Interaction between previous beliefs and cue predictive value in covariation-based causal induction.
The main aim of this work was to show the impact of preexisting causal beliefs on causal induction from cause-effect co-occurrence information, when several cues compete with each other for predicting the same effect. Two different causal scenarios -- one social (a), the other medical (b) -- were used to check the generality of the effects. In Experiments 1a and 1b, participants were provided information on the co-occurrence of a two-cause compound and an effect, but not about the potential relationship between each cause by its own and the effect. As expected, prior beliefs -- induced by means of instructions -- strongly modulated the causal strength assigned to each element of the compound. In Experiments 2a and 2b, covariation evidence was provided, not only about the predictive value of the two-cause compound, but also about one of the elements of the compound. When this evidence was available, prior beliefs had less impact on judgments, and these were mostly guided by the relative predictive value of the cue. These results demonstrate the involvement of inferential integrative mechanisms in the generation of causal knowledge and show that single covariation detection mechanisms -- either rule-based or associative -- are insufficient to account for human causal judgment. At the same time, the fact that the power of new covariational evidence to change prior beliefs depended on the availability of information on the relative (conditional) predictive value of the target candidate cause suggests that causal knowledge derived from information on causal mechanisms and from covariation probably share a common representational basis.
Catena
Andrés
A
Departamento de Psicología Experimental y Fisiología del Comportamiento, Facultad de Psicologia, Universidad de Granada, Campus de Cartuja, Granada, Spain. acatena@ugr.es
Maldonado
Antonio
A
Perales
José C
JC
Cándido
Antonio
A
D003071:Cognition; D003463:Cues; D003469:Culture; D006801:Humans; D008960:Models, Psychological; D011446:Prospective Studies