Skip to main content
eScholarship
Open Access Publications from the University of California

One and known: Incidental probability judgments from very few samples

  • Author(s): Singhal, Ishan;
  • Srinivasan, Narayanan;
  • Srivastava, Nisheeth
  • et al.
Abstract

We test whether people are able to reason based on incidentally acquired probabilistic and context-specific magnitude information. We manipulated variance of values drawn from two normal distributions as participants perform an unrelated counting task. Our results show that people do learn category-specific information incidentally, and that the pattern of their judgments is broadly consistent with normative Bayesian reasoning at the cohort level, but with large individual-level variability. We find that this variability is explained well by a frugal memory sampling approximation; observer models making this assumption explain approximately 70% of the variation in participants' responses. We also find that behavior while judging easily discriminable categories is consistent with a model observer drawing fewer samples from memory, while behavior while judging less discriminable categories is better fit by models drawing more samples from memory. Thus, our model-based analysis additionally reveals resource-rationality in memory sampling.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View