The World of Emotions is not Two-Dimensional

Fontaine, Johnny; Scherer, Klaus R.; Roesch, Etienne B.; Ellsworth, Phoebe C.

doi:10.1111/j.1467-9280.2007.02024.x

Cited by 937 publications

(740 citation statements)

References 15 publications

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“…[112] Only a few studies analyzed the accuracy of emotion recognition on other dimensions such as control, dominance, novelty, or predictability. [113] Focusing on valence and arousal, Soleymani et al [105] argue that the arousal dimension is better discriminated by brain activity than the valence dimension. When looking at the studies which analyzed both the classification of valence and arousal on two classes [105,106,110,114,115] the valence accuracy is only marginally higher than the arousal accuracy (valence mean accuracy is 65.6%, arousal mean accuracy is 68.2%), and it is difficult to conclude any potential advantage of neurophysiological signals for arousal assessment.…”

Section: Assessed Emotionsmentioning

confidence: 99%

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Mühl

Allison

Nijholt

et al. 2014

Brain-Computer Interfaces

234

159

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Affective states, moods and emotions, are an integral part of human nature: they shape our thoughts, govern the behavior of the individual, and influence our interpersonal relationships. The last decades have seen a growing interest in the automatic detection of such states from voice, facial expression, and physiological signals, primarily with the goal of enhancing human-computer interaction with an affective component. With the advent of brain-computer interface research, the idea of affective brain-computer interfaces (aBCI), enabling affect detection from brain signals, arose. In this article, we set out to survey the field of neurophysiology-based affect detection. We outline possible applications of aBCI in a general taxonomy of brain-computer interface approaches and introduce the core concepts of affect and their neurophysiological fundamentals. We show that there is a growing body of literature that evidences the capabilities, but also the limitations and challenges of affect detection from neurophysiological activity

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Section: Assessed Emotionsmentioning

confidence: 99%

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Mühl

Allison

Nijholt

et al. 2014

Brain-Computer Interfaces

234

159

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“…Surprised faces signal the presence of a novel object but remain ambiguous regarding its valence. In the real world, surprise is transitory and is followed by another emotion (Fontaine et al, 2007). Given that this subsequent emotion is uncertain, it could be advantageous for the viewer to orient faster toward the object eliciting surprise in order to determine whether it is dangerous.…”

Section: Goe Modulation By Emotionsmentioning

confidence: 99%

“…Fearful and surprised facial expressions share many facial features including eye widening (Gosselin & Simard, 1999), which contributes to their facilitation of gaze-oriented attention (Bayless et al, 2011). In addition, surprise's valence is ambiguous (Fontaine, Scherer, Roesch, & Ellsworth, 2007) but is interpreted negatively in the context of negative emotions such as fearful faces (Neta & Whalen, 2010). Finally, surprise signals the presence of an unexpected event, which could prompt faster orienting to determine whether it is a danger.…”

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confidence: 99%

Fearful, surprised, happy, and angry facial expressions modulate gaze-oriented attention: Behavioral and ERP evidence

Lassalle

Itier

2013

Social Neuroscience

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The impact of emotions on gaze-oriented attention was investigated in non-anxious participants. A neutral face cue with straight gaze was presented, which then averted its gaze to the side while remaining neutral or expressing an emotion (fear/surprise in Exp.1 and anger/happiness in Exp.2). Localization of a subsequent target was faster at the gazed-at location (congruent condition) than at the non-gazed-at location (incongruent condition). This Gaze-Orienting Effect (GOE) was enhanced for fear, surprise, and anger, compared to neutral expressions which did not differ from happy expressions. In addition, Event Related Potentials (ERPs) to the target showed a congruency effect on P1 for fear and surprise and a left lateralized congruency effect on P1 for happy faces, suggesting that target visual processing was also influenced by attention to gaze and emotions. Finally, at cue presentation, early postero-lateral (Early Directing Attention Negativity (EDAN)) and later antero-lateral (Anterior Directing Attention Negativity (ADAN)) attentionrelated ERP components were observed, reflecting, respectively, the shift of attention and its holding at gazed-at locations. These two components were not modulated by emotions. Together, these findings show that the processing of social signals such as gaze and facial expression interact rather late and in a complex manner to modulate spatial attention. KeywordsGaze orienting; Attention; ERPs; Emotions Gaze direction is a crucial non-verbal cue, which we use to determine where and what others are attending. It can also direct another's attention toward an object, a phenomenon called joint attention, which helps assess others' intentions and understand their behaviors (BaronCohen, 1995).Joint attention is typically studied using a modified version of the Posner cuing paradigm (Posner, 1980), in which a central face cue with averted gaze is followed by a lateral target. Congruent trials in which the target appears at the gazed-at location are responded to faster than incongruent trials in which the target appears at the opposite side of gaze. The response time difference between congruent and incongruent trials reflects the orienting of attention toward gaze direction (Friesen & Kingstone, 1998). This robust Gaze Orienting Effect (GOE) was shown for letter discrimination, target detection, or localization tasks, for Stimulus Onset Asynchronies (SOAs) up to 700 ms, and when the cue is non-predictive or even counter-predictive of the target location (for a review, see Frischen, Bayliss, & Tipper, 2007 Many studies have investigated whether emotions modulate attention orienting by gaze. A GOE increase with fearful compared to neutral and/or happy faces has been reported and interpreted as reflecting the evolutionary advantage to orient rapidly in the direction of a potential threat (Bayless, Glover, Taylor, & Itier, 2011;Fox, Mathews, Calder, & Yiend, 2007;Graham, Friesen, Fichtenholtz, & LaBar, 2010;Mathews, Fox, Yiend, & Calder, 2003;Pecchinenda, Pes, Ferlazzo, & Zoccolo...

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“…Examples of such affective dimensions are power (sense of control), valence (pleasant vs. unpleasant), activation (relaxed vs. aroused), and expectancy (anticipation). Fontaine et al [6] argue that these four dimensions account for most of the distinctions between everyday emotion categories, and hence form a good set to analyse.…”

Section: Introductionmentioning

confidence: 99%

Modeling Latent Discriminative Dynamic of Multi-dimensional Affective Signals

Ramirez

Baltrušaitis

Morency

2011

Affective Computing and Intelligent Interaction

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Abstract. During face-to-face communication, people continuously exchange para-linguistic information such as their emotional state through facial expressions, posture shifts, gaze patterns and prosody. These affective signals are subtle and complex. In this paper, we propose to explicitly model the interaction between the high level perceptual features using Latent-Dynamic Conditional Random Fields. This approach has the advantage of explicitly learning the sub-structure of the affective signals as well as the extrinsic dynamic between emotional labels. We evaluate our approach on the Audio-Visual Emotion Challenge (AVEC 2011) dataset. By using visual features easily computable using off-theshelf sensing software (vertical and horizontal eye gaze, head tilt and smile intensity), we show that our approach based on LDCRF model outperforms previously published baselines for all four affective dimensions. By integrating audio features, our approach also outperforms the audio-visual baseline.

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The World of Emotions is not Two-Dimensional

Cited by 937 publications

References 15 publications

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Fearful, surprised, happy, and angry facial expressions modulate gaze-oriented attention: Behavioral and ERP evidence

Modeling Latent Discriminative Dynamic of Multi-dimensional Affective Signals

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