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Related Experiment Video

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Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

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Published on: September 16, 2015

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

Douglas B Markant¹, Burr Settles², Todd M Gureckis³

¹Center for Adaptive Rationality, Max Planck Institute for Human Development.

Cognitive Science

|March 20, 2015

Summary

People prefer to gather information that clarifies specific choices, rather than broadly reducing uncertainty. This "local" information sampling strategy may be due to cognitive limitations during learning.

Keywords:

Active learning Information sampling Machine learning Self-directed learning

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Area of Science:

Cognitive Science
Machine Learning
Information Theory

Background:

Information gathering is crucial for learning, but optimal strategies are not well understood.
Active learning research in machine learning offers models for information sampling.
Human decision-making in information acquisition requires further investigation.

Purpose of the Study:

To model and investigate human strategies for selecting information during learning.
To compare different theoretical models of information sampling.
To understand the cognitive underpinnings of information-gathering decisions.

Main Methods:

Developed theoretical models of information sampling inspired by active learning.
Conducted a novel empirical study to analyze information-gathering decisions.
Applied model-based analysis to participant data.

Main Results:

People favor information that resolves uncertainty between two options over broad uncertainty reduction.
A 'local' sampling strategy was identified, differing from purely normative or confirmatory approaches.
Findings suggest cognitive constraints influence information selection.

Conclusions:

Human information sampling is not strictly normative; a 'local' strategy prevails.
Cognitive limitations likely shape how individuals seek information for learning.
Understanding these strategies can inform educational and AI-driven learning systems.