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Sudoku puzzles – why they’re good for you

Posted
by DPS

Solving Sudoku puzzles depends on neural pathways that even the most powerful computers can’t replicate, according to research published on the Princeton University physics website.

By studying how people solve the puzzles, we might be able to develop more intelligent and brain-like computers.

The Sudoku mathematical puzzle involves filling in a grid of 81 squares with varying combinations of the numbers one to nine, which can be diabolically difficult.

To crack Sudoku, brains use a unique set of neural pathways known as associative memory which enables discovery of a pattern from a partial clue.

The task of completing a Sudoku puzzle is confounded because of the large number of possible permutations of this pattern. But every time a right number goes in the right place it provides a clue, which reduces the number of permutations.

In this way Sudoku is based on a combination of logic and intelligent guesswork based on our abilities of associative memory. Although computers can store large amounts of information and process it at great speed, they aren’t yet capable of sophisticated associative memory.

In neural terms, the signals developed can produce a strong and reasonably accurate feeling of correctness of the item retrieved. This may account for a strong psychological feeling of ‘right’ or ‘wrong’ when a memory is retrieved from a minimal clue.

The process also helps to remember a name from a fragment or recognise a partially obscured face.

For example, for a computer to recognise a partially visible face it would first have to recognise that the face is obscured, then that it is a face, and then it would have to find a match.

To answer all these questions takes an enormous amount of computation, compared to humans doing it in a fraction of a second.

There were would be significant implications if we can figure out how this is done and design computers that can replicate it. Being able to mimic associative memory would give computers extreme robustness of pattern recognition.

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