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EmojiGrid affective rating experiment

The EmojiGrid is an affective self-report tool consisting of a rectangular grid that is labelled with emojis. The facial expressions of the emoji labels vary from disliking (unpleasant) via neutral to liking (pleasant) along the x-axis (valence), and gradually increase in intensity along the y-axis (arousal). To report their affective appraisal of a given stimulus, users mark the location inside the grid that best represents their impression. This example experiment shows how the EmojiGrid can be used in a computer-based response paradigm.

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EmojiGrid affective rating task

In this example experiment you will see 200 pictures.

Please use the EmojiGrid to rate for each image how you think the person(s) in the image feel(s).

To respond, first place the cursor on the position inside the grid that best represents your feelings towards the image that is shown, and then click your mouse button once only.

Immediately after clicking the next image will appear.

You can stop the image presentation at any time by clicking on the red square in the lower right corner of the screen.

Creative Commons Attribution-NonCommercial (CC BY-NC)


PiSCES: Pictures with social context and emotional scenes with norms for emotional valence, intensity, and social
https://doi.org/10.3758/s13428-017-0947-x


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Preferred Citation The EmojiGrid as a tool to assess experienced and perceived emotions
https://www.mdpi.com/2624-8611/1/1/36
The EmojiGrid as a rating tool for the affective appraisal of touch
https://doi.org/10.1371/journal.pone.0237873
Affective rating of audio and video clips using the EmojiGrid
https://f1000research.com/articles/9-970
The EmojiGrid as an immersive self-report tool for the affective assessment of 360 VR videos
https://link.springer.com/chapter/10.1007/978-3-030-31908-3_24
A network model of affective odor perception
https://doi.org/10.1371/journal.pone.0236468
The relation between valence and arousal in subjective odor experience
https://doi.org/10.1007/s12078-019-09275-7
Conducted at TNO Human Factors, Soesterberg, The Netherlands
Published on 29 November 2021
Corresponding author Alexander Toet TNO Human factors