应用解析水平理论考察时间距离对决策的影响

Construal Level Theory is a kind of social cognitive theory, with the main conclusion of that the more time distance is, the more people use high level construal and the more high level construal affect in judging, decision making and such of these cognitive processes, and the low level construal has the mirror process as well. In these ten years, construal level theory is developing very fast and some of the researches of consumer behavior has concerned this developed trend very much. In 2007’s Journal of Consumer psychology, half of the papers focused on construal topics and ask for more researches about these topics. So our research mainly focused on such topics, the application of construal level theory in consumer behavior research. The first part is contained 3 experiments which mean to discuss the questions from reviewing the prior literatures. The research methods, manipulated definition, construal level factors VS construal level events are the goal of first part of this study. The results showed that construal level’s effect might be a consecutive variable and has a consecutive trend when time goes by. Time distance might not be a semantic cognitive effect to construal level but a internal ones for when there’s no verbal information of time distance the results would have the same one as well. The second part of the study is focus on a simplified model of decision-making, which contained two times decision-making, to detect whether and how would the prior decision making affect the next decision making. The study conducted the time distance in first decision making with the situation for the decision and conducted the second time distance in the time distance between the two decision situations. Although we couldn’t get the detail of the weight changes in construal level factors limited by our research, we can have a logical hypothesis based on “one-down- the other-up”. The results showed that the level of construal factors can affect the memory and the more the factors affected, the bigger the error would be. The first time distance and second time distance could effect the second decision in a interaction-way, and support the hypothesis. The whole research is mean to explore the application of construal level theory in consumer behavior research. The conclusion suggests that the periods of products and the consuming may need different marketing strategies in a long-term perspective and time distance should be control are detected in marketing analysis even there’s no time information on the paper.

Cognitive models in biomimetic pattern cognition

We describe a new model which is based on the concept of cognizing theory. The method identifies subsets of the data which are embedded in arbitrary oriented lower dimensional space. We definite k-mean covering, and study its property. Covering subsets of points are repeatedly sampled to construct trial geometry space of various dimensions. The sampling corresponding to the feature space having the best cognition ability between a mode near zero and the rest is selected and the data points are partitioned on the basis of the best cognition ability. The repeated sampling then continues recursively on each block of the data. We propose this algorithm based on cognition models. The experimental results for face recognition demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high and effective.

Biomimetic (topological) pattern recognition - A new model of pattern recognition theory and its application

A new theoretical model of Pattern Recognition principles was proposed, which is based on "matter cognition" instead of "matter classification" in traditional statistical Pattern Recognition. This new model is closer to the function of human being, rather than traditional statistical Pattern Recognition using "optimal separating" as its main principle. So the new model of Pattern Recognition is called the Biomimetic Pattern Recognition (BPR)(1). Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Therefore, it is also called the Topological Pattern Recognition (TPR). The fundamental idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples. We experimented with the Biomimetic Pattern Recognition (BPR) by using artificial neural networks, which act through covering the high dimensional geometrical distribution of the sample set in the feature space. Onmidirectionally cognitive tests were done on various kinds of animal and vehicle models of rather similar shapes. For the total 8800 tests, the correct recognition rate is 99.87%. The rejection rate is 0.13% and on the condition of zero error rates, the correct rate of BPR was much better than that of RBF-SVM.