Strategies to intervene on causal systems are adaptively selected

Anna Coenen, Bob Rehder, Todd M. Gureckis

Research output: Contribution to journalArticlepeer-review


How do people choose interventions to learn about causal systems? Here, we considered two possibilities. First, we test an information sampling model, information gain, which values interventions that can discriminate between a learner's hypotheses (i.e. possible causal structures). We compare this discriminatory model to a positive testing strategy that instead aims to confirm individual hypotheses. Experiment 1 shows that individual behavior is described best by a mixture of these two alternatives. In Experiment 2 we find that people are able to adaptively alter their behavior and adopt the discriminatory model more often after experiencing that the confirmatory strategy leads to a subjective performance decrement. In Experiment 3, time pressure leads to the opposite effect of inducing a change towards the simpler positive testing strategy. These findings suggest that there is no single strategy that describes how intervention decisions are made. Instead, people select strategies in an adaptive fashion that trades off their expected performance and cognitive effort.

Original languageEnglish (US)
Pages (from-to)102-133
Number of pages32
JournalCognitive Psychology
StatePublished - Jun 1 2015


  • Causal learning
  • Hypothesis testing
  • Information gain
  • Interventions
  • Self-directed learning

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Linguistics and Language
  • Artificial Intelligence


Dive into the research topics of 'Strategies to intervene on causal systems are adaptively selected'. Together they form a unique fingerprint.

Cite this