9,822 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
Autonomous Assessment of Videogame Difficulty Using Physiological Signals
Given the well-explored relation between challenge and involvement in a task, (e.g.,
as described in Csikszentmihalyi’s theory of flow), it could be argued that the presence
of challenge in videogames is a core element that shapes player experiences and should,
therefore, be matched to the player’s skills and attitude towards the game. However,
handling videogame difficulty, is a challenging problem in game design, as too easy a
task can lead to boredom and too hard can lead to frustration. Thus, by exploring the
relationship between difficulty and emotion, the current work intends to propose an
artificial intelligence model that autonomously predicts difficulty according to the set
of emotions elicited in the player. To test the validity of this approach, we developed
a simple puzzle-based Virtual Reality (VR) videogame, based on the Trail Making Test
(TMT), and whose objective was to elicit different emotions according to three levels of
difficulty. A study was carried out in which physiological responses as well as player self-
reports were collected during gameplay. Statistical analysis of the self-reports showed
that different levels of experience with either VR or videogames didn’t have a measurable
impact on how players performed during the three levels. Additionally, the self-assessed
emotional ratings indicated that playing the game at different difficulty levels gave rise to
different emotional states. Next, classification using a Support Vector Machine (SVM) was
performed to verify if it was possible to detect difficulty considering the physiological
responses associated with the elicited emotions. Results report an overall F1-score of 68%
in detecting the three levels of difficulty, which verifies the effectiveness of the adopted
methodology and encourages further research with a larger dataset.Dada a relação bem explorada entre desafio e envolvimento numa tarefa (p. ex., con-
forme descrito na teoria do fluxo de Csikszentmihalyi), pode-se argumentar que a pre-
sença de desafio em videojogos é um elemento central que molda a experiência do jogador
e deve, portanto, ser compatível com as habilidades e a atitude que jogador exibe perante
o jogo. No entanto, saber como lidar com a dificuldade de um videojogo é um problema
desafiante no design de jogos, pois uma tarefa muito fácil pode gerar tédio e muito di-
fícil pode levar à frustração. Assim, ao explorar a relação entre dificuldade e emoção,
o presente trabalho pretende propor um modelo de inteligência artificial que preveja
de forma autônoma a dificuldade de acordo com o conjunto de emoções elicitadas no
jogador. Para testar a validade desta abordagem, desenvolveu-se um jogo de puzzle em
Realidade Virtual (RV), baseado no Trail Making Test (TMT), e cujo objetivo era elicitar
diferentes emoções tendo em conta três níveis de dificuldade. Foi realizado um estudo no
qual se recolheram as respostas fisiológicas, juntamente com os autorrelatos dos jogado-
res, durante o jogo. A análise estatística dos autorelatos mostrou que diferentes níveis de
experiência com RV ou videojogos não tiveram um impacto mensurável no desempenho
dos jogadores durante os três níveis. Além disso, as respostas emocionais auto-avaliadas
indicaram que jogar o jogo em diferentes níveis de dificuldade deu origem a diferentes
estados emocionais. Em seguida, foi realizada a classificação por intermédio de uma Má-
quina de Vetores de Suporte (SVM) para verificar se era possível detectar dificuldade,
considerando as respostas fisiológicas associadas às emoções elicitadas. Os resultados re-
latam um F1-score geral de 68% na detecção dos três níveis de dificuldade, o que verifica
a eficácia da metodologia adotada e incentiva novas pesquisas com um conjunto de dados
maior
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