Chess and content-oriented psychology of thinking

In this paper a number of principles for content-oriented cognitive psychology will be presented in the context of research into chess players’ information processing. It will be argued that modern theoretical concepts of attention, imagery and memory are based on underlying concepts of capacity and format and that these concepts are not sufficiently powerful to express all phenomena associated with mental contents. Instead, one must develop a genuinely content-oriented theoretical language to discuss, for example, contents and their integration into thinking. The main problem is how to explain the contents of representations. Why do representations have precisely the contents that they have. Here the main attention will be focussed on the question how can one explain the selection of content elements in representations. To formulate the basic concepts of contentoriented thought research several issues must be discussed. Firstly, it will be shown that traditional attention and memory research is capacity-oriented and therefore unable to express mental contents. Secondly, it will be argued that there are content phenomena which must be explained by properties of other content phenomena. Thirdly, it will be shown that in chess, people integrate information into representations by using functional rules or reasons, i.e. concepts and rules, which tell why some information contents must be included in a representation. It will then be shown that people integrate information around learned ‘thought models’ whose contents, together with functional rules or reasons, explain and clarify the contentstructure of a mental representation. It will also be argued that the analysis of contents is metascientifically closer to linguistics with its basic method of explication and content analysis than natural sciences, which form the most common underlying model in current experimental psychology. Finally, content-oriented cognitive psychology and its presuppositions will be compared with neural and computational approaches to show that it gives an additional and alternative theoretical resource, but not a contradictory conceptual platform, to the previous theoretical ways of working with human thinking.