اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدFree Will and Advances in Cognitive Science
267
0
0.0
(
0
)
تأليف
Leonid Perlovsky
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Free will is fundamental to morality, intuition of self, and normal functioning of the society. However, science does not provide a clear logical foundation for this idea. This paper considers the fundamental scientific argument against free will, called reductionism, and explains the reasons for choosing dualism against monism. Then, the paper summarizes unexpected conclusions from recent discoveries in cognitive science. Classical logic turns out not to be the fundamental mechanism of mind. It is replaced by dynamic logic. Mathematical and experimental evidence are considered conceptually. Dynamic logic counters logical arguments for reductionism. Contemporary science of mind is not reducible; free will can be scientifically accepted along with scientific monism.
قيم البحث
اقرأ أيضاً
Rhythmic electrical activity in the brain emerges from regular non-trivial interactions between millions of neurons. Neurons are intricate cellular structures that transmit excitatory (or inhibitory) signals to other neurons, often non-locally, depen
ding on the graded input from other neurons. Often this requires extensive detail to model mathematically, which poses several issues in modelling large systems beyond clusters of neurons, such as the whole brain. Approaching large populations of neurons with interconnected constituent single-neuron models results in an accumulation of exponentially many complexities, rendering a realistic simulation that does not permit mathematical tractability and obfuscates the primary interactions required for emergent electrodynamical patterns in brain rhythms. A statistical mechanics approach with non-local interactions may circumvent these issues while maintaining mathematically tractability. Neural field theory is a population-level approach to modelling large sections of neural tissue based on these principles. Herein we provide a review of key stages of the history and development of neural field theory and contemporary uses of this branch of mathematical neuroscience. We elucidate a mathematical framework in which neural field models can be derived, highlighting the many significant inherited assumptions that exist in the current literature, so that their validity may be considered in light of further developments in both mathematical and experimental neuroscience.
The human brain is a complex dynamical system that gives rise to cognition through spatiotemporal patterns of coherent and incoherent activity between brain regions. As different regions dynamically interact to perform cognitive tasks, variable patte
rns of partial synchrony can be observed, forming chimera states. We propose that the emergence of such states plays a fundamental role in the cognitive organization of the brain, and present a novel cognitively-informed, chimera-based framework to explore how large-scale brain architecture affects brain dynamics and function. Using personalized brain network models, we systematically study how regional brain stimulation produces different patterns of synchronization across predefined cognitive systems. We then analyze these emergent patterns within our novel framework to understand the impact of subject-specific and region-specific structural variability on brain dynamics. Our results suggest a classification of cognitive systems into four groups with differing levels of subject and regional variability that reflect their different functional roles.
In this paper we present a brain-inspired cognitive architecture that incorporates sensory processing, classification, contextual prediction, and emotional tagging. The cognitive architecture is implemented as three modular web-servers, meaning that
it can be deployed centrally or across a network for servers. The experiments reveal two distinct operations of behaviour, namely high- and low-salience modes of operations, which closely model attention in the brain. In addition to modelling the cortex, we have demonstrated that a bio-inspired architecture introduced processing efficiencies. The software has been published as an open source platform, and can be easily extended by future research teams. This research lays the foundations for bio-realistic attention direction and sensory selection, and we believe that it is a key step towards achieving a bio-realistic artificial intelligent system.
The Mozart effect refers to scientific data on short-term improvement on certain mental tasks after listening to Mozart, and also to its popularized version that listening to Mozart makes you smarter (Tomatis, 1991; Wikipedia, 2012). Does Mozart effe
ct point to a fundamental cognitive function of music? Would such an effect of music be due to the hedonicity, a fundamental dimension of mental experience? The present paper explores a recent hypothesis that music helps to tolerate cognitive dissonances and thus enabled accumulation of knowledge and human cultural evolution (Perlovsky, 2010, 2012). We studied whether the influence of music is related to its hedonicity and whether pleasant or unpleasant music would influence scholarly test performance and cognitive dissonance. Specific hypotheses evaluated here are that during a test students experience contradictory cognitions that cause cognitive dissonances. If some music helps to tolerate cognitive dissonances, then first, this music should increase the duration during which participants can tolerate stressful conditions while evaluating test choices. Second, this should result in improved performance. These hypotheses are tentatively confirmed in the reported experiments as the agreeable music was correlated with better performance above that under indifferent or unpleasant music. It follows that music likely performs a fundamental cognitive function explaining the origin and evolution of musical ability considered previously a mystery.
The paper discusses relationships between aesthetics theory and mathematical models of mind. Mathematical theory describes abilities for concepts, emotions, instincts, imagination, adaptation, learning, cognition, language, approximate hierarchy of t
he mind and evolution of these abilities. The knowledge instinct is the foundation of higher mental abilities and aesthetic emotions. Aesthetic emotions are present in every act of perception and cognition, and at the top of the mind hierarchy they become emotions of the beautiful. The learning ability is essential to everyday perception and cognition as well as to the historical development of understanding of the meaning of life. I discuss a controversy surrounding this issue. Conclusions based on cognitive and mathematical models confirm that judgments of taste are at once subjective and objective, and I discuss what it means. The paper relates cognitive and mathematical concepts to those of philosophy and aesthetics, from Plato to our days, clarifies cognitive mechanisms and functions of the beautiful, and resolves many difficulties of contemporary aesthetics.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
