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Steven Pinker -- How the Mind Works - Hampshire High Italian ...

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Trie Mini's Eye | 235terns can sometimes be solved with <strong>the</strong> technique called constraint satisfactionthat we met in Chapter 2 when looking at Necker cubes andaccented speech. When <strong>the</strong> parts of a puzzle cannot be solved one at atime, <strong>the</strong> puzzle-solver can keep in mind several guesses for each one,compare <strong>the</strong> guesses for <strong>the</strong> different parts of <strong>the</strong> puzzle, and sec whichones are mutually consistent. A good analogy is working on a crosswordpuzzle with a pencil and an eraser. Often a clue for a horizontal word isso vague that several words can be penciled in, and a clue for a verticalword is so vague that several words can be penciled in. But if only one of<strong>the</strong> vertical guesses shares a letter with any of <strong>the</strong> horizontal guesses,that pair of words is kept and <strong>the</strong> o<strong>the</strong>rs are erased. Imagine doing thatfor all <strong>the</strong> clues and squares at once and you have <strong>the</strong> idea of constraintsatisfaction. In <strong>the</strong> case of solving <strong>the</strong> correspondence problem in stereovision, <strong>the</strong> dots are <strong>the</strong> clues, <strong>the</strong> matchups and <strong>the</strong>ir depths are <strong>the</strong>guesses, and <strong>the</strong> three assumptions about <strong>the</strong> world are Jike <strong>the</strong> rulesthat say that every letter of even' word must sit in a box, every box musthave a letter in it, and all <strong>the</strong> sequences of letters must spell out words.Constraint satisfaction can sometimes be implemented in a constraintnetwork like <strong>the</strong> one I presented on page 107. Marr and <strong>the</strong> <strong>the</strong>oreticalneuroscientist Tomaso Poggio designed one for stereo vision. Theinput units stand for points, such as <strong>the</strong> black and white squares of arandom-dot stereogram. They feed into an array of units that representall of <strong>the</strong> M x 72 possible matchups of a point in <strong>the</strong> left eye with someo<strong>the</strong>r point in <strong>the</strong> right eye. When one of <strong>the</strong>se units turns on, <strong>the</strong> networkis guessing that <strong>the</strong>re is a splotch at a particular depth in <strong>the</strong> world(relative to where <strong>the</strong> eyes have converged). Here is a bird's-eye view ofone plane of <strong>the</strong> network, showing a fraction of <strong>the</strong> units.Array of processors, one for each matchupp.Processors along a 0—^0line of sight ^^\ O "0 D KI - t_L ivy x . .• \ ,• -, Neighboring processorsinhibit one ano<strong>the</strong>r ,Q &. & O „, . „_„ J„,,U- -. .. .. y L . *• ., ^—at The same deptn~/y^ reinfoice one ano<strong>the</strong>rDepth[npot from <strong>the</strong> left eyeInput from <strong>the</strong> right eye

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