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.

Proposal for a semantic hierarchy of terminological metaphors in science and technology

Cognitive linguistics is considered as one of the most appropriate approaches to the study of scientific and technical language formation and development, where metaphor is accepted to play an essential role. This paper, based on the Cognitive Theory of Metaphor, takes as the starting point the terminological metaphors established in the research project METACITEC(Note 1), which was developed with the purpose of unfolding constitutive metaphors and their function in the language of science and technology. After the analysis of metaphorical terms and using a mixed corpus from the fields of Agriculture, Geology, Mining, Metallurgy, and other related technical fields, this study presents a proposal for a hierarchy of the selected metaphors underlying the scientific conceptual system, based on the semantic distance found in the projection from the source domain to the target domain. We argue that this semantic distance can be considered as an important parameter to take into account in order to establish the metaphoricity of science and technology metaphorical terms. The findings contribute to expand on the CTM stance that metaphor is a matter of cognition by reviewing the abstract-concrete conceptual relationship between the target and source domains, and to determine the role of human creativity and imagination in the language of science and technology configuration

Feature overlap slows lexical selection: Evidence from the picture–word interference paradigm

How does the presence of a categorically related word influence picture naming latencies? In order to test competitive and noncompetitive accounts of lexical selection in spoken word production, we employed the picture–word interference (PWI) paradigm to investigate how conceptual feature overlap influences naming latencies when distractors are category coordinates of the target picture. Mahon et al. (2007. Lexical selection is not by competition: A reinterpretation of semantic interference and facilitation effects in the picture-word interference paradigm. Journal of Experimental Psychology. Learning, Memory, and Cognition, 33(3), 503–535. doi:10.1037/0278-7393.33.3.503) reported that semantically close distractors (e.g., zebra) facilitated target picture naming latencies (e.g., HORSE) compared to far distractors (e.g., whale). We failed to replicate a facilitation effect for within-category close versus far target–distractor pairings using near-identical materials based on feature production norms, instead obtaining reliably larger interference effects (Experiments 1 and 2). The interference effect did not show a monotonic increase across multiple levels of within-category semantic distance, although there was evidence of a linear trend when unrelated distractors were included in analyses (Experiment 2). Our results show that semantic interference in PWI is greater for semantically close than for far category coordinate relations, reflecting the extent of conceptual feature overlap between target and distractor. These findings are consistent with the assumptions of prominent competitive lexical selection models of speech production.

汉语深层失读及词汇－语义系统的认知神经心理学研究

Chinese deep dyslexia is an important type of reading disorder that attracted many research interests. In the current thesis, three studies concerning Chinese deep dyslexia were performed: (1) clinical dominating and incidental symptoms were collected and their relationships were analyzed, and error scores of different character and word types compared; to verify typical reading model of alphabetic readers, and to develope a novel model for reading Chinese scripts; (2) based on these results, further neuropsychological analysis on the basic rules of lexical-semantic system and semantic distance were employed; (3) rehabilitation scheme were shaped to verify our research results. With cognitive neuropsychological methods, this study was mainly focused on deep dyslexic patients with brain impairment. The results were compared with those of normal people on rapid reading. Computer emulation was also used to describe reading process of patients. Both group analysis and case study were carried out. This study for the first time systematically investigated the clinical symptoms of Chinese deep dyslexia. A novel model was developed with a hypothesis that the sublexical pathway is composed of two parallel pathways: the phonetic sublexical pathway and the semantic sublexical pathway. Two characteristics of Chinese deep dyslexia were found compared with alphabetic deep dyslexia: (1) having no distinct word class effect and imagination effect; (2) the organization of Chinese lexical-semantic system has correlation with construct regulation, imagination and splitability of characters. Evoking of semantic correlation is stronger than phonetic correlation.

