47 research outputs found
The ordinal nature of emotions
Representing computationally everyday emotional
states is a challenging task and, arguably, one of the most fundamental
for affective computing. Standard practice in emotion annotation
is to ask humans to assign an absolute value of intensity
to each emotional behavior they observe. Psychological theories
and evidence from multiple disciplines including neuroscience,
economics and artificial intelligence, however, suggest that the
task of assigning reference-based (relative) values to subjective
notions is better aligned with the underlying representations
than assigning absolute values. Evidence also shows that we
use reference points, or else anchors, against which we evaluate
values such as the emotional state of a stimulus; suggesting
again that ordinal labels are a more suitable way to represent
emotions. This paper draws together the theoretical reasons to
favor relative over absolute labels for representing and annotating
emotion, reviewing the literature across several disciplines. We
go on to discuss good and bad practices of treating ordinal
and other forms of annotation data, and make the case for
preference learning methods as the appropriate approach for
treating ordinal labels. We finally discuss the advantages of
relative annotation with respect to both reliability and validity
through a number of case studies in affective computing, and
address common objections to the use of ordinal data. Overall,
the thesis that emotions are by nature relative is supported by
both theoretical arguments and evidence, and opens new horizons
for the way emotions are viewed, represented and analyzed
computationally.peer-reviewe
Your Gameplay Says It All: Modelling Motivation in Tom Clancy's The Division
Is it possible to predict the motivation of players just by observing their
gameplay data? Even if so, how should we measure motivation in the first place?
To address the above questions, on the one end, we collect a large dataset of
gameplay data from players of the popular game Tom Clancy's The Division. On
the other end, we ask them to report their levels of competence, autonomy,
relatedness and presence using the Ubisoft Perceived Experience Questionnaire.
After processing the survey responses in an ordinal fashion we employ
preference learning methods based on support vector machines to infer the
mapping between gameplay and the reported four motivation factors. Our key
findings suggest that gameplay features are strong predictors of player
motivation as the best obtained models reach accuracies of near certainty, from
92% up to 94% on unseen players.Comment: Version accepted for IEEE Conference on Games, 201
A study on affect model validity : nominal vs ordinal labels
The question of representing emotion computationally remains largely unanswered: popular
approaches require annotators to assign a magnitude (or a class) of some emotional
dimension, while an alternative is to focus on the relationship between two or more options.
Recent evidence in affective computing suggests that following a methodology of ordinal
annotations and processing leads to better reliability and validity of the model. This paper
compares the generality of classification methods versus preference learning methods
in predicting the levels of arousal in two widely used affective datasets. Findings of this
initial study further validate the hypothesis that approaching affect labels as ordinal data
and building models via preference learning yields models of better validity.peer-reviewe
Enhancing health care via affective computing
Affective computing is a multidisciplinary field that studies the various ways by which computational processes are able to elicit, sense, and detect manifestations of human emotion. While the methods and technology delivered by affective computing have demonstrated very promising results across several domains, their adoption by healthcare is still at its initial stages. With that aim in mind, this commentary paper introduces affective computing to the readership of the journal and praises for the benefits of affect-enabled systems for prognostic, diagnostic and therapeutic purposes.peer-reviewe