Modelling word activation in semantic networks : three scaled entanglement models compared

Modelling how a word is activated in human memory is an important requirement for determining the probability of recall of a word in an extra-list cueing experiment. Previous research assumed a quantum-like model in which the semantic network was modelled as entangled qubits, however the level of activation was clearly being over-estimated. This paper explores three variations of this model, each of which are distinguished by a scaling factor designed to compensate the overestimation.

Comparing the semantic networks of deaf and hearing children using the DRM paradigm

This study uses the Deese-Roediger-McDermott paradigm to investigate how deaf children with cochlear implants organize their semantic networks as compared to their hearing age-mates.

Combining temporal and spectral information with spatial mapping to identify differences between phonological and semantic networks:a magnetoencephalographic approach

Early, lesion-based models of language processing suggested that semantic and phonological processes are associated with distinct temporal and parietal regions respectively, with frontal areas more indirectly involved. Contemporary spatial brain mapping techniques have not supported such clear-cut segregation, with strong evidence of activation in left temporal areas by both processes and disputed evidence of involvement of frontal areas in both processes. We suggest that combining spatial information with temporal and spectral data may allow a closer scrutiny of the differential involvement of closely overlapping cortical areas in language processing. Using beamforming techniques to analyze magnetoencephalography data, we localized the neuronal substrates underlying primed responses to nouns requiring either phonological or semantic processing, and examined the associated measures of time and frequency in those areas where activation was common to both tasks. Power changes in the beta (14-30 Hz) and gamma (30-50 Hz) frequency bandswere analyzed in pre-selected time windows of 350-550 and 500-700ms In left temporal regions, both tasks elicited power changes in the same time window (350-550 ms), but with different spectral characteristics, low beta (14-20 Hz) for the phonological task and high beta (20-30 Hz) for the semantic task. In frontal areas (BA10), both tasks elicited power changes in the gamma band (30-50 Hz), but in different time windows, 500-700ms for the phonological task and 350-550ms for the semantic task. In the left inferior parietal area (BA40), both tasks elicited changes in the 20-30 Hz beta frequency band but in different time windows, 350-550ms for the phonological task and 500-700ms for the semantic task. Our findings suggest that, where spatial measures may indicate overlapping areas of involvement, additional beamforming techniques can demonstrate differential activation in time and frequency domains. © 2012 McNab, Hillebrand, Swithenby and Rippon.

A tensor encoding model for semantic processing

This paper develops and evaluates an enhanced corpus based approach for semantic processing. Corpus based models that build representations of words directly from text do not require pre-existing linguistic knowledge, and have demonstrated psychologically relevant performance on a number of cognitive tasks. However, they have been criticised in the past for not incorporating sufficient structural information. Using ideas underpinning recent attempts to overcome this weakness, we develop an enhanced tensor encoding model to build representations of word meaning for semantic processing. Our enhanced model demonstrates superior performance when compared to a robust baseline model on a number of semantic processing tasks.

How activation, entanglement, and searching a semantic network contribute to event memory

Free association norms indicate that words are organized into semantic/associative neighborhoods within a larger network of words and links that bind the net together. We present evidence indicating that memory for a recent word event can depend on implicitly and simultaneously activating related words in its neighborhood. Processing a word during encoding primes its network representation as a function of the density of the links in its neighborhood. Such priming increases recall and recognition and can have long lasting effects when the word is processed in working memory. Evidence for this phenomenon is reviewed in extralist cuing, primed free association, intralist cuing, and single-item recognition tasks. The findings also show that when a related word is presented to cue the recall of a studied word, the cue activates it in an array of related words that distract and reduce the probability of its selection. The activation of the semantic network produces priming benefits during encoding and search costs during retrieval. In extralist cuing recall is a negative function of cue-to-distracter strength and a positive function of neighborhood density, cue-to-target strength, and target-to cue strength. We show how four measures derived from the network can be combined and used to predict memory performance. These measures play different roles in different tasks indicating that the contribution of the semantic network varies with the context provided by the task. We evaluate spreading activation and quantum-like entanglement explanations for the priming effect produced by neighborhood density.

