Semantic Concepts Research Articles

Evaluating semantic control with transcranial magnetic stimulation: a systematic review with meta-analysis

BackgroundThis meta-analysis investigates the role of specific brain regions in semantic control processes using Transcranial Magnetic Stimulation (TMS). According to the Controlled Semantic Cognition framework, control processes help manage the contextually appropriate retrieval of semantic information by activating a distributed neural network, including the inferior frontal gyrus, the posterior middle temporal gyrus, and inferior parietal lobule. Lesions in these areas can lead to difficulties in manipulating weakly activated or competing semantic information. Researchers have used TMS to simulate such deficits in healthy individuals.MethodBy synthesizing results from TMS studies that targeted these regions, we aimed to evaluate whether neurostimulation over these areas can effectively impair participants’ performance under high semantic control demands.ResultsResults from different meta-analytical approaches consistently showed no significant effects of TMS, especially after correcting for publication bias. Nevertheless, variability in experimental methodologies was evident.ConclusionThese findings raise questions about the effectiveness of TMS in simulating deficits in semantic control and highlight the need for methodological improvements in future studies to enhance reliability and interpretability.

Metaphorical conceptualization of AI in digital discourse

This study investigates the conceptual metaphors employed to characterize Artificial Intelligence (AI) within online public discourse. By using a cognitive semantic approach, this investigation aims to uncover how metaphors shape social perceptions of AI, revealing the cognitive mechanisms involved in making sense of this rapidly evolving technology. An adapted version of the Metaphor Identification Procedure (MIP) has been combined with semantic frames to analyze the metaphorical mappings between the source and target frames, offering a more precise examination of the metaphors' conceptual structure. The analysis reveals a spectrum of metaphors portraying AI both as a beneficial partner and a potential threat, reflecting diverse attitudes and concerns about its integration into society. By focusing on the frame level, this study provides a fine-grained understanding of how different aspects of AI are construed through familiar conceptual frames. The findings contribute to the field of Cognitive Semantics and offer valuable insights for AI developers, educators, and communicators, emphasizing the importance of metaphors in framing society’s understanding of emerging technologies.

Seamless Science: Lifting Experimental Mechanical Testing Lab Data to an Interoperable Semantic Representation

The scientific landscape is undergoing rapid transformations with the advent of the digital age which revolutionizes research methodologies. In materials science and engineering, an adoption of modern data management techniques is desirable to maximize the efficiency and accessibility of research efforts. Traditional practices in testing laboratories are usually inadequate for efficient data acquisition and utilization as they lead to local storage and difficulty in publication and correlation with other results. Electronic laboratory notebooks (ELNs) are promising prospects in this respect. Semantic concepts and ontologies enhance interoperability by standardizing experimental data representation. An in‐laboratory pipeline seamlessly integrating an ELN with transformation scripts to convert experimental into interoperable data in a machine‐actionable format is created in this study as a proof of concept. Tensile test results and the corresponding tensile test ontology are used exemplary. Linking ELN data to semantic concepts enriches the stored information while improving interpretability and reusability. Involving undergraduate students builds a bridge between theory and practice during their training and promotes their digital skills. This study underscores the potential of ELNs and knowledge representations as beneficial means toward improved data management practices that enhance collaborative research and education while ensuring compatibility with evolving standards and technologies.

Semantic Categories: Uncertainty and Similarity

This paper addresses understanding and categorizing language by using Markov categories to establish a mathematical framework for semantic concepts. This framework enables us to measure the semantic similarity between linguistic expressions within a given text. Furthermore, this approach enables the measurement and control of uncertainty in language categorization and the creation of metrics for evaluating semantic similarity. We provide use cases to demonstrate how the proposed methods can be applied and computed, focusing on their interpretability and the universality of categorical constructions. This work contributes to the field by offering a novel perspective on semantic similarity and uncertainty metrics in language processing, generating criteria to automate their computation.

Using data from cue presentations results in grossly overestimating semantic BCI performance

Neuroimaging studies have reported the possibility of semantic neural decoding to identify specific semantic concepts from neural activity. This offers promise for brain-computer interfaces (BCIs) for communication. However, translating these findings into a BCI paradigm has proven challenging. Existing EEG-based semantic decoding studies often rely on neural activity recorded when a cue is present, raising concerns about decoding reliability. To address this, we investigate the effects of cue presentation on EEG-based semantic decoding. In an experiment with a clear separation between cue presentation and mental task periods, we attempt to differentiate between semantic categories of animals and tools in four mental tasks. By using state-of-the-art decoding analyses, we demonstrate significant mean classification accuracies up to 71.3% during cue presentation but not during mental tasks, even with adapted analyses from previous studies. These findings highlight a potential issue when using neural activity recorded during cue presentation periods for semantic decoding. Additionally, our results show that semantic decoding without external cues may be more challenging than current state-of-the-art research suggests. By bringing attention to these issues, we aim to stimulate discussion and drive advancements in the field toward more effective semantic BCI applications.

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.

Project Based Learning Arabic Semantic Learning Based on Open Journal Systems at University

This study aims to identify the steps of Project Based Learning Arabic Semantic Learning Based on the Open Journal Systems for PBA INSURI Ponorogo students. The research method used is qualitative with a case study model. The research data is from student Arabic Semantic lecture activities using the Project Based Learning method. Data collection techniques using observation and documentation. The data analysis is presented with data reduction, data presentation, conclusion drawing, and verification. The results showed; The stages of Semantic Learning with the Project-Based Learning model using the Open Journal System Platform are Introduction to Topics and Objectives, Explanation and Orientation, Project Planning, Project Implementation, Presentation of Results, Reflection and Evaluation, Integration with the Curriculum, and Awards and Rewards. In the context of the Arabic Language Education Study Program at INSURI Ponorogo, applying PjBL in semantic learning can provide a deep and applicable learning experience. By utilizing the Open Journal System (OJS), students can contribute to peer reviews of journal articles while understanding semantic concepts in Arabic.

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.

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

AbstractTo 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.

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.