Associative and Normative Models of Causal Induction: Reacting to Versus Understanding Cause
Baker AG., Murphy RA., Vallee-Tourangeau F.
This chapter discusses two views of causal judgment that are roughly analogous to a distinction between being able to react appropriately to causes and being able to understand them. The associationist view is identified with the British Empiricists. It claims that the judgments of cause come from certain empirical cues to causality, which includes: (1) regular succession, (2) temporal contiguity, and (3) spatial contiguity. Associations between events are strengthened when the events are contiguous and are weakened when an event occurs by itself. These models have the advantage that they are computationally simple and they impose a low memory load on the organism because experience is stored as a small number of associative strengths. They have the disadvantage that information about past events is lost in the computation. Also, these models do not have episodic memory. The second classes of models are referred to as normative models. They claim that humans and other animals compute the covariation between cause and effect and then use this information as part of a causal model or schema. The chapter reviews that a retrospective normative model makes the choice of domain in which to do the normative calculation. Associative models and those that involve causal models or schema are appropriate to overlapping but not identical domains of information processing. Simple associative ideas can be used in many situations in which contiguity is important, but in which mental models are unavailable. These can be used in situations in which associative networks are difficult to apply.