Comparing associative, statistical, and inferential reasoning accounts of human contingency learning

Abstract

For more than two decades, researchers have contrasted the relative merits of associative and statistical theories as accounts of human contingency learning. This debate, still far from resolution, has led to further refinement of models within each family of theories. More recently, a third theoretical view has joined the debate: the inferential reasoning account. The explanations of these three accounts differ critically in many aspects, such as level of analysis and their emphasis on different steps within the information-processing sequence. Also, each account has important advantages (as well as critical flaws) and emphasizes experimental evidence that poses problems to the others. Some hybrid models of human contingency learning have attempted to reconcile certain features of these accounts, thereby benefiting from some of the unique advantages of different families of accounts. A comparison of these families of accounts will help us appreciate the challenges that research on human contingency learning will face over the coming years.

Figure 1

2 × 2 contingency matrix. Cells in this matrix store the frequencies of the presence and the absence of the cue and the outcome: Cell a = frequency of cue and outcome present (fa); Cell b = frequency of cue present and outcome absent (fb); Cell c = frequency of cue absent and outcome present (fc); Cell d = frequency of cue and outcome absent (fd).