Semantic Concepts Research Articles

О машинном обучении, мифах о сильном искусственном интеллекте и о том, что такое понимание

The first part of the article discusses the book The Myth of Artificial Intelligence by American scientist and entrepre-neur E. Larson, which focuses on debunking some myths about artificial intelligence. These myths, which have persist-ed for over half a century, suggest that the emergence of human-like ("general") AI and eventually superintelligence is inevitable, occurring naturally as AI systems evolve. The book criticizes these myths in two ways: scientific and social. It is shown that machine learning does not lead to general AI, and the myth of AI makes human potential look weaker. The second part of the article considers the problem of understanding. The concept of cognitive semantics is proposed, based on the ideas of J. Lakoff, S. Pinker, A. Damasio and A. Seth. In particular, it is noted that: understanding is an interpretation in terms of a person’s picture of the world; the picture of the world is constructed by our brain, and it is structured through the categorization of human experience; meanings (senses) are formed earlier than conceptual structures are formed; biological goals underlie meanings; not only the brain but also the body participates in cognitive processes, and understanding is associated with actions in the environment, knowledge of which is contained in the picture of the world. The article concludes by pointing out dead ends, difficulties and dangers on the path to general AI.

Indian philosophy as a precursor of the string theory of consciousness. Part 2

Introduction. The article examines the formation of the Ancient Indian philosophy and its connection to consciousness. The Indian philosophy has come a long way from its onset, in parallel with the Chinese and Ancient Greek philosophy. At the same time, the ideas of some of its branches and schools differ in a number of features. Purpose setting. By the example of the Sankhya school of philosophy, we have explored the evolution of the views of Ancient Indian philosophers on the evolution of the Universe, nature and man. We have put particular emphasis on the concept of consciousness.Methodology and methods of the study. In Sankhya system, the fundamental Prakriti and Purusha principles are identified. Other elements are supplementary. Ancient philosophers laid special emphasis on the three vibrating gunas. These are sattva, rajas, and tamas. We have revealed the functional connections between the gunas. The way they interact, in balance or dominance, determines the entire variety of material and spiritual processes. The evolution of the Universe in the Sankhya interpretation is close to the scientific theory of the Big Bang. The development of nature can be traced through the complication of atoms starting from the influence of a stimulus on unconscious chaos. The result of human evolution is the emergence of consciousness derivatives: intellect, mind and I (Ego). This allows better understanding of the behavioral characteristics of an existing personality. Yoga complements Sankhya system and makes it possible to transform consciousness in a number of states.Results. The analysis was carried out using scientific articles and monographs relatively unknown in the Russian press. Cognitive development occurs in a spiral. The scientific theory of strings appeared two and a half millennia after the emergence of the gunas concept. We have made a number of analogies between Ancient Indian gunas and contemporary string theory. And have substantiated the theory of consciousness not with quantum theory, but with a deeper string theory. The concept of consciousness is supported in line with the scientific paradigm of T. Kuhn. The leading ideas are the ideas about information of D. Chalmers and the physicist J. Wheeler.Conclusion. Thus, the phenomenon of consciousness has a silicon and biological basis. We insist on using the semantic concept of artificial consciousness instead of artificial intelligence. Therefore, between human consciousness and artificial consciousness there may be dangerous risks of dominance of the latter.

Inhibition of the inferior parietal lobe triggers state-dependent network adaptations

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in daily life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition.

Semantic feature salience matters: new values of significance and relative weight for the Spanish feature norms for young and older adults

Abstract To determine the semantic importance of certain features has been a topic of interest for many semantic memory models. Diverse measures have been proposed in the last years. Semantic significance, the latest measure proposed by Montefinese and colleagues, is both sophisticated and comprehensive. Given the cultural and linguistic variability of semantic measures, this study presents values of significance and relative weight for the Spanish-speaking population corresponding to 400 concrete concepts. First, we presented data for both young and older adults. Second, we assessed the effect of significance on response times in two speeded feature verification tasks. Third, we compared significance values with the existing Italian significance norms. To evaluate the effect of significance to predict response times, two speeded verification tasks (Experiment 1 and 2) were carried out, selecting a total of a 130 concepts for analysis. In Experiment 1, subjects were presented with a feature followed by a concept, while in Experiment 2, the order of stimulus presentation was reversed (i.e., the concept was presented before the feature). An independent linear mixed model showed that significance was a good predictor of response latencies in Experiments 1 and 2. Moreover, results revealed a strong positive correlation between the Spanish and Italian significance values. Findings are discussed in terms of recent theories of semantic cognition.

