Over more than a half century of experience in research on learning, memory, and decision, I have come to believe that the most substantial and enduring advances have not been in the accumulation of empirical facts or the construction of models, but in the production of fruitful interactions between models and experimental research. Most experimental facts require continual reinterpretation and most models drop by the wayside like autumn leaves, but the results of interactions between models and experiments constitute most of our generalizable knowledge. Success in the interactive research effort depends not only on clearly formulated models and well-conducted experiments, but, just as importantly, on sound interpretations of the results of applying the models to the experiments. This interpretive phase of the effort is in some respects the most difficult, and I take as my main task in this article an account of some of the issues that have to be resolved and some of the traps that have to be avoided in order for the process to run to a successful conclusion. As a preliminary, I turn to a review of the basic concept of applying a model to data as it has evolved since its first rudimentary instantiation in the literature of memory and decision more than a century ago. Applying Models to Experiments Details of techniques for fitting curves, or, more broadly, formal models, whether mathematical or computer imple-This article presents in substance the author’s Governing Board Keynote

I present D cognitive model of the humon ability lo acquire c.us.I relotionshipr. I report on experimental evidence demonrtroting that human leornerr acquire occwote cwxd relationships more rapidly when training examples oreconrirtent with o general theory of cwsolity. This article describes o learning procerr that uses o general theory of causality OS background knowledge. The leorning pro-cess, which I cdl theory-driven learning (TDL), hypothesizes cw~a1 relationships consistent both with observed doto and the general theory of courolity. TDL accounts for data on both the rote a+ which humon learners acquire couscll relo-tionrhips, and the types of COUSJ relationships they acquire. Experiments with TDL demonrtrote the odvontoge of TDL for acquiring cowa relationships over similarity-bored opproacher to learning: Fewer examples ore required to loom an acc~rote relotionrhio. 1.