WHAT WE WANTED TO DISCOVER (PROJECT GOALS)

We wanted to learn more about when and why distracting stimuli (i.e., something we see or hear) automatically capture attention.

We defined the following project goals:

Goal 1. Observe how participants react to different types of distraction. Do participants perceive irrelevant stimuli as more distracting when they are visual as opposed to auditory? Are there certain patterns that participants are picking up on while doing the task?

Goal 2. Explore how participants perform in tasks with different types of distraction. When an irrelevant stimulus LOOKS like something participants need to respond to, are there steep declines in performance? Or do participants quickly adapt to it?

Goal 3. Test when the ability to ignore one type of distraction helps participants to ignore another distraction type. For example, when participants can ignore something distracting that they see, when will this help them to ignore something distracting that they hear?

OUR PLAN FOR SUCCESS (RESEARCH STRATEGY)

To pin down the root cause of when and why distracting stimuli automatically capture attention, we decided to run a series of studies to investigate a variety of different factors. Some of these factors included:

a) How closely distracting stimuli resembled something participants needed to respond to (i.e., a target)

b) Whether participants could use distracting stimuli to help them complete the task

OUR MAIN CHALLENGE

The main challenge we encountered was ruling out alternative explanations of our observations. To address this issue, the research team ideated and iterated a series of prototype study designs prior to data collection.

HOW WE EXECUTED OUR STRATEGY (RESEARCH PROCESS)

We used a mixed-methods approach involving testing tasks and surveys.

Testing Tasks

We programmed a series of tasks that varied in a number of factors, such as the type of distraction and degree of randomization.

Surveys

We administered surveys at the beginning of all studies to help us characterize the sample (e.g., gender, health status, etc.).

Analysis

We analyzed the response times and error rates via ANOVA and t-tests. We used demographic survey data to see whether our findings differed by age, gender, or health status. We used data visualizations to help interpret emerging patterns of behavior across the different measures and tasks.

TOOLS WE USED

Tools – Multivariate Testing, Participant Recruitment, Surveys, Data Analysis, Data Visualization

WHAT WE LEARNED (DISCOVERIES)

Insight 1: The more a distracting stimulus resembles something you need to respond to, the more it will capture your attention.

Insight 2: You are more likely to remember a distracting stimulus if you need to respond to it. This can both help and hurt performance.

Insight 3: The more a distracting stimulus reliably predicts the appearance of something task-relevant (e.g., a target), the more it will capture your attention.

THE IMPACT WE HAD

Our research process led to this being a highly comprehensive study on when and why distracting stimuli grab our attention in everyday life. Our discoveries hold great promise for direct applications in advertising and web design.