Knowledge-Based Causal Induction

Abstract

This chapter reviews that the comparison between causal-model theory and associative accounts of causal induction highlighted a number of important differences between these two approaches. Causal-model theory postulates a rigorous separation between the learning input and mental representations. This characteristic allows for the flexible assignment of the learning input to elements of the resulting mental models. By contrast, most associative learning theories work in the tradition of stimulus response theories in which learning cues play the double causal role of representing events and eliciting responses. It discusses that this inflexibility leads to clear misrepresentations of objective causal relations. Associative theories code the learning cues as CS and the outcomes as US are unable to capture the structural characteristics of diagnostic learning situations in which effects are presented as cues. The Rescorla-Wagner theory correctly captures the asymmetry between causes and effects only when the learning situation is fortuitously presented in a way that corresponds to the implicit structural characteristics of this theory. A second major tenet of causal-model theory postulates the necessity of an interaction between top-down assumptions and the processing of the learning input. Causal-model theory represents reconciliation between theories focusing on statistical covariation learning and theories focusing on causal, mechanical processes. The chapter also explores that causal directionality is one of the most important features of causal relations that determine the way statistical relations are interpreted. It is a physical fact that multiple causes of a common effect potentially interact, whereas multiple effects of a common cause are rendered conditionally independent when the common cause is held constant. The chapter reviews that without prior knowledge that is already available at the outset of the induction process new causal knowledge cannot properly be acquired.