One of the simplest and most commonly-used tools for measuring the affective dimension of user experience is the verbal self-response survey. Unfortunately, these tools aren’t always effective because users sometimes unintentionally repress or don’t recognize their own emotions. By contrast, non-verbal tools use the concept of “affect-tagging” to capture a more intuitive, instinctive emotional response from the user about their immediate emotional state. By presenting users with animated, multimedia emotional characters instead of words, affect-tagging has proven to be better at detecting low intensity emotions, particularly those most common in an HCI context.

To assess the validity of affect-tagging as a new method for measuring user experience, Mary Frances Jones from the Georgia Tech University and the Affective Technology Research Group conducted a survey to compare verbal self-response surveys vs. non-verbal self-response surveys. Based on a review of the literature, the hypothesis was that affect-tagging could capture more clear and meaningful data, and ultimately richer insights into the user’s overall experience with an interface.

At the time of this report, some participants are still completing the survey. However, early analysis of the data suggests that, compared with traditional verbal surveys, affect-tagging shows promise for detecting the presence of negative, low intensity emotions which users are notoriously less likely to self-report in verbal surveys. This could be promising as a means of measuring user emotion with more reliability, especially when combined with other non-verbal, physiological measurement techniques such as eye tracking, facial scanning and galvanic skin sensing.

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