Generality of the summation effect in human causal learning

Abstract

Considerable research has examined the contrasting predictions of the elemental and configural association theories proposed by Rescorla and Wagner (1972) and Pearce (1987), respectively. One simple method to distinguish between these approaches is the summation test, in which the associative strength attributed to a novel compound of two separately trained cues is examined. Under common assumptions, the configural view predicts that the strength of the compound will approximate to the average strength of its components, whereas the elemental approach predicts that the strength of the compound will be greater than the strength of either component. Different studies have produced mixed outcomes. In studies of human causal learning, Collins and Shanks (2006) suggested that the observation of summation is encouraged by training, in which different stimuli are associated with different submaximal outcomes, and by testing, in which the alternative outcomes can be scaled. The reported experiments further pursued this reasoning. In Experiment 1, summation was more substantial when the participants were trained with outcomes identified as submaximal than when trained with simple categorical (presence/absence) outcomes. Experiments 2 and 3 demonstrated that summation can also be obtained with categorical outcomes during training, if the participants are encouraged by instruction or the character of training to rate the separately trained components with submaximal ratings. The results are interpreted in terms of apparent performance constraints in evaluations of the contrasting theoretical predictions concerning summation.

Keywords:

• Summation
• Configural
• Elemental
• Causal learning
• Rescorla–Wagner model

Acknowledgments

This work was supported by Fondecyt Grant 1060838 to E. H. Vogel.

Notes

¹ Pearce's proposal of contextual cues in compound with A, B, and AB predicts summation by allowing for enhanced generalization between AX or BX and ABX in testing, which is not fully offset by the greater generalization between AX and BX during training. Kinder and Lachnit (2003, see also Pearce, Esber, George, & Haselgrove, 2008) have suggested a more general parametric variation in Pearce's similarity function that is in this spirit, but has not been articulated to address the specific requirements that A and B be effectively more similar to AB but not to each other.