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In The Brain from 25,000 Feet, Mark A. Changizi defends a
non-reductionist philosophy and applies it to a variety of problems
in the brain sciences. Some of the key questions answered are as
follows. Why do we see visual illusions, and why are illusions
inevitable for any finite-speed vision machine? Why aren't brains
universal learning machines, and what does the riddle of induction
and its solution have to do with human learning and innateness? The
author tackles such questions as why the brain is folded, and why
animals have as many limbs as they do, explaining how these relate
to principles of network optimality. He describes how most natural
language words are vague and then goes on to explain the connection
to the ultimate computational limits on machines. There is also a
fascinating discussion of how animals accommodate greater
behavioral complexity. This book is a must-read for researchers
interested in taking a high-level, non-mechanistic approach to
answering age-old fundamental questions in the brain sciences.
In The Brain from 25,000 Feet, Mark A. Changizi defends a
non-reductionist philosophy and applies it to a variety of problems
in the brain sciences. Some of the key questions answered are as
follows. Why do we see visual illusions, and why are illusions
inevitable for any finite-speed vision machine? Why aren't brains
universal learning machines, and what does the riddle of induction
and its solution have to do with human learning and innateness? The
author tackles such questions as why the brain is folded, and why
animals have as many limbs as they do, explaining how these relate
to principles of network optimality. He describes how most natural
language words are vague and then goes on to explain the connection
to the ultimate computational limits on machines. There is also a
fascinating discussion of how animals accommodate greater
behavioral complexity. This book is a must-read for researchers
interested in taking a high-level, non-mechanistic approach to
answering age-old fundamental questions in the brain sciences.
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