PROJECT:
Predicting When People Think Information Is “Worth It”

Information Seeking ❖ Decision Modeling ❖ Gaussian Process Models ❖ Model Comparison

People sometimes gather additional information before deciding, but skip it in other situations. When do people think information is “worth it”, and why?

I built a quantitative model of information seeking in a controlled behavioral task and found that people seek information when they experience conflict between possible actions and want to resolve it.

MY ROLE: I joined the project to deliver the modeling pipeline, including

  • analytical strategy

  • data cleaning and feature engineering

  • modeling (Gaussian Process regression, model comparison)

  • visualization and interpretation

  • stakeholder communication

Behavioral data had been collected prior to my involvement.

BEHAVIORAL INSIGHTS + UX relevance

Research question

When do people seek extra information before deciding, and why? This insight can inform UX design that selectively presents information that users need.

Study design

In a controlled task, participants chose between two options that yielded probabilistic rewards. Across choices, we manipulated:

  • which option was more desirable (higher reward)

  • which option participants believed was likely to yield rewards, and to what extent

Participants could either choose immediately or gather extra information first.

In the experimental task, participants chose between two options. Each option had a different likelihood of yielding rewards, visually indicated by the amount of evidence (ovals). Before deciding, they could gather extra information about how likely each option was to yield a reward.

Finding

Participants sought additional information not when they were most uncertain about which option was likely to reward them, but when the desirable option was currently less likely to reward them. In other words, participants’ information seeking was triggered by a conflict between the two options (the one desirable and the other supported by the current evidence).

Therefore, people strategically seek information to resolve conflict between possible actions.

MODELING + data science relevance

Challenge

Prior to this project, there was no established modeling approach to information seeking. I developed a new approach to uncover the relationship between information seeking and the key experimental variables (which option was desirable, which option was expected to be rewarding). It was critical to estimate the relationship without imposing a specific structure (e.g., linear, quadratic), so that I could conduct a valid statistical inference on its shape.

Approach

I used Gaussian Process (GP) logistic regression. GP regression is a principled way to estimate smooth functions without imposing a form. It is a powerful tool in any domains where we need to uncover interpretable behavior patterns shaped by multiple factors in some unknown manner.

Results

GP regression revealed that participants gathered information the most when the current evidence favored the undesirable option, supporting our behavioral insight that information seeking is triggered by conflict between possible actions.

Participants’ tendency to gather extra information peaked not when both options were equally likely to yield rewards, but when the undesirable option was slightly more likely to yield rewards.

To further evaluate the statistical evidence for this claim, I conducted model comparison by cross validation (leave-one-participant-out). My original model (driven by a conflict between actions) outperformed alternatives.

This demonstrates that advanced, nonstandard methods such as GP can still support rigorous statistical inference with proper techniques such as cross validation.

Note: Modeling code and behavioral data are available at https://gitlab.com/kenji.k/beadsVOI/.