Deriving word association networks from text corpora

This article presents and evaluates a model to automatically derive word association networks from text corpora. Two aspects were evaluated: To what degree can corpus-based word association networks (CANs) approximate human word association networks with respect to (1) their ability to quantitatively predict word associations and (2) their structural network characteristics. Word association networks are the basis of the human mental lexicon. However, extracting such networks from human subjects is laborious, time consuming and thus necessarily limited in relation to the breadth of human vocabulary. Automatic derivation of word associations from text corpora would address these limitations. In both evaluations corpus-based processing provided vector representations for words. These representations were then employed to derive CANs using two measures: (1) the well known cosine metric, which is a symmetric measure, and (2) a new asymmetric measure computed from orthogonal vector projections. For both evaluations, the full set of 4068 free association networks (FANs) from the University of South Florida word association norms were used as baseline human data. Two corpus based models were benchmarked for comparison: a latent topic model and latent semantic analysis (LSA). We observed that CANs constructed using the asymmetric measure were slightly less effective than the topic model in quantitatively predicting free associates, and slightly better than LSA. The structural networks analysis revealed that CANs do approximate the FANs to an encouraging degree.

Semantic data modelling and visualization using Noetica

Noetica is a tool for structuring knowledge about concepts and the reIationships between them. It differs from typical information systems in that the knowledge it represents is abstract, highly connected, and includes meta-knowledge (knowledge about knowledge). Noetica represents knowledge using a strongly typed graph data model. By providing a rich type system it is possible to represent conceptual information using formalized structures. A class hierarchy provides a basic classification for all objects. This allows for a consistency of representation that is not often found in `free' semantic networks, and gives the ability to easily extend a knowledge model while retaining its semantics. Visualization and query tools are provided for this data model. Visualization can be used to explore complete sets of link-classes, show paths while navigating through the database, or visualize the results of queries. Noetica supports goal-directed queries (a series of user-supplied goals that the system attempts to satisfy in sequence) and pathfinding queries (where the system finds relationships between objects in the database by following links).

Loose but normal: a semantic association study.

An abnormal facilitation of the spreading activation within semantic networks is thought to under-lie schizophrenics' remote associations and referential ideas. In normal subjects, elevated magical ideation (MI) has also been associated with a style of thinking similar to that of schizotypal subjects. We thus wondered whether normal subjects with a higher MI score would judge "loose associations" as being more closely related than do subjects with a lower MI score. In two experiments, we investigated whether judgments of the semantic distance between stimulus words varied as a function of MI. In the first experiment, random word pairs of two word classes, animals and fruits, were presented. Subjects had to judge the semantic distance between word pairs. In the second experiment, sets of three words were presented, consisting of a pair of indirectly related, or unrelated nouns plus a third noun. Subjects had to judge the semantic distance of the third noun to the word pair The results of both experiments showed that higher MI subjects considered unrelated words as more closely associated than did lower MI subjects. We conjecture that for normal subjects high on MI "loose associations" may not be loose after all. We also note that the tendency to link uncommon, nonobvious, percepts may not only be the basis of paranormal and paranoid ideas of reference, but also a prerequisite of creative thinking.

Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language

This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages.

