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Related Experiment Videos

Inferences about unobserved causes in human contingency learning.

York Hagmayer1, Michael R Waldmann

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

Quarterly Journal of Experimental Psychology (2006)
|March 17, 2007
PubMed
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People assess cause-and-effect relationships differently during learning versus after. Initial judgments lean towards interdependent causes, while final conclusions favor independent causes, impacting causal inference models.

Area of Science:

  • Cognitive Psychology
  • Causal Inference
  • Machine Learning

Background:

  • Estimating causal efficacy requires accounting for unobserved causes.
  • Theoretical approaches differ on whether unobserved causes are always present or may be absent.
  • Causal Bayes net theories often assume cause independence for simplified modeling.

Purpose of the Study:

  • To investigate how people assess the presence of unobserved causes during causal learning.
  • To examine whether assumptions about cause interdependence or independence change over time.
  • To compare judgments made during active intervention versus passive observation.

Main Methods:

  • Two experiments were conducted where participants learned causal relationships.
  • Learning involved observing co-occurrence (Experiment 1) or active intervention (Experiment 2).

Related Experiment Videos

  • Assumptions about unobserved causes were assessed online during learning and in final judgments.

    • Main Results:

    • A dissociation was observed in participants' judgments.
    • Online judgments during learning showed a tendency to assume interdependence of causes.
    • Final judgments after the learning phase leaned towards an independence assumption.

    Conclusions:

    • Human causal learning involves dynamic shifts in assumptions about unobserved causes.
    • The timing of judgment assessment influences whether interdependence or independence is favored.
    • Findings have implications for understanding human causal inference and developing AI learning models.