MoodCoach

CONTACT: Jane Luht, Head of Technology Transfer, Phone: +372 737 4804, E-mail: jane.luht@ut.ee


A straightforward and powerful method for emotion mapping in human performance technology.

Human performance is driven by emotions. In sport and in general performance psychology, there is a consensus about the relationships between performer’s emotional state and performance outcome: in order to deliver an optimal performance, we need to be in an optimal zone.

The Plutchik model for classification of emotions.

However, this relationship is not a linear one and it can even not be characterized by any nonlinear curve – the relationship between emotions and performance is individually unique: some people perform better while being relaxed and easygoing, others need to be psyched up and feel on the edge etc. Thus, different people need to obtain different state for delivering their optimal mental and physical performance. Psychological training is used to teach people how to regulate their emotions to achieve the optimal zone or the flow state. Importantly, in order to do that, the performer first needs to know “where” his/her optimal emotional zone is.


The huge complexity of human emotional experiences as represented in Fig 1 can be reduced on two bipolar dimensions: pleasantness and activation. Indeed, the performer can report his/her emotional state on a single-item scale called the Affect Grid, Fig.2. We have advanced to original Affect Grid and assigned specific color values to each value of the Affect Grid combining RGB (red, green and blue additive color model) values. By doing that we have standardized the procedure of emotional mapping and assigned a unique value to each combination of activation and pleasantness ratings. This advancement makes the representation of specific emotional states more straightforward. Thus, we have generated a simple emotional map for assessment of emotional states and training for emotion regulation in human performance technology.


In summary, MoodCoach is a 2D scale designed as a quick means of assessing emotional state along the dimensions of pleasantness and arousal. By using statistics of ordinal regression, it can yield the probabilities of achieving specific levels of performance (e.g. optimal, moderate, poor) at different levels of pleasantness (Fig. 3) and arousal (Fig.4). As an outcome, MoodCoach delivers a 2D representation of the optimal performance zone as illustrated in Fig. 5. The performer can use this information for training to achieve his/her optimal zone.

Figure. 2. Affect Grid (Russell, 1979) for representing 81 emotional states along the scales of (x) pleasure-displeasure and (y) arousal-sleepiness. Performer marks his/her current emotional state by making a cross on the specific cell. This is a quick and easy way to precisely describe the current emotional state.
Figure. 3. Example of probabilities of delivering of a specific performance level at different levels of arousal.

Figure. 4. Example of probabilities of delivering of a specific performance level at different levels of pleasantness

Figure. 5. MoodCoach output representing the optimal performance zone based on the example of Figure 3 and 4.

Software prototype for Android platform is available at the University of Tartu.