Witch, strumpet, maid and saint

Abstract Shakespeare’s Henry VI, Part 1 depicts Joan of Arc in contradictory terms. This paper discusses the narrative and literary techniques that are used to develop Joan of Arc’s characterisation, particularly the alternation of location between the Anglo-Burgundian and the French-Armagnac camp, the antagonistic staging of Joan of Arc compared to Lord Talbot and Margaret of Anjou, and the contrastive use of epithets, including the shifting positive, neutral or negative dictionary meanings and context-dependent meanings, as per Ray Jackendoff’s theory of Conceptual Semantics. This results in questioning the apparent meaning of words and concepts and thereby also the apparent judgment on the characters themselves, thus ambiguating, questioning, and ridiculing apparent certainties.

THE NOTION OF SEQUENTIALITY IN LANGUAGE: AN INVESTIGATION IN COGNITIVE SEMANTICS

AbstractThis research aims to clarify the concept of sequentiality in language: the case in which a number of things occur one after the other, as seen in phrases like a queue of customers, a strand of programs and a fleet of cars. Utilizing Langacker's theory of Cognitive Semantics, the study seeks to substantiate two claims. The first is that the meaning of a linguistic expression is understood in terms of the domain in which it is embedded. The second is that the meaning of a linguistic expression is the outcome of the specific construal imposed on its content. The paper proposes that the choice of a sequential phrase is influenced by how the speaker perceives and describe a situation. The study intends to demonstrate that each sequential phrase carries a unique meaning that sets it apart from others. When two or more sequential phrases modify a noun, they indicate semantic contrast, with each phrase offering a different perspective. The findings show that sequential phrases have distinct functions and are distinguishable in their usage through examples provided.

‘Long’, ‘flat’, ‘round’, ‘hard’, ‘heavy’, ‘sharp’

Abstract Cognitive linguists have not paid much attention to the conceptual semantics of basic adjectives used predicatively about physical objects, concentrating mainly on attributive constructions (e.g., adjective ordering, interactions between adjective and noun) or on figurative uses. Among the exceptions are Anna Wierzbicka (2006) on ‘shape’ descriptors and Goddard & Wierzbicka (2007) on ‘physical quality’ descriptors, both conducted within the NSM framework of semantic-conceptual analysis. Building on this work, the present article proposes original semantic-conceptual explications for the basic physical meanings of ‘long’, ‘flat’, ‘round’, ‘hard’, ‘heavy’, and ‘sharp’. The project has high significance because it can be argued that these meanings function as conceptual building blocks in the semantics of innumerable other words and concepts in the realm of the physical. The article showcases cutting-edge work in NSM semantics, including introducing the notion of ‘visuospatial parsing’ into NSM research and emphasizing the importance of embodied experience in human conceptualization.

Language proficiency is associated with neural representational dimensionality of semantic concepts

Previous studies suggest that semantic concepts are characterized by high-dimensional neural representations and that language proficiency affects semantic processing. However, it is not clear whether language proficiency modulates the dimensional representations of semantic concepts at the neural level. To address this question, the present study adopted principal component analysis (PCA) and representational similarity analysis (RSA) to examine the differences in representational dimensionalities (RDs) and in semantic representations between words in highly proficient (Chinese) and less proficient (English) language. PCA results revealed that language proficiency increased the dimensions of lexical representations in the left inferior frontal gyrus, temporal pole, inferior temporal gyrus, supramarginal gyrus, angular gyrus, and fusiform gyrus. RSA results further showed that these regions represented semantic information and that higher semantic representations were observed in highly proficient language relative to less proficient language. These results suggest that language proficiency is associated with the neural representational dimensionality of semantic concepts.

