The second episode of the Emotion Lab is dedicated to technology and human emotion. You will hear why measuring emotions is important, how to do it, what’s the research and global community in this field and what are its potential impacts.
“Emotions are the fundamental part of the continuous cognitive appraisal and feedback loop that drives all our decision making”, explains Graeme. In order to have an impact on human behaviour, one needs to understand human emotions and motivations, which are the driver for any human decision and action. Evolutionarily speaking, emotions evolved to protect humans from danger and were part of our survival model.
Today, they help us navigate everyday life. Because emotions are so universal, more and more people are interested in understanding them and driving behavioural change. In the 21st century, personal behaviour is the root cause of the most unnecessarily and preventable mortality – obesity, heart disease, smoking, drug abuse and others. Whether it’s to do with motivating a patient to a healthy diet, helping to treat addiction, improving work performance, dealing with trauma or simply convincing a customer to purchase a product, emotions can be used to drive behavioural change.
How do you even start with something as abstract as measuring emotions?
Graeme explains that the key to understanding emotions is the study of affect – emotional displays and measurable indications of emotional state. These are biosignals that developed across human evolution. To measure them you could measure brain activity or physical cues like body language of facial expressions. Although measuring brain signals with EEG can be helpful in lab conditions, its weak signals make it less useful in real world data measurement.
Physical cues are strong indicators of emotions as humans produce physical responses to stimulus. Those cues could also be psychological changes (eg. change in heart rate or variability, sweat production, tremor). However, they cannot be interpreted without a context. Graeme explains that the key to measuring emotions is to be able to observe and measure both – the context and the stimulus and compare those against one’s baseline. Because we are all different, there’s no “one size fits model” here. Listen in to hear Graeme explaining those concepts with examples.
So how do you do it? How do you measure the stimulus, the context and the baseline?
Measuring and collecting a range of quality data in a controlled environment is a challenge that motivates Emterq’s team. Graeme explains how VR can help researchers design real world experiments, where they are able to understand moment by moment what’s happening and control every aspect of the stimulus (environment and simulations).
With a combination of sensors that measure biometrics (eg. human heart rate, eye tracking and many others), researchers can obtain a full picture of human emotions like never before. Listen in to hear more about the range of sensors that are needed and their integration as well as how AI allows for deeper understanding of such data and interpretations across broad populations.
In research, large, representative data samples are key. In the episode, Graeme and James talk about gathering the world’s largest dataset of emotional biometrics from the London Science Museum VR experiments and the impacts it may have on this field of research. Graeme also explains the key drivers for the rapid growth of interest in this field, one of which is the pandemic of mental health illness.
Although the study of psychology for a long time understood that studying our minds and emotions is crucial, in the past, there were no possibilities to make objective measurements and the field relied (and still does in many cases) on subjective self-assessments. Rapidly advancing technology is a huge opportunity to change that. Listen in to hear Graeme explain why we are living in the age of emotion and what’s ahead of us.
Emteq’s team is excited to be part of this process and their vision reflects their passion to further research on human emotion and behaviour. Our pioneering combination of hardware and software – AR/VR, sensors and ML analysed data – has already been very successful within the academic community.
