64 research outputs found

    Concepts, Frames and Cascades in Semantics, Cognition and Ontology

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    This open access book presents novel theoretical, empirical and experimental work exploring the nature of mental representations that support natural language production and understanding, and other manifestations of cognition. One fundamental question raised in the text is whether requisite knowledge structures can be adequately modeled by means of a uniform representational format, and if so, what exactly is its nature. Frames are a key topic covered which have had a strong impact on the exploration of knowledge representations in artificial intelligence, psychology and linguistics; cascades are a novel development in frame theory. Other key subject areas explored are: concepts and categorization, the experimental investigation of mental representation, as well as cognitive analysis in semantics. This book is of interest to students, researchers, and professionals working on cognition in the fields of linguistics, philosophy, and psychology

    Language evolution as a constraint on conceptions of a minimalist language faculty

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    PhD ThesisLanguage appears to be special. Well-rehearsed arguments that appeal to aspects of language acquisition, psycholinguistic processing and linguistic universals all suggest that language has certain properties that distinguish it from other domain general capacities. The most widely discussed theory of an innate, modular, domain specific language faculty is Chomskyan generative grammar (CGG) in its various guises. However, an examination of the history and development of CGG reveals a constant tension in the relationship of syntax, phonology and semantics that has endured up to, and fatally undermines, the latest manifestation of the theory: the Minimalist Program. Evidence from language evolution can be deployed to arrive at a more coherent understanding of the nature of the human faculty for language. I suggest that all current theories can be classed on the basis of two binary distinctions: firstly, that between nativist and non-nativist accounts, and secondly between hypotheses that rely on a sudden explanation for the origins of language and those that rely on a gradual, incremental picture. All four consequent possibilities have serious flaws. By scrutinising the extant cross-disciplinary data on the evolution of hominins it becomes clear that there were two significant periods of rapid evolutionary change, corresponding to stages of punctuated equilibrium. The first of these occurred approximately two million years ago with the speciation event of Homo, saw a doubling in the size, alongside some reorganisation, of hominin brains, and resulted in the first irrefutable evidence of cognitive behaviour that distinguishes the species from that of our last common ancestor with chimpanzees. The second period began seven to eight hundred thousand years ago, again involving reorganisation and growth of the brain with associated behavioural innovations, and gave rise to modern humans by at least two hundred thousand years ago. ii I suggest that as a consequence of the first of these evolutionary breakthroughs, the species Homo erectus was endowed with a proto-‘language of thought’ (LoT), a development of the cognitive capacity evident in modern chimpanzees, accompanied by a gestural, and then vocal, symbolic protolanguage. The second breakthrough constituted a great leap involving the emergence of advanced theory of mind and a fully recursive, creative LoT. I propose that the theory outlined in the Representational Hypothesis (RH) clarifies an understanding of the nature of language as having evolved to represent externally this wholly internal, universal LoT, and it is the latter which is the sole locus of syntax and semantics. By clearly distinguishing between a phonological system for semiotic representation, and that which it represents, a syntactico-semantic LoT, the RH offers a fully logical and consistent understanding of the human faculty for language. Language may have the appearance of domain specific properties, but this is entirely derived from both the nature of that which it represents, and the natural constraints of symbolic representation

    Analysis of FMRI Exams Through Unsupervised Learning and Evaluation Index

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    In the last few years, the clustering of time series has seen significant growth and has proven effective in providing useful information in various domains of use. This growing interest in time series clustering is the result of the effort made by the scientific community in the context of time data mining. For these reasons, the first phase of the thesis focused on the study of the data obtained from fMRI exams carried out in task-based and resting state mode, using and comparing different clustering algorithms: SelfOrganizing map (SOM), the Growing Neural Gas (GNG) and Neural Gas (NG) which are crisp-type algorithms, a fuzzy algorithm, the Fuzzy C algorithm, was also used (FCM). The evaluation of the results obtained by using clustering algorithms was carried out using the Davies Bouldin evaluation index (DBI or DB index). Clustering evaluation is the second topic of this thesis. To evaluate the validity of the clustering, there are specific techniques, but none of these is already consolidated for the study of fMRI exams. Furthermore, the evaluation of evaluation techniques is still an open research field. Eight clustering validation indexes (CVIs) applied to fMRI data clustering will be analysed. The validation indices that have been used are Pakhira Bandyopadhyay Maulik Index (crisp and fuzzy), Fukuyama Sugeno Index, Rezaee Lelieveldt Reider Index, Wang Sun Jiang Index, Xie Beni Index, Davies Bouldin Index, Soft Davies Bouldin Index. Furthermore, an evaluation of the evaluation indices will be carried out, which will take into account the sub-optimal performance obtained by the indices, through the introduction of new metrics. Finally, a new methodology for the evaluation of CVIs will be introduced, which will use an ANFIS model

    Recent Applications in Graph Theory

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    Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks
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