Our general approach is to study the interaction of top-down knowledge about abstract characteristics of causality and bottom-up contingency learning. The majority of current learning theories view learning as a purely data-driven, associative process ("bottom up"). In contrast, our theory ("causal-model theory") assumes that the processing of the learning input is partly determined by domain knowledge. We are particularly interested in the role of abstract knowledge about causality, such as knowledge about causal directionality, causal relevance, causal structures, and causal interventions. In a number of studies we have shown that this kind of knowledge may dramatically affect learning despite the fact that the learning input was kept constant. Currently we are planning to explore the neural basis of associative as opposed to causal learning processes.
Categorization and Induction
In this project we are interested in the interplay between alternative categorial frameworks and induction. The traditional approach to categorization claims that categories mirror the correlational structure of the en-vironment. By contrast, we argue that in many domains there are alternative ways of categorizing the world. For example, human behavior may either be explained by functional, cognitive or by neuropsychological theories. We are interested in factors determining the way domains are categorized, and in the influence of alternative categorial schemes on subsequent induction processes.
University of Göttingen
Waldmann M R (2000) Competition among causes but not effects in predictive and diagnostic learning. Journal of Experimental Psychology: Learning, Memory, and Cognition 26: 53-76
Waldmann M R & Hagmayer Y (1999) How categories shape causality. In M. Hahn & S. C. Stoness (Eds.), Proceedings of the Twenty-first Annual Conference of the Cognitive Science Society (pp. 761-766). Mahwah, NJ: Erlbaum
Waldmann M R (1996) Knowledge-based causal induction. In D. R. Shanks, K. J. Holyoak, & D. L. Medin (Eds.), The psychology of learning and motivation, Vol. 34: Causal learning (pp. 47-88). San Diego: Academic Press
Waldmann M R, Holyoak K J & Fratianne A (1995) Causal models and the acquisition of category structure. Journal of Experimental Psychology: General 124: 181-206
Waldmann M R & Holyoak K J (1992) Predictive and diagnostic learning within causal models: Asymmetries in cue competition. Journal of Experimental Psychology: General 121: 222-236