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Visualization : Theory and Practice in Science Education
External representations (pictures, diagrams, graphs, concrete models) have always been valuable tools for the science teacher. The formation of personal, internal, representations – visualizations – from them plays a key role in all learning, especially in that of science. The use of personal computers and sophisticated software has expanded into the areas of simulation, virtual reality, and animation, and students now engage in the creation of models, a key aspect of scientific methodology. Several academic disciplines underlie these developments, yet act independently of each other, to the detriment of an attainment of what is possible. This book brings together the insights of practicing scientists, science education researchers, computer specialists, and cognitive scientists, to produce a coherent overview. It links presentations about the cognitive theory of representation and visualization, its implications for science curriculum design, and for learning and teaching in classrooms and laboratories.
Vector Semantics
Links the formal theory of word vectors to the cognitive theory of linguistics. The computational linguists and deep learning researchers who developed word vectors have relied primarily on the ever-increasing availability of large corpora and of computers with highly parallel GPU and TPU compute engines, and their focus is with endowing computers with natural language capabilities for practical applications such as machine translation or question answering. Cognitive linguists investigate natural language from the perspective of human cognition, the relation between language and thought, and questions about conceptual universals, relying primarily on in-depth investigation of language in use.
Self-Organizing Natural Intelligence : Issues of Knowing, Meaning, and Complexity
This book proposes, utilizes, and demonstrates the research superiority of a highly developed multidisciplinary theory models approach to intelligence. With conceptual tools, concepts and mathematical methods more suited to continuous, dynamic phenomena of living things, the entire scope of natural intelligence based upon empirical studies of actual human and animal experience is addressed. Results show that human and animal intelligence is largely self-organizing and emergent across a spectrum of major categories of kinds of natural intelligence, not limited to a single "top down" capacity as current proponents of the single-capacity g-theory and IQ approach support.
