Self-Directed Learning Favors Local, Rather Than Global, Uncertainty

Douglas B. Markant, Burr Settles, Todd M. Gureckis

Research output: Contribution to journalArticlepeer-review


Collecting (or "sampling") information that one expects to be useful is a powerful way to facilitate learning. However, relatively little is known about how people decide which information is worth sampling over the course of learning. We describe several alternative models of how people might decide to collect a piece of information inspired by "active learning" research in machine learning. We additionally provide a theoretical analysis demonstrating the situations under which these models are empirically distinguishable, and we report a novel empirical study that exploits these insights. Our model-based analysis of participants' information gathering decisions reveals that people prefer to select items which resolve uncertainty between two possibilities at a time rather than items that have high uncertainty across all relevant possibilities simultaneously. Rather than adhering to strictly normative or confirmatory conceptions of information search, people appear to prefer a "local" sampling strategy, which may reflect cognitive constraints on the process of information gathering.

Original languageEnglish (US)
Pages (from-to)100-120
Number of pages21
JournalCognitive Science
Issue number1
StatePublished - Jan 1 2016


  • Active learning
  • Information sampling
  • Machine learning
  • Self-directed learning

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence


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