Kognitiva system i namngivningen av finländska handelsfartyg 1838-1938

This dissertation is an onomastic study of Finland s stock of ship names (nautonomasticon) recorded over the period 1838 1938. The primary material investigated consists of 2 066 examples of ship names from the fleets of coastal towns, distributed over five sample years. The material is supplemented with two bodies of comparative data; one that consists of 2 535 examples of boat names from the archipelago area at the corresponding time, and another that comprises 482 examples of eighteenth century Finnish ship names. This study clarifies the categories of names that appear the frequency of the names, formation, morphology, linguistic origin, functions, and semantic qualities. By comparing the material with boat names from previous centuries, and from other countries, the characteristics of Finnish vessel names are further highlighted. Additional clarification is brought to the chronological, regional, and social variations, and to the emergence of various forms of systematic naming. This dissertation builds on older research from other countries, and uses traditional onomastic methods alongside a more modern methodology. The approach is interdisciplinary, meaning that the names are explored using facts not only from nautical history, but also from a range of other historical disciplines such as economics, culture, art, and literature. In addition, the approach is socio-onomastic, i.e. that the variations in names are studied in a societal context. Using a synchronised perspective, cognitive linguistic theories have provided the tools for this exploration into the metaphorical and the prototypical meaning of the names, and the semantic domains that the names create. The quantitative analysis has revealed the overall picture of Finnish boat names. Personal names, names from mythology, and place names, emerge as significant categories, alongside nonproprial names in Swedish and Finnish. The interdisciplinary perspective has made it possible to explain certain trends in the stock of boat names, for example, the predisposition towards names from classical mythology, the breakthrough of names taken from the national epos Kalevala, names in the Finnish language from around the middle of the nineteenth century, and the continuing rise of place names during the latter part of the period 1838 1938. The socio-onomastic perspective has also identified clear differences between those ship names used in towns, and those ship names used in the archipelago, and it has clarified how naming conventions tend to spread from town centres to peripheral areas. The cognitive linguistic methods have revealed that the greater part of the vessel names can be interpreted as metaphors, in particular personifications, and that many names are related in their content and also form semantic networks and cognitive systems. The results indicate that there is a mental nautonomasticon that consists of a standard set of traditional ship names, but they also reveal the existence of conscious or unconscious cognitive systems (rules and conventions) that guide the naming of boats.

Incidencia de la matriz de creencias compartidas y la centralidad sociocognitiva en la construcción de significados y formación de consensos

Resumen: El modelo de la Muestra de la Información sostiene que la probabilidad de que cierta información sea mencionada en un grupo es mayor si se encuentra disponible en muchos miembros que en uno solo. La información compartida en la matriz de creencias preexistente a la interacción social, tiene mayor probabilidad de ser expresada, repetida y validada por consentimiento e influye en el producto grupal. Objetivos: cuantificar el impacto de la matriz de creencias compartidas en los procesos de negociación de significados y comprender cualitativamente este proceso. Sujetos: Participaron 225 estudiantes de Psicología de la Universidad Nacional de Mar del Plata consensuando sobre la relación significativa entre 9 conceptos académicos.El conocimiento previo compartido fue operativizado usando la Centralidad Sociocognitiva. El mapeo de las redes semánticas de los participantes, su inter influencia y evolución en las diferentes instancias de la negociación, el tratamiento analítico de comparación cuali y cuantitativa y su resolución gráfica, se realiza por medio de métodos especiales desarrollados sobre Análisis de Redes Sociales. Resultados: Las predicciones de influencia social entre pares y la visualización de la evolución de las redes semánticas de los participantes y los grupos, arrojan resultados robustos y sugerentes para su aplicación a diversos ámbitos de interacción social y comunicacional.

Talking about colds and flu: The lay diagnosis of two common illnesses among older British people

This paper reports on a study of the ways in which 54 older people in South Wales (UK) talk about the symptoms and causes of cold and influenza (flu). The study was designed to understand why older people might reject or accept the offer of seasonal flu vaccine, and in the course of the interviews respondents were also asked to express their views about the nature and causes of the two key illnesses. The latter are among the most common infections in human beings. In terms of the biomedical paradigm the common cold is caused by numerous respiratory viruses, whilst flu is caused by the influenza virus. Medical diagnosis is usually made on clinical grounds without laboratory confirmation. Symptoms of flu include sudden onset of fever and cough, and colds are characterized by sneezing, sore throat, and runny nose, but in practice the symptoms often overlap. In this study we examine the degree by which the views of lay people with respect to both diagnosis and epidemiology diverge with that which is evident in biomedical discourse. Our results indicate that whilst most of the identified symptoms are common to lay and professional people, the former integrate symptoms into a markedly different observational frame from the latter. And as far as causation is concerned it is clear that lay people emphasize the role of 'resistance' and 'immunity' at least as much as 'infection' in accounting for the onset of colds and flu. The data are analyzed using novel methods that focus on the co-occurrence of concepts and are displayed as semantic networks. As well as reporting on its findings the authors draw out some implications of the study for social scientific and policy discussions concerning lay diagnosis, lay expertise and the concept of an expert patient.