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Related Experiment Videos

Seeing versus doing: two modes of accessing causal knowledge.

Michael R Waldmann1, York Hagmayer

  • 1Department of Psychology, University of Göttingen, Göttingen, Germany. michael.waldmann@bio.uni-goettingen.de

Journal of Experimental Psychology. Learning, Memory, and Cognition
|March 10, 2005
PubMed
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Humans can predict outcomes of actions using observational data, distinguishing between "seeing" and "doing." This causal reasoning ability, demonstrated in experiments, differs from observed patterns and challenges simpler learning theories.

Area of Science:

  • Cognitive Science
  • Causal Inference
  • Machine Learning

Background:

  • Causal reasoning allows predictions of action outcomes from observational data.
  • Distinguishing between observation ('seeing') and intervention ('doing') is crucial for accurate causal inference.

Purpose of the Study:

  • To investigate if humans differentiate between 'seeing' and 'doing' when making predictions from learned causal models.
  • To test the ability of causal reasoning theories to model these predictions.

Main Methods:

  • Four learning experiments using deterministic and probabilistic observational data.
  • Participants learned causal model parameters through observation only.
  • Predictions were elicited based on hypothetical 'seeing' vs. 'doing' scenarios.

Related Experiment Videos

Main Results:

  • Participants made distinct predictions for 'seeing' versus 'doing' scenarios.
  • Predictions were sensitive to causal model structure and parameter magnitudes.
  • This predictive ability differed significantly from purely observed data patterns.

Conclusions:

  • Humans exhibit sophisticated causal reasoning, distinguishing observational learning from active intervention.
  • This competency is not explained by associative or simple probabilistic theories.
  • Causal Bayes net theories offer a framework for modeling this nuanced predictive ability.