Handbook of Cognitive Semantics, Volume 1–4, edited by Li, Thomas Fuyin

Handbook of Cognitive Semantics, Volume 1–4, edited by Li, Thomas Fuyin

Evolving Representations of Childhood: A Diachronic Analysis of the Lexeme Child in British Newspapers from the 18th To 21st Centuries

This article investigates the semantic evolution of the lexeme CHILD in British newspaper discourse from the 18th to the 21st centuries, employing a diachronic approach and cognitive semantics framework. The study utilizes lexical analysis and frame modelling methodologies to trace changes in the lexical meaning of the lexeme CHILD during the mentioned period, drawing on dictionaries and newspaper archives. Findings reveal a dynamic semantic structure characterized by the emergence of new meanings and the persistence of core components like 'person' and 'juvenile'. The analysis identifies shifts influenced by societal, cultural, and ideological factors, reflecting changes in societal perceptions and linguistic representations over time. This research contributes to understanding how a language mirrors and shapes societal attitudes towards childhood across centuries, emphasizing the role of newspaper discourse in framing cultural norms and perceptions.

A Spatio-Temporal Capsule Neural Network with Self-Correlation Routing for EEG Decoding of Semantic Concepts of Imagination and Perception Tasks.

Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitation medicine as well as cognitive neuroscience. Electroencephalogram (EEG) is commonly used in the relevant fields, because it is a low-cost noninvasive technique with high temporal resolution. However, as EEG signals contain a high noise level resulting in a low signal-to-noise ratio, it makes decoding EEG-based semantic concepts for imagination and perception tasks (SCIP-EEG) challenging. Currently, neural network algorithms such as CNN, RNN, and LSTM have almost reached their limits in EEG signal decoding due to their own short-comings. The emergence of transformer methods has improved the classification performance of neural networks for EEG signals. However, the transformer model has a large parameter set and high complexity, which is not conducive to the application of BCI. EEG signals have high spatial correlation. The relationship between signals from different electrodes is more complex. Capsule neural networks can effectively model the spatial relationship between electrodes through vector representation and a dynamic routing mechanism. Therefore, it achieves more accurate feature extraction and classification. This paper proposes a spatio-temporal capsule network with a self-correlation routing mechaninsm for the classification of semantic conceptual EEG signals. By improving the feature extraction and routing mechanism, the model is able to more effectively capture the highly variable spatio-temporal features from EEG signals and establish connections between capsules, thereby enhancing classification accuracy and model efficiency. The performance of the proposed model was validated using the publicly accessible semantic concept dataset for imagined and perceived tasks from Bath University. Our model achieved average accuracies of 94.9%, 93.3%, and 78.4% in the three sensory modalities (pictorial, orthographic, and audio), respectively. The overall average accuracy across the three sensory modalities is 88.9%. Compared to existing advanced algorithms, the proposed model achieved state-of-the-art performance, significantly improving classification accuracy. Additionally, the proposed model is more stable and efficient, making it a better decoding solution for SCIP-EEG decoding.

Enhanced generalization and specialization of brain representations of semantic knowledge in healthy aging

Aging is often associated with a decrease in cognitive capacities. However, semantic memory appears relatively well preserved in healthy aging. Both behavioral and neuroimaging studies support the view that changes in brain networks contribute to this preservation of semantic cognition. However, little is known about the role of healthy aging in the brain representation of semantic categories. Here we used pattern classification analyses and computational models to examine the neural representations of living and non-living word concepts. The results demonstrate that brain representations of animacy in healthy aging exhibit increased similarity across categories, even across different task contexts. This pattern of results aligns with the neural dedifferentiation hypothesis that proposes that aging is associated with decreased specificity in brain activity patterns and less efficient neural resource allocation. However, the loss in neural specificity for different categories was accompanied by increased dissimilarity of item-based conceptual representations within each category. Taken together, the age-related patterns of increased generalization and specialization in the brain representations of semantic knowledge may reflect a compensatory mechanism that enables a more efficient coding scheme characterized by both compression and sparsity, thereby helping to optimize the limited neural resources and maintain semantic processing in the healthy aging brain.

Triangulating evidence in health sciences with Annotated Semantic Queries.

