Neural correlates of retrospective memory confidence during face-name associative learning.

The ability to accurately assess one's own memory performance during learning is essential for adaptive behavior, but the brain mechanisms underlying this metamemory function are not well understood. We investigated the neural correlates of memory accuracy and retrospective memory confidence in a face-name associative learning task using magnetoencephalography in healthy young adults (n = 32). We found that high retrospective confidence was associated with stronger occipital event-related fields during encoding and widespread event-related fields during retrieval compared to low confidence. On the other hand, memory accuracy was linked to medial temporal activities during both encoding and retrieval, but only in low-confidence trials. A decrease in oscillatory power at alpha/beta bands in the parietal regions during retrieval was associated with higher memory confidence. In addition, representational similarity analysis at the single-trial level revealed distributed but differentiable neural activities associated with memory accuracy and confidence during both encoding and retrieval. In summary, our study unveiled distinct neural activity patterns related to memory confidence and accuracy during associative learning and underscored the crucial role of parietal regions in metamemory.

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.

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.

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).

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.

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.

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.