The correlation between intelligence and reaction times is well supported in the science of psychometrics. In this article a potential method for accurately measuring a computer user’s IQ is laid out, based on their preferred mouse pointer speed (the ratio between physical movement of the mouse hardware and movement of the pointer on the device’s screen). This method can be implemented by scientists to screen subjects for IQ studies, and by online advertisers to target a particular IQ demographic. Study designs and caveats are discussed.
As someone who prefers an extremely fast pointer speed on the computers I use, I’ve found that when I let others use my computers, they find the pointer speed unsettlingly fast. While working on a data entry job that required me to copy data from a table on one browser tab and paste it into a web form on another tab, I noticed that with my high pointer speed, I always overshot the targets I wanted to click, then slowed the pointer to a stop, and upon an attempt in the reverse direction at a slower speed, I was able to accurately click the thing I wanted to click.
This sounds like an inefficient method of using a computer, since it requires added cognitive load to reverse direction and go toward the target at a different. My hypothesis is that given a particular level of cognitive processing rate (IQ or reaction times), this overshoot-and-correct method of pinpointing a target on a screen actually saves time and allows the user to complete tasks at a higher speed than if the pointer was slower and the user was able to pinpoint a target with a single attempt.
It is likely that most computer users use the overshoot-and-correct method for pinpointing targets. What interests here is that the faster the pointer speed, the higher the cognitive demands of the overshoot-and-correct method becomes, so that there is a point on the IQ-pointer speed curve at which a particular IQ achieves maximum efficiency (enables the user to be maximally productive at the task they are performing):
The above chart is a hypothetical illustration of the relationship between IQ and mouse pointer speeds, based on a diminishing-returns model.
An Empirical Test of the Hypothesis
A group of subjects, using identical computers, are given a data-entry task to perform. The task involves accurately copying tabulated data on one tab of a browser screen and pasting it in a form on a second browser screen that only accepts one row at a time. The users will click a “submit data” button, at which point the page reloads and the users enter the data for the second row from tab 1. The users are rewarded based on task completion time (not the per-row speed but the speed at which they complete the entire test, perhaps taking 30 minutes) and accuracy.
The browser window should take up only part of the available screen (perhaps two thirds). In the remaining screen space a window is shown that the users can use to control the mouse pointer speed. By default, the mouse pointer speed is set to the slowest possible setting, so that all users will require to adjust the mouse pointer speed to a comfortable level. The users are informed by the researchers that they can increase their efficiency (and potential rewards) by increasing the mouse pointer speed. They must also be informed that if the pointer speed is too high, this can negatively affect their performance.
The time required for the completion of the task should be high enough that the amount of time spent on adjusting the mouse pointer speed should only have a minor negative impact on the completion time. The completion time should also be long enough for the users to find their efficiency-maximizing pointer speed. At the beginning of the task users may choose an unnaturally high speed that slows them down by creating a too high cognitive demand. But given enough time, as mental fatigue sets in, users will likely slow the pointer down until they reach a level of “comfort”. This point of “comfort” is what the researchers are seeking to find out, the point at which the pointer speed is at the maximum speed it can be without overtaxing the brain.
A time of at least 30 minutes will probably be required for the study. A time of one hour might provide further accuracy.
At the end of the study, the researchers will gather the subjects’ mouse pointer speed setting, task completion time, and accuracy. Using the following equation, a score can be given to each subject:
score = mouse pointer speed setting * (1/task completion time) * percentage accuracy
Thus a subject who used a pointer speed setting of 1.5, completed the task in 45 minutes with an accuracy of 99% will have a score of
1.5 * (1/45) * 0.99 = 0.031, while someone who used a setting of 2.5, took 40 minutes with an accuracy of 98% will have a score of
2.5 * (1/40) * 0.98 = 0.061.
Task Design Concerns
The best type of task for this study is one where the best users do not finish the task at significantly less time than the average user. Otherwise, if some users finish at 15 minutes when others take an hour, the study might actually be testing for the subjects’ technical proficiency at copying and pasting data and switching tabs than for their pointer speed point of comfort.
Another concern is the use of keyboard shortcuts, which can significantly enhance task completion time. For this reason subjects can be given computers without keyboards, or if using laptops computers, the keyboards should be covered with a material that effectively prevents the subject from using it.
Generalizing the Results
This IQ-measuring technique will not work on touch devices due to the lack of a mouse. While an interaction-latency technique can be developed (how a long a user takes to interact with the various prompts and other buttons on an app, for example), this will be less useful than the mouse pointer speed technique due to the fact that there is no overshoot-and-correct phenomenon going on. We are left with the much less useful data point of how long a user takes to interact with an app, which is affected by the app’s user experience design, the user’s familiarity with the app, and the size of the screen of their device (whether they can reach the button with the a finger on the same hand that is holding the device, or if they need to use the fingers on the other hand).
The point here is that while on a computer, a generalized tool can be develop that can be embedded on any website to measure a user’s IQ, on touchscreen devices, no such general tool can be developed. On a computer, the generalized tool can instantly tell the user’s IQ without having them do a test (by simply measuring their mouse pointer speed setting), while on a touchscreen device the user will have to be tested and measured for their IQ to be found out.
Accounting for Technical Proficiency
Probably a large percentage of the population are not proficient enough to know how to change their computer’s mouse pointer speed, meaning that they will be stuck with the default pointer speed that comes with their computers. And even more importantly, many are probably unaware that increasing pointer speeds can improve their computer experience. I have seen people use computers with cumbersomely slow pointer speeds, needing to pick up the mouse and drop it somewhere else on their mouse pads to continue finishing a single mouse movement.
For this reason, it must be determined at which IQ level a user becomes self-observant and proficient enough to know that increasing mouse pointer speeds can have a benefit, to know that this is possible on their system, and to know how to do it (or find out how to do it with an internet search). As a hypothetical example, an IQ of 110 might be needed for someone to have the self-observance and proficiency to set their pointer speeds to their comfort levels. This means that if someone has a lower pixels-per-second rate, we cannot accurately tell what their IQ is. It might be 85 or 100. They don’t know how to change their computer’s pointer speed, therefore their pixels-per-second rate alone cannot reliably predict their IQ.
This is partially (or largely) mitigated by the fact that a user can choose to move their physical mouse faster or slower, regardless of the pointer speed setting, so that they can achieve their desired pointer speed. Therefore using the pixels-per-second as a measure of IQ might be highly accurate except in the circumstances where a user’s mouse pointer speed is set so low that moving their hands faster cannot overcome the slowness, or when the mouse pointer speed is set too high and the user cannot adjust their hand movement to slow down the mouse enough.
Improving the Dataset
The previous study I described can be used to establish whether there is any truth to a correlation pixels-per-second pointer speed rate and IQ. However, for industrial application, a different type of study is required. Instead of building a pixels-per-second to IQ database based on lab-derived data, a more accurate picture can be drawn by having subjects take IQ tests in the lab, but having them visit a web page on their home computers that records their pixels-per-second rate and sends the data back to the researchers.
In this way the real-world correlation coefficient between IQ and mouse pointer speeds can be established that accounts for the various factors that affect the measurement, such as different software and hardware set ups and different times of day.
 Jensen, Arthur R. “Why Is Reaction Time Correlated with Psychometric G?” Current Directions in Psychological Science 2, no. 2 (1993): 53-56. http://www.jstor.org/stable/20182199.
Jan 07, 2017 02:04 PM /