Seeing versus doing: Two modes of accessing causal knowledge

Seeing versus doing: Two modes of accessing causal knowledge (2005)

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by Michael R. Waldmann , York Hagmayer

Venue:	Journal of Experimental Psychology: Learning, Memory, and Cognition
Citations:	11 - 3 self

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Datum	Value	Source
TITLE	Seeing versus doing: Two modes of accessing causal knowledge	INFERENCE
AUTHOR NAME	Michael R. Waldmann	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Göttingen	SVM HeaderParse 0.2
AUTHOR NAME	York Hagmayer	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Göttingen	SVM HeaderParse 0.2
ABSTRACT	The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true causal reasoning. We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, whose parameters were learned in a purely observational learning phase, depending on whether learners believe that an event within the model has been merely observed (“seeing”) or was actively manipulated (“doing”). The predictions reflect sensitivity both to the structure of the causal models and to the size of their parameters. This competency is remarkable because the predictions for potential interventions were very different from the patterns that had actually been observed. Whereas associative and probabilistic theories fail, recent developments of causal Bayes net theories provide tools for modeling this competency. Causal knowledge underlies our ability to predict future events, to explain the occurrence of present events, and to achieve goals by means of actions. Thus, causal knowledge belongs to one of our most central cognitive competencies. However, the nature of causal knowledge has been debated. A number of philosophers and	SVM HeaderParse 0.2
YEAR	2005	INFERENCE
VENUE	Journal of Experimental Psychology: Learning, Memory, and Cognition	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	216--227	INFERENCE
VOLUME	31	INFERENCE
CITATIONS	33 found	ParsCit 1.0