Time and time again, as we will see, the brilliant forebears of modern neuroscience abandoned their fierce reasoning skills and, deus ex machina, thr… - Michael S. Gazzaniga

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Time and time again, as we will see, the brilliant forebears of modern neuroscience abandoned their fierce reasoning skills and, deus ex machina, threw in a spook at the end of their analysis.

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About Michael S. Gazzaniga

Michael S. Gazzaniga (born December 12, 1939) is an American neuroscientist, author and professor of psychology at the University of California, Santa Barbara, where he heads the new SAGE Center for the Study of the Mind.

Biography information from Wikiquote

Also Known As

Native Name: Michael Steven Gazzaniga
Alternative Names: Gazzaniga, M.S. M. S. Gazzaniga Michael S Gazzaniga Gazzaniga, Michael S.

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According to Panksepp, seven primal emotional and motivational feelings that appear to be common features of animal and human consciousness at both a behavioral and a neural level are SEEKING, FEAR, RAGE, LUST, CARE, GRIEF, and PLAY.

Possessing a theory of mind means that an individual ascribes mental states, such as purpose, intention, knowledge, beliefs, doubts, pretending, liking, and so forth, to himself and to others.

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The computer scientists Jeff Clune, Jean-Baptiste Mouret, and Hod Lipson did what computer scientists do: they designed computer simulations.23 They used well-studied networks that had sensory inputs and produced outputs. What those outputs were determined how well the network performed when faced with environmental problems. They simulated twenty-five thousand generations of evolution, programming in a direct selection pressure to either maximize performance alone or maximize performance and minimize connection costs. And voilà! Once wiring-cost-minimization was added, in both changing and unchanging environments, modules immediately began to appear, whereas without the stipulation of minimizing costs, they didn’t. And when the three looked at the highest-performing networks that evolved, those networks were modular. Among that group, they found that the lower the costs were, the greater the modularity that resulted. These networks also evolved much quicker — in markedly fewer generations — whether in stable or changing environments. These simulation experiments provide strong evidence that selection pressures to maximize network performance and minimize connection costs will yield networks that are significantly more modular and more evolvable.

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