Elemental Causal Learning from Transitions
نویسندگان
چکیده
Much research on elemental causal learning has focused on how causal strength is learned from the states of variables. In longitudinal contexts, the way a cause and effect change over time can be informative of the underlying causal relationship. We propose a framework for inferring the causal strength from different observed transitions, and compare the predictions to existing models of causal induction. Subjects observe a cause and effect over time, updating their judgments of causal strength after observing different transitions. The results show that some transitions have an effect on causal strength judgments over and above states.
منابع مشابه
Elemental causal induction 1 Running head: ELEMENTAL CAUSAL INDUCTION Elemental causal induction
We present a framework for the rational analysis of elemental causal induction – learning about the existence of a relationship between a single cause and effect – based upon causal graphical models. This framework makes precise the intuitive distinction between causal structure and causal strength: the difference between asking whether or not a causal relationship exists, and asking how strong...
متن کاملStructure and strength 1 Running head: STRUCTURE AND STRENGTH Structure and strength in causal induction
We present a framework for the rational analysis of elemental causal induction – learning about the existence of a relationship between a single cause and effect – based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship exists and asking how strong that causal relat...
متن کاملStructure and strength in causal induction q
We present a framework for the rational analysis of elemental causal induction—learning about the existence of a relationship between a single cause and effect—based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship exists and asking how strong that causal relations...
متن کاملTHE iNFLUENCE OF KNOWLEDGE ABOUT CAUSAL MECHANiSMS ON COMPOUND PROCESSiNG
Empirical evidence has shown that several factors influence whether a compound is represented as several independent components or as a configuration. However, most of the previous research focused on data-driven factors (e.g., modality of the stimuli presented in the experimental task). in one experiment, i analyzed the influence of people’s knowledge about causal mechanisms on compound proces...
متن کاملRepresentation and Generalisation in Associative Systems
This paper examines the nature of stimulus representation in associative learning systems. Specifically, it addresses the issue of whether representation is elemental or configural in nature. We use a human causal learning paradigm, employing contingencies more commonly associated with studies of retrospective revaluation. Whereas most models of retrospective revaluation view it as an entirely ...
متن کامل