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Aiming to bridge the gap between analytical and continental philosophy, this peer-reviewed series presents innovative, cutting-edge contributions in contemporary philosophical inquiry, written in English or German. The series is a useful introduction to a variety of topics, aimed at readers interested in the concepts, methods, and historical developments of philosophy.
Problem Behavior. --- Hermeneutics. --- Understanding. --- language. --- method.
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This work is a contribution to understanding multi-object traffic scenes from video sequences. All data is provided by a camera system which is mounted on top of the autonomous driving platform AnnieWAY. The proposed probabilistic generative model reasons jointly about the 3D scene layout as well as the 3D location and orientation of objects in the scene. In particular, the scene topology, geometry as well as traffic activities are inferred from short video sequences.
computer vision --- machine learning --- scene understanding
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Comprehension. --- Understanding --- Apperception --- Learning, Psychology of --- Memory
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Ein beträchtlicher Teil der literaturwissenschaftlichen Theoriebildung geht von der Voraussetzung aus, dass sprachliche Gebilde mehr sind als graphische Muster auf dem Papier. Man nimmt an, dass es zusätzlich zur wahrnehmbaren Gestalt des Zeichens auch noch eine Bedeutung geben müsse, die man zwar nicht mit den äußeren Sinnen, aber doch mit dem Geist erfassen kann. Dass dieses Modell des Verstehens als Bedeutungszuweisung problematisch ist, wurde häufig bemerkt, doch es ist bislang nicht ausreichend geklärt, wie die Literaturwissenschaft den Mythos der Bedeutung und den damit verbundenen Mythos der Innenwelt überwinden und zugleich den Anspruch aufrechterhalten kann, eine empirische Wissenschaft zu sein, die erkennen will, was Zeichen bedeuten, und analysiert, wie Texte beschaffen sind. Die Studie schlägt als Antwort auf diese Herausforderung eine Neufassung von Grundbegriffen wie ,Text', ,Bedeutung', ,Absicht', ,Interpretation', ,Verstehen', ,Würdigung' und ,Einfühlung' vor. Sie skizziert eine verhaltensorientierte Philosophie der Literaturwissenschaft, derzufolge die Forschung den historisch situierten Gebrauch der Wörter untersucht und ihn unter Aufbietung des eigenen Verhaltensrepertoires verlebendigt. Most theories of philological research rely on a seemingly obvious premise: one imagines written signs as graphical patterns associated with meanings; one sees the human mind as the abode of thoughts and intentions. The study shows how this concept systematically misdirects academic thinking and proposes a fundamental conceptual redefinition.
Philology --- Research. --- Hermeneutics. --- literary theory. --- understanding.
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Reading comprehension. --- Comprehension. --- Understanding --- Apperception --- Learning, Psychology of --- Memory --- Comprehension
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Understanding, as Descartes, Locke and Kant all insisted, is the primary 'faculty' of the mind; yet our modern sciences have been slow to advance a clear and testable account of what it means to understand, of children's acquisition of this concept and, in particular, how children come to ascribe understanding to themselves and others. By drawing together developmental and philosophical theories, this book provides a systematic account of children's concept of understanding and places understanding at the heart of children's 'theory of mind'. Children's subjective awareness of their own minds, of what they think, depends on learning a language for ascribing mental states to themselves and others. This book will appeal to researchers in developmental psychology, cognitive science, education and philosophy who are interested in the cognitive and emotional development of children and in the more basic question of what it means to have a mind.
Comprehension. --- Understanding --- Apperception --- Learning, Psychology of --- Memory --- Cognitive learning.
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This special issue reviews state-of-the-art approaches to the biophysical roots of cognition. These approaches appeal to the notion that cognitive capacities serve to optimize responses to changing external conditions. Crucially, this optimisation rests on the ability to predict changes in the environment, thus allowing organisms to respond pre-emptively to changes before their onset. The biophysical mechanisms that underwrite these cognitive capacities remain largely unknown; although a number of hypotheses has been advanced in systems neuroscience, biophysics and other disciplines. These hypotheses converge on the intersection of thermodynamic and information-theoretic formulations of self-organization in the brain. The latter perspective emerged when Shannon’s theory of message transmission in communication systems was used to characterise message passing between neurons. In its subsequent incarnations, the information theory approach has been integrated into computational neuroscience and the Bayesian brain framework. The thermodynamic formulation rests on a view of the brain as an aggregation of stochastic microprocessors (neurons), with subsequent appeal to the constructs of statistical mechanics and thermodynamics. In particular, the use of ensemble dynamics to elucidate the relationship between micro-scale parameters and those of the macro-scale aggregation (the brain). In general, the thermodynamic approach treats the brain as a dissipative system and seeks to represent the development and functioning of cognitive mechanisms as collective capacities that emerge in the course of self-organization. Its explicanda include energy efficiency; enabling progressively more complex cognitive operations such as long-term prediction and anticipatory planning. A cardinal example of the Bayesian brain approach is the free energy principle that explains self-organizing dynamics in the brain in terms of its predictive capabilities – and selective sampling of sensory inputs that optimise variational free energy as a proxy for Bayesian model evidence. An example of thermodynamically grounded proposals, in this issue, associates self-organization with phase transitions in neuronal state-spaces; resulting in the formation of bounded neuronal assemblies (neuronal packets). This special issue seeks a discourse between thermodynamic and informational formulations of the self-organising and self-evidencing brain. For example, could minimization of thermodynamic free energy during the formation of neuronal packets underlie minimization of variational free energy?
consciousness --- understanding --- Markov blanket --- Hebbian assembly --- neuronal packet --- Bayesian brain
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This special issue reviews state-of-the-art approaches to the biophysical roots of cognition. These approaches appeal to the notion that cognitive capacities serve to optimize responses to changing external conditions. Crucially, this optimisation rests on the ability to predict changes in the environment, thus allowing organisms to respond pre-emptively to changes before their onset. The biophysical mechanisms that underwrite these cognitive capacities remain largely unknown; although a number of hypotheses has been advanced in systems neuroscience, biophysics and other disciplines. These hypotheses converge on the intersection of thermodynamic and information-theoretic formulations of self-organization in the brain. The latter perspective emerged when Shannon’s theory of message transmission in communication systems was used to characterise message passing between neurons. In its subsequent incarnations, the information theory approach has been integrated into computational neuroscience and the Bayesian brain framework. The thermodynamic formulation rests on a view of the brain as an aggregation of stochastic microprocessors (neurons), with subsequent appeal to the constructs of statistical mechanics and thermodynamics. In particular, the use of ensemble dynamics to elucidate the relationship between micro-scale parameters and those of the macro-scale aggregation (the brain). In general, the thermodynamic approach treats the brain as a dissipative system and seeks to represent the development and functioning of cognitive mechanisms as collective capacities that emerge in the course of self-organization. Its explicanda include energy efficiency; enabling progressively more complex cognitive operations such as long-term prediction and anticipatory planning. A cardinal example of the Bayesian brain approach is the free energy principle that explains self-organizing dynamics in the brain in terms of its predictive capabilities – and selective sampling of sensory inputs that optimise variational free energy as a proxy for Bayesian model evidence. An example of thermodynamically grounded proposals, in this issue, associates self-organization with phase transitions in neuronal state-spaces; resulting in the formation of bounded neuronal assemblies (neuronal packets). This special issue seeks a discourse between thermodynamic and informational formulations of the self-organising and self-evidencing brain. For example, could minimization of thermodynamic free energy during the formation of neuronal packets underlie minimization of variational free energy?
consciousness --- understanding --- Markov blanket --- Hebbian assembly --- neuronal packet --- Bayesian brain
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