Integrating information from data sources representing different study designs has the potential to strengthen evidence in population health research. However, this concept of evidence "triangulation" presents a number of challenges for systematically identifying and integrating relevant information. These include the harmonization of heterogenous evidence with common semantic concepts and properties, as well as the priortization of the retrieved evidence for triangulation with the question of interest. We present Annotated Semantic Queries (ASQ), a natural language query interface to the integrated biomedical entities and epidemiological evidence in EpiGraphDB, which enables users to extract "claims" from a piece of unstructured text, and then investigate the evidence that could either support, contradict the claims, or offer additional information to the query. This approach has the potential to support the rapid review of preprints, grant applications, conference abstracts, and articles submitted for peer review. ASQ implements strategies to harmonize biomedical entities in different taxonomies and evidence from different sources, to facilitate evidence triangulation and interpretation. ASQ is openly available at https://asq.epigraphdb.org and its source code is available at https://github.com/mrcieu/epigraphdb-asq under GPL-3.0 license.

The Semantic Analysis of TOWARDS from the Perspective of Cognitive Linguistics

The utilization of language has contributed to the imperative need for comprehension, selection, and articulation of the semantics of words, phrases, sentences, paragraphs, etc. Indeed, meaning serves as an abstract entity within linguistic expressions, embodying the objects and phenomena of the world. Language signals are derived from human cognition, reflecting individuals' perceptions of the surrounding world. The evolution of cognition is intricately intertwined with and propels the advancement of the conceptual system and semantics, consequently impacting the content and structure of the language system. This article conducts an analysis of the semantic network of the preposition towards in Arthur Conan Doyle's The Complete Sherlock Holmes through cognitive perspective, encompassing spatial and non-spatial connotations. A quantitative method has been employed in this research endeavor to examine the meanings of this preposition. Subsequently, a limited-scale semantic network was established based on the findings. Furthermore, the result of this research can be used as a prototype for subsequent investigations.

Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning

The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially in domains where collecting large volumes of annotated data is not trivial. Biomedical data include several modalities containing complementary information, such as medical images and reports: images are often large and encode low-level information, while reports include a summarized high-level description of the findings identified within data and often only concerning a small part of the image. However, only a few methods allow to effectively link the visual content of images with the textual content of reports, preventing medical specialists from properly benefitting from the recent opportunities offered by deep learning models. This paper introduces a multimodal architecture creating a robust biomedical data representation encoding fine-grained text representations within image embeddings. The architecture aims to tackle data scarcity (combining supervised and self-supervised learning) and to create multimodal biomedical ontologies. The architecture is trained on over 6,000 colon whole slide Images (WSI), paired with the corresponding report, collected from two digital pathology workflows. The evaluation of the multimodal architecture involves three tasks: WSI classification (on data from pathology workflow and from public repositories), multimodal data retrieval, and linking between textual and visual concepts. Noticeably, the latter two tasks are available by architectural design without further training, showing that the multimodal architecture that can be adopted as a backbone to solve peculiar tasks. The multimodal data representation outperforms the unimodal one on the classification of colon WSIs and allows to halve the data needed to reach accurate performance, reducing the computational power required and thus the carbon footprint. The combination of images and reports exploiting self-supervised algorithms allows to mine databases without needing new annotations provided by experts, extracting new information. In particular, the multimodal visual ontology, linking semantic concepts to images, may pave the way to advancements in medicine and biomedical analysis domains, not limited to histopathology.

Macroscale brain states support the control of semantic cognition

A crucial aim in neuroscience is to understand how the human brain adapts to varying cognitive demands. This study investigates network reconfiguration during controlled semantic retrieval in differing contexts. We analyze brain responses to two semantic tasks of varying difficulty – global association and feature matching judgments – which are contrasted with non-semantic tasks on the cortical surface and within a whole-brain state space. Demanding semantic association tasks elicit activation in anterior prefrontal and temporal regions, while challenging semantic feature matching and non-semantic tasks predominantly activate posterior regions. Task difficulty also modulates activation along different dimensions of functional organization, suggesting different mechanisms of cognitive control. More demanding semantic association judgments engage cognitive control and default mode networks together, while feature matching and non-semantic tasks are skewed towards cognitive control networks. These findings highlight the brain’s dynamic ability to tailor its networks to support diverse neurocognitive states, enriching our understanding of controlled cognition.

