Information sampling behavior with explicit sampling costs

• Published in: Decision (Washington, D.C.) Vol. 3; no. 3; p. 147
• Main Authors: Juni, Mordechai Z, Gureckis, Todd M, Maloney, Laurence T
• Format: Journal Article
• Language: English
• Published: United States 01.07.2016
• Subjects: information gathering behavior; optimal stopping; sensitivity to sample dispersion; ideal observer model; cost-benefit analysis
• ISSN: 2325-9965
• DOI: 10.1037/dec0000045

The decision to gather information should take into account both the value of information and its accrual costs in time, energy and money. Here we explore how people balance the monetary costs and benefits of gathering additional information in a perceptual-motor estimation task. Participants were rewarded for touching a hidden circular target on a touch-screen display. The target's center coincided with the mean of a circular Gaussian distribution from which participants could sample repeatedly. Each "cue" - sampled one at a time - was plotted as a dot on the display. Participants had to repeatedly decide, after sampling each cue, whether to stop sampling and attempt to touch the hidden target or continue sampling. Each additional cue increased the participants' probability of successfully touching the hidden target but reduced their potential reward. Two experimental conditions differed in the initial reward associated with touching the hidden target and the fixed cost per cue. For each condition we computed the optimal number of cues that participants should sample, before taking action, to maximize expected gain. Contrary to recent claims that people gather less information than they objectively should before taking action, we found that participants over-sampled in one experimental condition, and did not significantly under- or over-sample in the other. Additionally, while the ideal observer model ignores the current sample dispersion, we found that participants used it to decide whether to stop sampling and take action or continue sampling, a possible consequence of imperfect learning of the underlying population dispersion across trials.