Diferenças de gênero na produção de associações livres de palavras através do ciclo sono-vigília

Although several studies, have shown differences in cognitive performance between men and women, it not yet known whether these differences occur in tasks involving free association of words (WA). Studies across the sleep-wake cycle (SWC) suggest that rapid eye movement sleep (REM) favors semantic flexibility, in comparison with pre-sleep waking (Pre-WK), slow-wave sleep (SWS) and post-sleep waking (Post-WK). The present work has two aims: (1) to evaluate the semantic distances of word pairs produced by AP, comparing men and women, (2) to evaluate semantic distance in word pairs produced by free association across the SWC in young adults of both sexes. To achieve aim (1), we applied a task of WA in 68 adult volunteers during waking (52 women and 16 men). The WA task consisted of writing the first word that came to mind after viewing another word offered as a stimulus (root Word). To achieve aim (2), we performed polysomnography to identify specific stages of the SWC. The experimental subjects were then awakened (if they were asleep) and were immediately given a WA task. The task was administered to 2 groups of 10 subjects each (G1 and G2). G1 subjects were stimulated with the same set of root words after waking from various states of SWC, while G2 subjects received sets of different root words at each state of the SWC. In the absence of a Portuguese corpus suitable for the measurement of semantic distances, the words collected in our experiments were translated to English, and semantically quantified within a systematic and representative corpus of that language (Wordnet). This procedure removed the polysemies typical of Portuguese, but preserved the semantic macrostructure common to both languages. During waking, we found that semantic distances are significantly lower in WA produced by women, in comparison with the distances observed in men. Through the SWC, there were no statistically significant differences in G1. In G2 women, we detected a significant increase of semantic distances upon being awakened from SWS. In contrast, G2 men showed a significant increase in semantic distances upon being awakened from REM. The results of the first experiment are consistent with the notion that women have a more concrete reasoning than men. The results of the second experiment indicate that men awakened from REM present more flexibility in word association than when being awakened from other states. In contrast, women showed more flexible word association after being awakened from SWS, in compared with other states. The results indicate that the cognitive flexibility attributed to different states of the SWC shows gender dependency

High level methods for scene exploration

Virtual worlds exploration techniques are used in a wide variety of domains — from graph drawing to robot motion. This paper is dedicated to virtual world exploration techniques which have to help a human being to understand a 3D scene. An improved method of viewpoint quality estimation is presented in the paper, together with a new off-line method for automatic 3D scene exploration, based on a virtual camera. The automatic exploration method is working in two steps. In the first step, a set of “good” viewpoints is computed. The second step uses this set of points of view to compute a camera path around the scene. Finally, we define a notion of semantic distance between objects of the scene to improve the approach.

Integrating Events Across Levels of Consciousness

Our knowledge grows as we integrate events experienced at different points in time. We may or may not become aware of events, their integration, and their impact on our knowledge and decisions. But can we mentally integrate two events, if they are experienced at different time points and at different levels of consciousness? In this study, an event consisted of the presentation of two unrelated words. In the stream of events, half of events shared one component ("tree desk" … "desk fish") to facilitate event integration. We manipulated the amount of time and trials that separated two corresponding events. The contents of one event were presented subliminally (invisible) and the contents of the corresponding overlapping event supraliminally (visible). Hence, event integration required the binding of contents between consciousness levels and between time points. At the final test of integration, participants judged whether two supraliminal test words ("tree fish") fit together semantically or not. Unbeknown to participants, half of test words were episodically related through an overlap ("desk"; experimental condition) and half were not (control condition). Participants judged episodically related test words to be closer semantically than unrelated test words. This subjective decrease in the semantic distance between test words was both independent of whether the invisible event was encoded first or second in order and independent of the number of trials and the time that separated two corresponding events. Hence, conscious and unconscious memories were mentally integrated into a linked mnemonic representation.

