We know what attention is!

Visual and auditory attention in individuals with DYRK1A and SCN2A disruptive variants

This preliminary study sought to assess biomarkers of attention using electroencephalography (EEG) and eye tracking in two ultra-rare monogenic populations associated with autism spectrum disorder (ASD). Relative to idiopathic ASD (n = 12) and neurotypical comparison (n = 49) groups, divergent attention profiles were observed for the monogenic groups, such that individuals with DYRK1A (n = 9) exhibited diminished auditory attention condition differences during an oddball EEG paradigm whereas individuals with SCN2A (n = 5) exhibited diminished visual attention condition differences noted by eye gaze tracking when viewing social interactions. Findings provide initial support for alignment of auditory and visual attention markers in idiopathic ASD and neurotypical development but not monogenic groups. These results support ongoing efforts to develop translational ASD biomarkers within the attention domain.

Bilingualism modifies cognition through adaptation, not transfer

The standard explanation for bilingual effects on cognition is that an aspect of language processing transfers to nonverbal cognitive performance, leading to improvements in executive functioning. However, much evidence is incompatible with that view, and transfer across those domains seems unlikely. The present argument is that bilingual experience modifies cognition through an adaptation to the underlying attention system, making attention more efficient. 'Transfer' focuses on the overlap of specific processes, so task similarity predicts outcomes. By contrast, 'adaptation' focuses on recruitment of the modified resource, so the degree of attention required predicts outcome. In this view, bilinguals require less attentional effort than monolinguals for similar levels of performance, and outperform monolinguals on tasks with high attention demands regardless of task similarity.

Co-perceiving: Bringing the social into perception

Humans and other animals possess the remarkable ability to effectively navigate a shared perceptual environment by discerning which objects and spaces are perceived by others and which remain private to themselves. Traditionally, this capacity has been encapsulated under the umbrella of joint attention or joint action. In this comprehensive review, we advocate for a broader and more mechanistic understanding of this phenomenon, termed co-perception. Co-perception encompasses the sensitivity to the perceptual engagement of others and the capability to differentiate between objects perceived privately and those perceived commonly with others. It represents a distinct concept from mere simultaneous individual perception. Moreover, discerning between private and common objects doesn't necessitate intricate mind-reading abilities or mutual coordination. The act of perceiving objects as either private or common provides a comprehensive account for social scenarios where individuals simply share the same context or may even engage in competition. This conceptual framework encourages a re-examination of classical paradigms that demonstrate social influences on perception. Furthermore, it suggests that the impacts of shared experiences extend beyond affective responses, also influencing perceptual processes. This article is categorized under: Psychology > Attention Philosophy > Foundations of Cognitive Science Philosophy > Psychological Capacities.

Attention mechanism-enhanced graph convolutional neural network for unbalanced lithology identification

In this study, we propose a novel method for identifying lithology using an attention mechanism-enhanced graph convolutional neural network (AGCN). The aim of this method is to address the limitations of traditional approaches that evaluate unbalanced lithology by improving the identification of thin layers and small samples, while providing reliable data support for reservoir evaluation. To achieve this goal, we begin by using Principal Component Analysis (PCA) with maximum and minimum distance clustering (Max-min-distance) to correct the logging curves, which compensates for the low resolution of thin layers and enhances the accuracy of stratigraphic representation. Subsequently, we transform the logging data into graph-structured data by connecting distance similarity points and feature similarity points of the logging samples. We then use the graph convolutional network (GCN) to identify lithology, leveraging both labeled and unlabeled data to enhance the ability to identify lithology in small sample datasets. Additionally, our model incorporates a channel and spatial attention mechanism that assigns weights to the graph structure during lithology identification, improving the model's capability to discern differences across samples. To evaluate the performance of our model, we constructed a lithology dataset comprising five wells and conducted experiments. The results indicate that our approach achieves a maximum accuracy of 97.67%, surpassing the performance of a singlestructure model in lithology identification. In conclusion, our proposed method provides a promising and effective approach for unbalanced lithology identification, significantly improving accuracy levels.

Parallel Interactions Between Linguistic and Contextual Factors in Bilinguals

The necessity for introducing interactionist and parallelism approaches in different branches of cognitive science emerged as a reaction to classical sequential stage-based models. Functional psychological models that emphasized and explained how different components interact, dynamically producing cognitive and perceptual states, influenced multiple disciplines. Chiefly among them were experimental psycholinguistics and the many applied areas that dealt with humans' ability to process different types of information in different contexts. Understanding how bilinguals represent and process verbal and visual input, how their neural and psychological states facilitate such interactions, and how linguistic and nonlinguistic processing overlap, has now emerged as an important area of multidisciplinary research. In this article, we will review available evidence from different language-speaking groups of bilinguals in India with a focus on situational context. In the discussion, we will address models of language processing in bilinguals within a cognitive psychological approach with a focus on existent models of inhibitory control. The paper's stated goal will be to show that the parallel architecture framework can serve as a theoretical foundation for examining bilingual language processing and its interface with external factors such as social context.