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In the Light of Evolution Volume VII: The Human Mental Machinery (2014) / Chapter Skim
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Part III: THE HUMAN DIFFERENCE: FROM ETHICS TO AESTHETICS
Pages 153-156

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 153...
... She defends the need for quantitative models of altruistic behavior along a spectrum ranging from strong benefit to extreme harm. These models might help to scientifically distinguish between beneficial and harmful egoistic behavior, as well as clarify the relationships between egoism, altruism, and pathological altruism.
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From page 154...
... Participants rapidly learn where to search, looking near previously rewarded locations and avoiding previously unrewarded sites. A reinforcementlearning model, similar to that used previously to examine both foraging animal behavior and neuronal firing of dopaminergic cells, can describe the resulting search performance.
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From page 155...
... Finally, by analyzing the dynamics of brain functional connectivity, Camilo Cela-Conde and coworkers in Chapter 16 offer the first identification of brain networks engaged within distinct time frames during the appreciation of beauty. A fast aesthetic perception of the beautiful/ not-beautiful condition of each visual stimulus appears within 250–750 ms, whereas further aesthetic appreciation processes are subsequently performed in the 1,000- to 1,500-ms range.
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Key Terms

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  • leanne chukoskie
  • events based

  • rapidly learn
  • brain network
  • mechanistic bases
  • results provide
  • highly superior

  • novel scene
  • superior autobiographical
  • brain functional
  • brain regions
  • brain particularities

  • authors identify
  • extreme harm
  • near previously
  • search performance
  • learning model

  • rhythmic relationships
  • appreciation processes
  • particularly activated
  • oakley examines
  • movement planning


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