A New Age for Automated Consumer Neuroscience: Bringing Credibility, Speed and Scale to the Picture

Pedro Chaves | December 15, 2017
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The end of the year calendar has been particularly full at MindProber – after our trip to Mexico to present our case study, it was time to head to Chicago to participate in the IIeX Behavior event.

In a conference full of great content dedicated to the measurement of behavior in its multiple dimensions, we have decided on presenting a bit of the results of our latest research endeavor: a comprehensive meta-analysis on the use of biometrics data to measure emotional states and how MindProber is thriving to bring scale, speed, and credibility to the consumer neuroscience space.

We knew it would be risky to have a particularly technical talk in a Market Research event, but the overall experience was quite positive and we managed to collect important compliments and insights on how to effectively communicate science.

But on to the data: although the use of biometrics has been widespread both at the fundamental emotion research level and in the industry, no systematic analysis of its use has been conducted until now. We have decided to tackle this problem and do somewhat of a double contribution: to publish hardcore science with relevant Psychological and Psychophysiological findings and at the same time contribute to an increased awareness and seriousness of the Consumer Neuroscience practice.

From an initial screening of over 1000 papers, following a strict meta-analytical approach, combining the results from every paper from 1970 onwards which included biometrics recordings and stimulation with emotional content (focusing solely on video), we aimed at answering a simple yet profound set of questions:

What are the most common biometric signals in emotion research?

What are the features of the signals most commonly researched and with the highest discriminative power?

And finally, following from the above, is there any evidence coming from psychophysiology for the so-called categorical emotional models of emotion? That is, are we able to distinguish from different emotional “states” as described by the classical basic emotions models, such as anger, sadness or happiness?

The results seem clear: Galvanic Skin Response and Heart Activity are by far the most widely used methods in research, being prime examples of indexes of the activity of the Peripheral Nervous System.

 

                   Galvanic Skin Response image

                   Heart Rate image

Now, from an emotional standpoint, what can these data tell us? When looking at the combined effects from the literature, it’s apparent that we are out of luck if we approach biometrics from a categorical approach – that is, the changes induced by the stimuli are not sufficiently different from one emotional category to another. However, it seems that these same metrics are extremely useful to disentangle different levels of emotional arousal (i.e., how intense a given experience is, rather than how pleasant). And within the metrics battle, GSR comes out as a clear winner!

This, of course, has a significant impact on how we conceive emotional states and how these seem to be represented in the body, although we’ll leave that discussion to the paper itself (which you’ll be able to read from a top Psychology\Neuroscience journal somewhere in 2018)!

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Pedro Chaves
Pedro Chaves


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