Chandra Rajyam

Introduction

Over the past few years, we have seen the rise in popularity of online vision-based experiments. By conducting online experiments, researchers can access larger and more diverse participant samples than what may be possible within a lab. In fact, large numbers of participants can be collected at quickly and at relatively low costs (<24h, ~$1-2 USD/participant/10min). There are multiple online resources to recruit participants online, with the most popular being Amazon Mechanical Turk.

However, there are drawbacks over lack of control over the participant’s computer environment. For example, the experimenter will have trouble controlling for screen resolution. In this project, we will set up an experiment using Mechanical Turk that allows for the calibration of viewing conditions. Particularly, we will be focusing on accounting for the variability in hardware and software used by participants.

Appendix

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