Proceedings of the Annual Meeting of the Cognitive Science Society

In order to understand the relationship between human and machine discovery, it is necessary to collect data about human discoveries which can be compared with machine discovery performance. Historical data are difficult to obtain, are very sparse, and arguably do not reflect a typical human performance. W e contend that cognitive experiments can provide meaningful data, because the discovery tasks can be carefully defined and tailored to the comparison task, and the subject selection can be controlled in diff'erent ways. We describe an experimental study of the human processes of concept formation and discovery of regularities. In our experiments human subjects were allowed to interact with three world models on the computer. Our results demonstrate that humans use heuristics similar to those used in computer discovery systems such as B A C ON or FAHRENHEIT on comparable tasks which include finding one dimensional regularities, generalizing them to more dimensions, finding the scope of a regularity, and introduction of intrinsic concepts. Virtually aU our subjects made some relatively simple discoveries, while some of them were able to develop a complete theory of simple world models. The progress made by human subjects on comparably simple tasks was impeded significantly when the domain became richer, as measured by the number of regularities, their dimensionaUty, and the number of intrinsic concepts involved. This can be called a contextual complexity phenomenon. Our subjects demonstrated a definite pattern of chaotic experimentation and lack of theoretical progress when the level of complexity was too high.