An Overview of Cognitive Strategies in SLA of English in 21st Century

Cognitive linguistics scrutinizes the close relationship between the human brain, mind and SLA. Since the beginning of the 21st century, many new paradigms and approaches have emerged in this domain of SLA. Based on English as L2, this paper delves into the review in cognitive dimension from three main aspects: cognitive linguistics represented by constructional grammar, empirical cognitive science represented by neural-brain science and corpus research, as well as category and metaphor-related theories in cognitive semantics. These cognitive perspectives and strategies are interconnected and complementary, constituting a general and comprehensive sketch of new paradigms in cognitive SLA. This paper summarizes and analyzes how cognitive linguistics and cognitive science in the 21st century can provide coherent and appropriate theoretical guidance and methodological reference for the study of SLA, and meanwhile criticize and reflect on the shortcomings of the current research field and offers suggestions for future research directions.

Моделирование импликативной семантики ландшафтных номинаций в предложении (на материале немецкого языка)

The purpose of the study is to identify the relationship of the implicative semantics of landscape nominations with logical and grammatical types of sentences and to build models of implication of additional meanings of landscape nominations on this basis. The article explores the concept of implicative semantics and investigates how the logical and grammatical structure of German sentences relates to the implicit meanings of landscape-related terms found within them. These implicit meanings are derived from German linguistic culture. The scientific novelty of the study consists in the fact that for the first time the implicative semantics of landscape nominations is modeled in connection with the logical and grammatical types of the German sentence. As a result, based on the corpus data of the Digital Dictionary of the German Language, sentences containing nominations of large landscape forms are analyzed. The specifics of the semantic roles of landscape nominations in four main types of German sentences are described. The speaker's predicative connections with landscape objects and their implicative potential in German linguoculture are systematized. Models of the formation of the implicative meaning of landscape nominations are presented in the form of four types of frames: action, procedural, evaluative and the frame of "inclusion of the singular into the particular". The necessity of taking into account the speaker's intention in interpreting the implicative meaning in different types of discourse is shown.

Incomplete label distribution learning via label correlation decomposition

Label distribution learning (LDL) has garnered increased attention in recent studies on label ambiguity. However, collecting complete annotations for LDL tasks is often time-consuming and labor-intensive compared to traditional learning paradigms. Therefore, designing effective incomplete LDL algorithms is crucial to broaden LDL’s application scope. In this paper, we propose a novel LDL algorithm, called Incomplete Label Distribution Learning via Label Correlation Decomposition (IncomLDL-LCD), which simultaneously learns label distributions and recovers missing description degrees of labels through label correlations. Specifically, we decompose the label correlation into sparse local label correlation and low-rank global label correlation using a soft-thresholding operator and a singular value thresholding operator, respectively. The former is utilized to capture the related label subsets necessary for reconstructing each possible label, while the latter focuses on extracting the coarse-grained semantic concepts from all labels and exploring the groupings of labels. Additionally, we develop an alternating solution with the accelerated proximal gradient descent method for optimization. Extensive experiments on 16 real-world data sets with varying degrees of missing annotations validate that our algorithm effectively handles incomplete LDL tasks and outperforms state-of-the-art algorithms.

An integrated model of semantics and control.

Understanding the mechanisms enabling the learning and flexible use of knowledge in context-appropriate ways has been a major focus of research in the study of both semantic cognition and cognitive control. We present a unified model of semantics and control that addresses these questions from both perspectives. The model provides a coherent view of how semantic knowledge, and the ability to flexibly access and deploy that knowledge to meet current task demands, arises from end-to-end learning of the statistics of the environment. We show that the model addresses unresolved issues from both literatures, including how control operates over features that covary with one another and how control representations themselves are structured and emerge through learning, through a series of behavioral experiments and simulations. We conclude by discussing the implications of our approach to other fundamental questions in cognitive science, machine learning, and artificial intelligence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).