9,588 research outputs found
Language and Cognition Interaction Neural Mechanisms
How language and cognition interact in thinking? Is language just used for communication of completed thoughts, or is it fundamental for thinking? Existing approaches have not led to a computational theory. We develop a hypothesis that language and cognition are two separate but closely interacting mechanisms. Language accumulates cultural wisdom; cognition develops mental representations modeling surrounding world and adapts cultural knowledge to concrete circumstances of life. Language is acquired from surrounding language “ready-made” and therefore can be acquired early in life. This early acquisition of language in childhood encompasses the entire hierarchy from sounds to words, to phrases, and to highest concepts existing in culture. Cognition is developed from experience. Yet cognition cannot be acquired from experience alone; language is a necessary intermediary, a “teacher.” A mathematical model is developed; it overcomes previous difficulties and leads to a computational theory. This model is consistent with Arbib's “language prewired brain” built on top of mirror neuron system. It models recent neuroimaging data about cognition, remaining unnoticed by other theories. A number of properties of language and cognition are explained, which previously seemed mysterious, including influence of language grammar on cultural evolution, which may explain specifics of English and Arabic cultures
An Explanation to Individual Knowledge and Behavior Based on Empirical Substrates
Using recent findings from modern empirical disciplines and mainly building on F.A.Hayek’s thoughts, the paper gives a definition of knowledge in accord with the Austrian School’s tradition, and basing on the definition, it sums up three behavior assumptions and a framework on explaining individual behavior and expounds ideas on hierarchical knowledge and its change in real situations. By this way, the paper believes that the Austrian School can be greatly advanced with the help of modern empirical findings.knowledge, shared knowledge, hierarchy, behavioral assumption, reduced framework, empirical foundation
Brain Dynamics across levels of Organization
After presenting evidence that the electrical activity recorded from the brain surface can reflect metastable state transitions of neuronal configurations at the mesoscopic level, I will suggest that their patterns may correspond to the distinctive spatio-temporal activity in the Dynamic Core (DC) and the Global Neuronal Workspace (GNW), respectively, in the models of the Edelman group on the one hand, and of Dehaene-Changeux, on the other. In both cases, the recursively reentrant activity flow in intra-cortical and cortical-subcortical neuron loops plays an essential and distinct role. Reasons will be given for viewing the temporal characteristics of this activity flow as signature of Self-Organized Criticality (SOC), notably in reference to the dynamics of neuronal avalanches. This point of view enables the use of statistical Physics approaches for exploring phase transitions, scaling and universality properties of DC and GNW, with relevance to the macroscopic electrical activity in EEG and EMG
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