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Humans can do multiple tasks at the same (e.g. reading while listening to music), but we memorize information from less focused sources with worse efficiency than we do from our main focus or task.

Do such things exist in the case of artificial intelligence? I doubt, for example, that neural networks have such characteristics, but I may be wrong.

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Douglas Hofstadter's CopyCat architecture for solving letter-string analogy problems was deliberately engineered to maintain a semantically-informed notion of 'salience', i.e. given a variety of competing possibilities, tend to maintain interest in the one that is most compelling. Although the salience value of (part of) a solution is ultimately represented numerically, the means by which it determined is broadly intended to correspond (at least functionally) to the way 'selective attention' might operate in human cognition.

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Concentration, perhaps easier to grasp as "focus" or "attention", has quite some history in AI. This answer mentions CopyCat, and there was work with neural networks in the 80s as well (e.g. from Fukushima, creator of the Neocognitron).

More recently, attention in neural networks is gaining momentum. The mechanisms are applied to learning in deep neural networks.

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