Medida de distancia semántica en grafos UNL

El trabajo que se presenta a continuación desarrolla un modelo para calcular la distancia semántica entre dos oraciones representadas por grafos UNL. Este problema se plantea en el contexto de la traducción automática donde diferentes traductores pueden generar oraciones ligeramente diferentes partiendo del mismo original. La medida de distancia que se propone tiene como objetivo proporcionar una evaluación objetiva sobre la calidad del proceso de generación del texto. El autor realiza una exploración del estado del arte sobre esta materia, reuniendo en un único trabajo los modelos propuestos de distancia semántica entre conceptos, los modelos de comparación de grafos y las pocas propuestas realizadas para calcular distancias entre grafos conceptuales. También evalúa los pocos recursos disponibles para poder experimentar el modelo y plantea una metodología para generar los conjuntos de datos que permitirían aplicar la propuesta con el rigor científico necesario y desarrollar la experimentación. Utilizando las piezas anteriores se propone un modelo novedoso de comparación entre grafos conceptuales que permite utilizar diferentes algoritmos de distancia entre conceptos y establecer umbrales de tolerancia para permitir una comparación flexible entre las oraciones. Este modelo se programa utilizando C++, se alimenta con los recursos a los que se ha hecho referencia anteriormente, y se experimenta con un conjunto de oraciones creado por el autor ante la falta de otros recursos disponibles. Los resultados del modelo muestran que la metodología y la implementación pueden conducir a la obtención de una medida de distancia entre grafos UNL con aplicación en sistemas de traducción automática, sin embargo, la carencia de recursos y de datos etiquetados con los que validar el algoritmo requieren un esfuerzo previo importante antes de poder ofrecer resultados concluyentes.---ABSTRACT---The work presented here develops a model to calculate the semantic distance between two sentences represented by their UNL graphs. This problem arises in the context of machine translation where different translators can generate slightly different sentences from the same original. The distance measure that is proposed aims to provide an objective evaluation on the quality of the process involved in the generation of text. The author carries out an exploration of the state of the art on this subject, bringing together in a single work the proposed models of semantic distance between concepts, models for comparison of graphs and the few proposals made to calculate distances between conceptual graphs. It also assesses the few resources available to experience the model and presents a methodology to generate the datasets that would be needed to develop the proposal with the scientific rigor required and to carry out the experimentation. Using the previous parts a new model is proposed to compute differences between conceptual graphs; this model allows the use of different algorithms of distance between concepts and is parametrized in order to be able to perform a flexible comparison between the resulting sentences. This model is implemented in C++ programming language, it is powered with the resources referenced above and is experienced with a set of sentences created by the author due to the lack of other available resources. The results of the model show that the methodology and the implementation can lead to the achievement of a measure of distance between UNL graphs with application in machine translation systems, however, lack of resources and of labeled data to validate the algorithm requires an important effort to be done first in order to be able to provide conclusive results.

Semantic spaces : measuring the distance between different subspaces

Semantic Space models, which provide a numerical representation of words’ meaning extracted from corpus of documents, have been formalized in terms of Hermitian operators over real valued Hilbert spaces by Bruza et al. [1]. The collapse of a word into a particular meaning has been investigated applying the notion of quantum collapse of superpositional states [2]. While the semantic association between words in a Semantic Space can be computed by means of the Minkowski distance [3] or the cosine of the angle between the vector representation of each pair of words, a new procedure is needed in order to establish relations between two or more Semantic Spaces. We address the question: how can the distance between different Semantic Spaces be computed? By representing each Semantic Space as a subspace of a more general Hilbert space, the relationship between Semantic Spaces can be computed by means of the subspace distance. Such distance needs to take into account the difference in the dimensions between subspaces. The availability of a distance for comparing different Semantic Subspaces would enable to achieve a deeper understanding about the geometry of Semantic Spaces which would possibly translate into better effectiveness in Information Retrieval tasks.

Investigating subspace distances in semantic spaces

Semantic space models of word meaning derived from co-occurrence statistics within a corpus of documents, such as the Hyperspace Analogous to Language (HAL) model, have been proposed in the past. While word similarity can be computed using these models, it is not clear how semantic spaces derived from different sets of documents can be compared. In this paper, we focus on this problem, and we revisit the proposal of using semantic subspace distance measurements [1]. In particular, we outline the research questions that still need to be addressed to investigate and validate these distance measures. Then, we describe our plans for future research.

Multimodal semantic-associative collateral labelling and indexing of still images

A novel framework for multimodal semantic-associative collateral image labelling, aiming at associating image regions with textual keywords, is described. Both the primary image and collateral textual modalities are exploited in a cooperative and complementary fashion. The collateral content and context based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix, of the visual keywords, A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. Finally, we use Self Organising Maps to examine the classification and retrieval effectiveness of the proposed high-level image feature vector model which is constructed based on the image labelling results.

Semantic-associative visual content labelling and retrieval: A multimodal approach

A novel framework referred to as collaterally confirmed labelling (CCL) is proposed, aiming at localising the visual semantics to regions of interest in images with textual keywords. Both the primary image and collateral textual modalities are exploited in a mutually co-referencing and complementary fashion. The collateral content and context-based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix of the visual keywords. A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. We introduce a novel high-level visual content descriptor that is devised for performing semantic-based image classification and retrieval. The proposed image feature vector model is fundamentally underpinned by the CCL framework. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval, respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicate that the proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models. (C) 2007 Elsevier B.V. All rights reserved.