A rough-set and AI based approach for hierarchical cognitive processing of perceptions

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Modelling the psychological and cognitive processes is an important issue in theoretical and clinical aspects. Most of the modellings concern much on qualitative and philosophical descriptions. This research resorts to quantitative modelling of human cognitive processing based on rough sets, AI, and mathematical approaches. We formalise psychological targets and properties by objects and features. A feature domain is associated with these objects and features. Based on the subjects’ perception of the values in the feature domain, the cognitive processing is proceeded via deterministic procedures and non-deterministic procedures. The non-deterministic procedures involve the setting of rough set approximations. To evaluate the parameters involved in the setting, we design several experiments and algorithm, in particular probabilistic structures and entropy, to evaluate the effects of these parameters. These provide a great degree of flexibility in practical applications. These approaches could be easily implemented by or combined with machine learning and artificial intelligence techniques. Our approach could be applied directly in clinical assessment and mental health probing.

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Applied Soft Computing



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