Download e-book for kindle: Buddha's Brain: The Practical Neuroscience of Happiness, by Rick Hanson

By Rick Hanson

The mind body structure linked to religious states has been fertile flooring for researchers and writers alike. Neuropsychologist andmeditation instructor Hanson means that an figuring out of the mind along with 2,500-year-old Buddhist teachings might help readers in attaining extra happiness. He explains how the mind advanced to maintain people secure from exterior threats; the ensuing integrated negativity bias creates pain in smooth members. bringing up psychologist Donald Hebb's end that after neurons fireplace jointly, they cord jointly, Hanson argues that the brain's functioning could be tormented by easy practices and meditation to foster overall healthiness. vintage Buddhist suggestions resembling the 3 trainings—mindfulness, virtuous motion and wisdom—frame Hanson's process. Written with neurologist Mendius, the booklet contains descriptions and diagrams of mind functioning. transparent directions advisor the reader towards extra optimistic concepts and emotions. whereas the writer doesn't regularly prevail at clarifying complicated body structure, this lightly encouraging sensible advisor in your mind bargains worthy details supported by way of examine in addition to steps to alter instinctive styles in the course of the Buddhist course. (Nov.)

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Extra resources for Buddha's Brain: The Practical Neuroscience of Happiness, Love, and Wisdom

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Indeed, the key idea in this mode of theory building is that description is intrinsically nonreductive and its terms are defined in the same level of analysis in which the problem is stated. The descriptive approach has been the grand strategy of mathematical psychology—to represent the measurements obtained in behavioral experiments in a systematic and orderly form. That is, it is to match the course of an experimentally observed variable by means of one of the languages that can simulate or analogize the trajectories of empirical observations.

Subsequent evidence led to the discovery of basic particles such as the electron in 1897 by J. J. Thomson (1856–1940), the proton in 1917 by Ernest Rutherford (1871–1937), and the neutron in 1932 by James Chadwick (1891–1974). These empirical discoveries were rounded out by Niels Bohr (1885–1962) who proposed a full-blown theory of the atom consisting of a central positively charged nucleus populated by protons and neutrons and surrounded by negatively charged orbital electrons. This ultimate theory made the different properties of the elements meaningful.

Yet the phrase “brain-like computers” is ubiquitous throughout all of these discussions regardless of whether their computer models share anything beyond the basic postulate that brains and computers are both networks of many components. The key property of a brain that makes it so adaptive and responsive probably has more to do with the heterogeneity of the interconnections, and these may not be adequately simulated for what are eminently practical reasons—there are far too many of them. ” In the original article (de Garis, Chen, Goertzel, & Lian, 2010), an artificial brain was a vast network of neuronal-like elements interconnected in a relatively regular order—the more synthetic neurons that could be manipulated, the better.

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