9,822 research outputs found

    The ordinal nature of emotions

    Get PDF
    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

    Affective Brain-Computer Interfaces

    Get PDF

    Autonomous Assessment of Videogame Difficulty Using Physiological Signals

    Get PDF
    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
    corecore