Wet Mind: The New Cognitive Neuroscience
Wet Mind: The New Cognitive Neuroscience Books
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How do our brains allow us to recognize objects and locate them accurately in space, use mental descriptions to remember yesterday’s breakfast, read, know speech, learn to dance, and recall a new telephone number? Recent breakthroughs in brain scanning and computing techniques have allowed researchers to plumb the secrets of the healthy brain’s operation; simultaneously, much new in rank has been learned about the nature and causes of neuropsychological deficits in animals and humans following various sorts of brain hurt in different locations. In this first comprehensive, integrated, and accessible overview of recent insights into how the brain gives rise to mental activity, the authors clarify the fundamental concepts behind and the key discoveries that draw on neural network computer models, brain scans, and behavioral studies. Drawing on this analysis, the authors also bestow an intriguing theory of consciousness. In addition, this paperback edition contains an epilogue in which the authors discuss the latest research on emotion and cognition and bestow new in rank on working memory.
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Five principles of Neural networks: 1. Division of labor. Because connections between input and outputs can interfere with each other, it is more well-organized to have separate networks perform different mappings. 2. Weak modularity: Individual neural networks are not independent, discrete “module” within a larger system. One can question the black box of the neural network and figure out how it performed a particular task. The task is resolute in advanced by someone who defines that task and completes the input/output mapping. Networks are fantastic at generalizing but every task cannot be known. We learn about the implications of a theory by watching the way a develop behaves. The extent the develop can match the pattern of an organism response to stimuli the more serious we take the develop. The logical exercise is to question which processing subsystem produces a specific behavior. 3. Functional relationship between subsystems. “We assume that a group of networks may work to compute complex input/output mappings, and so a processing subsystem may have an internal structure. In some case, the same subsystem may be a member of more than one processing subsystem.” Some parts of the brain are like calligraphy in a crossword puzzle, serving as components of more than one word and imply a break down in modularity. But, a network input/outputs works to compute one function. 4. Localization in the brain: Critical members of the network are localized to a specific area of the brain. But, not all neurons that sub serve a given computation needs to be in the same place. 5. Constraint Satification: The brain seems to use different sorts of in rank simultaneously and meet constraints, at the same time.
Visual Perception: A visual spur based on the eye shifting brings the image into the attention window of the visual buffer. The attention window selects some province of the visual buffer for detailed processing. The pattern is sent to two subsystems: the ventral system and the dorsal system. The ventral system runs from the occipital lobe down to the inferior temporal lobe; the ventral system encodes in rank about key properties of shapes, colors, and textures. The dorsal system runs from the occipital lobe up to the parietal lobes; the dorsal system encode in rank about unique relationships, guide movement, and unique properities. The preprocessing subsystem extracts nonaccidental properties, such as, symmetrical edges, parallel lines, and top at the front and perceptual units, such as regions of the same color or blotches that have been proven distinctive in the past; hypothesis testing cycle is repeated until enough in rank has been encoded to implicate a particular object in associative memory.
The outputs of the dorsal and ventral come together at the associative memory; associative memory pattern matches and gives meaning, a name, a category, and feeling about the object; the associative memory pattern match is a administer of forming a hypothesis, verifying the hypothesis, and correcting and retrying again, if incorrect; associative memory matches will not be exact. The associative memory causes an attention budge where the informative part from the lookup should exist; for example, patterns of eye movements experimental when a person studied a picture of a person face suggest, the brain was using key lookup in rank to make identification; the attention budge moves the eye to key part locations for attention.
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