Computational Cognitive Neuroscience
نویسنده
چکیده
Cognitive neuroscience was born in the 1990’s amid a technological explosion that produced powerful new methods for noninvasively studying the human brain, including functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS). These exciting new technologies revolutionized the scientific study of the mind, giving unprecedented observability into the neural processes that mediate human thought and action. With the new data came a growing need for new kinds of theories that could simultaneously account for the behavioral data that are the bread and butter of traditional mathematical psychology as well as the brain-related measures coming from the new technologies. Computational Cognitive Neuroscience (CCN) was created to fill this void. CCN evolved from computational neuroscience on one side and connectionism, neural network theory, and machine learning on the other. Like computational neuroscience, CCN strives for neurobiological accuracy and like connectionism, a major goal is to account for behavior. But CCN is unique because most computational neuroscience models make no attempt to account for behavior and most connectionist models make no attempt to be biologically detailed. The biological detail included in CCN models adds many more constraints on the modeling process than more traditional approaches. As a result, two researchers independently modeling the same behavior are more likely to converge on highly similar models with this new approach, and for this reason the resulting models should have a permanence that is unusual with older approaches. Not surprisingly, a growing of number of researchers are now pursuing the endeavor of CCN modeling (e.g., Anderson, Fincham, Qin, & Stocco, 2008; Ashby & Helie, 2011; Frank, 2005; Hartley, Burgess, Lever, Cacucci, & O’Keefe, 2000; O’Reilly, Munakata, Frank, Hazy, et al., 2012).
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تاریخ انتشار 2017