Psychonomics 2016 - Adaptive exploration in decisions from experience

Imagine that you’re on Amazon trying to decide which of two products to purchase. One way to learn about the options before you buy is to look at reviews from other people who have bought the same items. Each review gives you a glimpse into the relative value of each option, and you can explore each item (i.e., keep on reading reviews) for as long as you like until you feel ready to hit the buy button.

So, how many reviews do you decide to read for each product? How does the variability in the ratings affect how long you explore—for example, is there a difference between seeing a string of solid 4-star reviews as opposed to a mixture of 5-star and 3-star reviews? How does your exploration change when searching for a relatively mundane product like a power adapter as opposed to a major purchase like a computer?1

At Psychonomics this week I’m presenting some new results from a project where we try to understand how people adapt their exploration in response to these kinds of environmental factors, including the variability in the outcomes they experience and the rewards that are at stake. The experiment described in the poster below was designed to test the predictions of a sequential sampling model that accounts for this adaptive exploration and its effects on how people make choices. Sequential sampling models are widely used to model the relationship between choices and response times in a number of decision making tasks2, and our results suggest that similar mechanisms can account for the way that people sample experiences from the environment prior to making a choice.

Click on the image below to get the PDF of the poster:


  1. I’m using product reviews as an illustrative example of experience-based decision making in the wild (our experiment was more stripped down), but some recent research has looked exactly at how sampling review ratings to learn about products impacts choice (see here). 

  2. See this recent blog post for a nice introduction to sequential sampling models as applied to choice RT.