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.

Background

The goal is to have this experiment be a lodestar for easily calibrating experiment environments online, in the form of an library. We will want the library to have modular content, where necessary calibration tests can be added in. Similar libraries are https://www.jspsych.org/ and https://www.psychopy.org/.

Methods

First, screen resolutions vary considerably among participants. In fact, average deviations in participant screen resolutions are 243 and 136 pixels (width and height). As a work around, we can ask for participants to use a real-world object for measurement. They will have to hold up a credit card (which is standardized in size at 85.6mm in width) and be asked to adjust a shape on the screen to match it in size. We will then be able to measure the logical pixel density:

$LPD(px/mm)=OnscreenDistance(px)/PhysicalDistance(mm)$

The logical pixel density enables us to translate between physical distance and on-screen pixel length.

What participants see on Mechanical Turk. They are able to move the sliding bar to change the size of the on-screen credit card.

Next, viewing distance can vary among participants. The experiment can take advantage of the blind spot in the human eye (entry of the optic nerve on the retina). The entry point of the blind spot is located at approximately 13.5° horizontally. Using this information, combined with the logical pixel density, we can calculate the distance between the participant and their screen.

The trigonometry behind distance estimation (taken from [2]).

For determining the blind spot, we will make use of a sweeping red dot, moving away from the point of eye fixation until the viewer can just not see it peripherally. A modification was made to the method suggested in [2], where we will no longer be relying on the reaction of participant. Instead, the participant will be able to use a sliding bar to adjust the red dot.

Failed to parse (syntax error): {\displaystyle Viewing Distance(d)=Physical Distance(s)/tan⁡(\alpha)}

To ensure that there is consistency in brightness and screen contrast, we present a band of grey bars. Participants will be asked to adjust their computer brightness and contrast settings until they can distinguish all 20 bars. We are able to get information about the user’s operating system and provide them instructions on how to modify their contrast and brightness settings. The grayscale images show a brightness range of 5-255, defined in RGB values and with a change of 5 RGB steps each patch. Again, we are capable of recording this information to a database.

Brightness and contrast test - Gray patches to determine the perceived white and black point.

A user prompt from Modifying contrast on MacOS.

Next, we also can check for the display gamma. The industry standard has converged to a standard display gamma of 2.2, but there can be variability in that of participant computers. We use our knowledge that gamma correction is defined by the power-law expression. We ask users to perceptually compare a solid gray (col3) inner box to and outer box of alternating lines of black/white (col1/col2).

Appendix

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