Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. The present results support these claims and the neural efficiency hypothesis.
I’ve suggested that traditional ethical convictions in our culture have been grounded in a belief (often tacit) in an immaterial soul that somehow uses the brain, but reserves for itself the powers of moral reasoning, decision making, and an appreciation of meaning and purpose. The cognitive neuroscience revolution challenges that belief, and increasingly forces us to recognize that all mental life is a product of the evolved, genetically influenced structure of the brain. This challenge has also been seen to threaten sacred moral values, but I would argue (and like to think that Gazzaniga agrees) that in fact that is not a logical consequence. On the contrary, I think a better understanding of what makes us tick, and of our place in nature, can clarify those values. This understanding shows that political equality does not require sameness, but rather policies that treat people as individuals with rights; that moral progress does not require that the mind is free of selfish motives, only that it has other motives to counteract them; that responsibility does not require that behavior is uncaused, only that it responds to contingencies of credit and blame; and that finding meaning in life does not require that the process that shaped the brain have a purpose, only that the brain itself have a purpose.
Adolescence is a time of transformation that is characterized by discrete changes in behavior, cognition and the brain – some of which are likely pubertal dependent, and others which are not. Although set within cultural contexts, these transformations appear to have biological roots that are deeply embedded in our evolutionary past. Starting from an evolutionary perspective, this paper provides an overview of the neurobiological and hormonal changes of adolescence and the implications of this biology for adolescent risk-taking and other behaviors. So, what impact does consideration of the biology of adolescence have for understanding adolescent risk-taking? Basic neurobehavioral characteristics of adolescents have biological roots that are deeply imbedded in our evolutionary past. Adolescents view rewarding and aversive stimuli differently than do adults. The adolescent brain does not seem to merely reflect a series of regions attaining maturity at different times, but in some sense can be characterized as a brain that reacts differently to stimuli than does the mature brain.
Thanks to Carnegie Mellon University advances in brain imaging technology, we now know how specific concrete objects are coded in the brain, to the point where we can identify which object, such as a house or a banana, someone is thinking about from its brain activation signature. Now, CMU scientists are applying this knowledge about the neural representations of familiar concepts by teaching people new concepts and watching the new neural representations develop. ‘Each time we learn something, we permanently change our brains in a systematic way,’ said Bauer, the study’s lead author. The results from this study also indicate that it may be possible to use a similar approach to understand the ‘loss’ of knowledge in various brain disorders, such as dementia or Alzheimer’s disease, or due to brain injuries. The loss of a concept in the brain may be the reverse of the process that the study observed.
To scientists, the tsunami of relativism, scepticism, and postmodernism that washed through the humanities in the twentieth century was all water off a duck’s back. Science remained committed to objectivity and continued to deliver remarkable discoveries and improvements in technology. In What Science Knows, the Australian philosopher and mathematician James Franklin explains in captivating and straightforward prose how science works its magic. He begins with an account of the nature of evidence, where science imitates but extends commonsense and legal reasoning in basing conclusions solidly on inductive reasoning from facts. After a brief survey of the furniture of the world as science sees it—including causes, laws, dispositions and force fields as well as material things—Franklin describes colorful examples of discoveries in the natural, mathematical, and social sciences and the reasons for believing them. He examines the limits of science, giving special attention both to mysteries that may be solved by science, such as the origin of life, and those that may in principle be beyond the reach of science, such as the meaning of ethics. What Science Knows will appeal to anyone who wants a sound, readable, and well-paced introduction to the intellectual edifice that is science. On the other hand it will not please the enemies of science, whose willful misunderstandings of scientific method and the relation of evidence to conclusions Franklin mercilessly exposes.
People find it easier to learn about topics that interest them, but little is known about the mechanisms by which intrinsic motivational states affect learning. We used functional magnetic resonance imaging to investigate how curiosity (intrinsic motivation to learn) influences memory. In both immediate and one-day-delayed memory tests, participants showed improved memory for information that they were curious about and for incidental material learned during states of high curiosity. Functional magnetic resonance imaging results revealed that activity in the midbrain and the nucleus accumbens was enhanced during states of high curiosity. Importantly, individual variability in curiosity-driven memory benefits for incidental material was supported by anticipatory activity in the midbrain and hippocampus and by functional connectivity between these regions. These findings suggest a link between the mechanisms supporting extrinsic reward motivation and intrinsic curiosity and highlight the importance of stimulating curiosity to create more effective learning experiences.