Study Shows GSR Can Predict Performance in High-Stress Environments

Employers have always known the importance of understanding how their employees will react under high-stress situations, despite the near impossibility of doing so given the highly variable and individualized nature of stress triggers and responses. However, in certain industries, knowing your employees’ responses to stress can be extremely critical.

 

For example, airlines regularly attempt to test pilots’ reactions to in-air emergencies using flight simulators but acknowledge that simulators can’t really replicate the sudden stress and fear of a real-life emergency. Similarly, no amount of military drills will ever fully replicate the shock of a sudden battle. This remains true even in industries where lives are not on the line, such as a stockbroker who may prove adept and successful in standard day-to-day operations but become unable to act in a sudden high-stress situation on the floor of the stock exchange.

 

Clearly, we all have a vested interest in knowing how people (including ourselves) respond to high-stress environments—especially since tests and drills have proven a poor indicator of actual responses. However, a recently published study from MIT demonstrates that it may be possible to accurately predict how someone will perform under high pressure using galvanic skin response (GSR), the same technology used in the ZYTO Hand Cradle.1

 

MIT galvanic skin response experiment: process and objective

 

To conduct the experiment, researchers at MIT ran a series of tests on undergraduate volunteer participants who were linked to GSR biometric devices, which have long been considered accurate tools for measuring the body’s stress-based responses.

 

In the first round of tests, participants were asked to simply answer as many multiplication questions as possible in 10 minutes. During this round, any negative feelings were kept to a minimum by not telling participants if their responses were incorrect. The test was designed to be low stress and to function as both a baseline for each participant’s GSR responses and the time it took for them to answer the questions.

 

The next round was also designed to be low stress—participants were given 20 timed questions with more than adequate time to answer each question based on their baseline time from round one. They were told that they would receive $1 for every question answered correctly.

 

The final round of testing was designed to be high stress since the time to answer the question and receive $1 was cut in half from their baseline time. A heightened sense of urgency was created by displaying how much time they had left to receive the bonus on a rapidly reducing bar graphic. Additionally, an unpleasant buzzer sound was made when they ran out of time. Participants’ stress levels were constantly tracked using the GSR device.

 

Low-stress GSR responses can predict high-stress performance

 

graph showing increasing performance GSR experiment concept

 

Researchers then charted the results of these tests to look for patterns. The patterns indicated various ways in which certain responses during the low-stress GSR readings could be used to determine which participants would remain relatively low stress during the high-stress testing.

 

Researchers admit that they do not know why people responded differently—acknowledging that the factors that cause stress in individuals are highly variable and intersectional—which makes the ability to predict how people will respond so difficult and the results of the GSR scan so valuable.

 

The MIT researchers hope that more studies and research will be done, but are confident that the results of their experiment demonstrate the value of using GSR biosensors to accurately evaluate and predict the responses of workers to “dangerous, high-risk, high-stress conditions using data from safer, low-risk, low-stress conditions.”

 

The ability to predict the best and worst performers from GSR data can be extremely useful in testing and evaluating employees. Similarly, the ability to predict how a client may respond to high-stress situations based on GSR data from ZYTO scan data may be useful to practitioners and others in the health and wellness industry also.

 

 

Sources:

1. Mundell, C., Vielma, J.P., and T. Zaman. 2010. “Predicting Performance Under Stressful Conditions Using Galvanic Skin Response.” Massachusetts Institute of Technology. (Cambridge, MA: 2016).