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Causes versus enabling conditions.

P W Cheng1, L R Novick

  • 1Department of Psychology, University of California, Los Angeles 90024-1563.

Cognition
|August 1, 1991
PubMed
Summary
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People differentiate between causes and enabling conditions, a distinction not explained by necessity or sufficiency alone. A new probabilistic contrast model explains this by examining covariation within a focal set of events.

Area of Science:

  • Cognitive Science
  • Philosophy of Science
  • Psychology

Background:

  • Humans distinguish between causes and enabling conditions.
  • Traditional models of reasoning based on necessity and sufficiency fail to explain this distinction.
  • Existing explanations involve normality or conversational principles.

Purpose of the Study:

  • To evaluate existing explanations for the cause-condition distinction.
  • To propose and test a new probabilistic contrast model.

Main Methods:

  • Evaluation of normality-based and conversational accounts of causal reasoning.
  • Development of a probabilistic contrast model focusing on covariation within a focal set.
  • Two experiments comparing the probabilistic contrast model against normality and conversational views.

Related Experiment Videos

Main Results:

  • The probabilistic contrast model provides a novel explanation for distinguishing causes from enabling conditions.
  • Experimental results support the probabilistic contrast model over normality and conversational approaches.
  • Covariation within a focal set, defined probabilistically, is key to this distinction.

Conclusions:

  • The distinction between causes and enabling conditions can be explained by probabilistic covariation.
  • The probabilistic contrast model offers a more robust account than previous necessity, sufficiency, normality, or conversational models.
  • This research advances our understanding of causal reasoning and its contextual dependencies.