Complexity Measures and Classification Learning
نویسندگان
چکیده
In classification learning experiments, test subjects are presented with objects which they must categorize. The correct categories, which are known to the experimenter, are functions of the characteristics (“dimensions”) of the objects, such as size, color, brightness, and saturation. The experiments measure the relative difficulty of learning different categorizations. One major factor which influences the difficulty of learning is whether the dimensions are easily distinguishable by the subjects. Separable dimension problems are ones in which humans can differentiate dimensions of the objects, e.g. size, color, and shape. Integral dimension problems are ones in which humans can not (easily) distinguish the dimensions, e.g. brightness, saturation, and hue. Psychologists reason that when dimensions are separable, humans develop logical rules about which dimensions matter, and when they are integral, humans do not (cannot) develop these rules.
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