present study to evaluate these original default accepted combinations and arrive at an argumented judgement on whether they are to be truly accepted or non-accepted. Furthermore, additional lower taxa name violations of the Code are also included. Overall, there are three categories of names of lower taxa treated here, (1) with combinations, which are judged to be accepted with elevated status as (sub)specific taxa, (2) with combinations judged to be junior synonyms, and (3) with combinations, which are violating articles of the Code. Among the last category there are a small number of varietal taxa described after the 1960 cut-off date, which are unavailable but are proposed to have the original name combination retained, but as new names with authorship and year changed to those of the present article. Also, names found to be unavailable for various reasons (four-name combinations, junior homonyms, phylocode names) are in this category. The following 31 new names are proposed (three of which are junior synonyms of senior accepted names, and an additional three are unavailable names made available by employing the same name combination with the present authorship and year): Aaptos hoshinoi nom.nov., Ancorina nanosclera nom.nov., Axinella kurushima nom.nov., Callyspongia (Cladochalina) desqueyrouxfaundezae nom.nov., Cliona carpenteri subsp. hentscheli nom.nov., Callyspongia (Toxochalina) gustavoi nom.nov., Craniella microspira nom.nov., Dictyaulus romani nom.nov., Grantia breitfussi nom.nov., Haliclona alba subsp. albapontica nom.nov., Haliclona aquaeductus subsp. sebastopolensis nom.nov., Haliclona inflata subsp. vladimiri nom.nov., Haliclona informis subsp. voldomaroi nom.nov., Haliclona palmata subsp. pontuseuxiniensis nom.nov., Haliclona (Gellius) arthuri nom.nov., Haliclona (Gellius) godthaabae nom.nov., Haliclona merguiensis nom.nov., Haliclona senjitanitai nom.nov., Iophon hentscheli nom.nov., Leucandra wilsoni nom.nov., Paraleucilla bassensis nom.nov. (= P. saccharata), Pione carpenteri subsp. hentscheli nom.nov., Psammocinia samaaii nom.nov., Protoschmidtia czerniavskyi nom.nov. (= Metschnikowia tuberculata), Reiswiginella nom.nov., Scalarispongia lamarcki nom.nov., Spheciospongia hentscheli nom.nov., Spongia (Spongia) vonlendenfeldi nom.nov. (= S. (S.) lignosa), Suberites austral nom.nov., Suberites dendyi nom.nov., Suberites simae nom.nov., and Timea levii nom.nov.

The art of valuation: Using visual analysis to price classical paintings by Swedish Masters.

This study seeks to address the difficulty of pricing art and the limitations of conventional valuation models by using visual analysis to determine the price of paintings. We examine a large hand-collected sample of classical paintings by Swedish Masters, categorize them based on various dimensions, and reduce measurement error by visually examining and classifying each painting into a theme. We compare this 'visual' approach with the conventional 'terminological' approach. We find that the technique, theme, and auction house all have a substantial impact on the price. We argue that a visual inspection should take precedence over analysis based on the artwork's title. This is because the latter leaves many artworks unclassified and results in a systematic bias. The study demonstrates the importance of using art-informed characteristics to reduce measurement error in pricing paintings.

Data Augmentation with Nearest Neighbor Classifier for Few-Shot Named Entity Recognition.

Few-shot learning (FSL) is a category of machine learning models that are designed with the intent of solving problems that have small amounts of labeled data available for training. FSL research progress in natural language processing (NLP), particularly within the medical domain, has been notably slow, primarily due to greater difficulties posed by domain-specific characteristics and data sparsity problems. We explored the use of novel methods for text representation and encoding combined with distance-based measures for improving FSL entity detection. In this paper, we propose a data augmentation method to incorporate semantic information from medical texts into the learning process and combine it with a nearest-neighbor classification strategy for predicting entities. Experiments performed on five biomedical text datasets demonstrate that our proposed approach often outperforms other approaches.