Number Of Materials, Dimensions And Structural Complexity Of The Impact Of The Category Learning

Category learning is the way of obtaining category representation. Mechanism of procession of category learning is the mechanism of procession of mode forming. The result of category learning is the form of category representation. It could be applied to several aspects of human cognition. It is the foundation of some basic cognitive capability such as attention, memory, classification, decision-making, problem-solving and reasoning. Family resemblances category is the usual forming in category learning. The changing of manner of category learning and category dimensionality have influences on category learning.This paper has three experiments. Experiment 1 will explore the influences which Changing of complexity of category dimensionality have on category learning in the way of observing learning. Experiments 2 will explore the influences which Changing of complexity of category dimensionality have on category learning in the way of feedback learning. Experiments 3 will explore the influences which Changing of complexity of category dimensionality have on category learning in the way of inference learning. All these three experiments take example learning and character learning as dependent factors to explore the impact of number and structure of category dimensionality on category learning. These researches have come to these conclusions:(1) In the condition of observing learning, the number of category dimensionality has no impact on example learning, but has impact on feature learning. There are significant differences on the number of correct recognition of feature, but no significant differences on the accuracy of recognition of feature. The structure of category dimensionality has no impact on feature learning, but has impact on character learning example learning. There are significant differences between linearly separable structure and no-linearly separable structure.(2) In the condition of feedback learning, the number of category dimensionality has no impact on example learning and on feature learning, only on the number of correct recognition of feature. The structure of category dimensionality has impact on example learning and feature learning. Linearly separable structure is more easy for learning.(3) In the condition of inference learning, the number of category dimensionality has impact on example learning and on feature learning. Six dimensions could be very important for rule come into being of category leaning. The structure of category dimensionality has impact on example learning and feature learning. Linearly separable structure is easier for learning.(4) The changing of manner of category learning, feedback learning is useful for example learning, inference learning is useful for feature leaning. When more dimensions involved, more features will be learned. This is a complex mechanism, needs more experiments' support. Linearly separable structure is easier for learning. There is no-interaction of style of learning and numbers of dimensions, but on style of learning and structures of dimensions.