A Computational Model for Mood Recognition

Katsimerou, Christina; Redi, Judith; Heynderickx, Ingrid

doi:10.1007/978-3-319-08786-3_11

Cited by 11 publications

(11 citation statements)

References 22 publications

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“…In line with previous work2930, ‘mood’ was formalized as a running average of recent outcomes. We note that this implementation allows mood both to change gradually due to the aggregated effect of multiple outcomes as is considered typical for mood, or more rapidly, in response to a single highly significant outcome (as is more characteristic of emotions31).…”

Section: Resultsmentioning

confidence: 96%

Interaction between emotional state and learning underlies mood instability

2015

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Intuitively, good and bad outcomes affect our emotional state, but whether the emotional state feeds back onto the perception of outcomes remains unknown. Here, we use behaviour and functional neuroimaging of human participants to investigate this bidirectional interaction, by comparing the evaluation of slot machines played before and after an emotion-impacting wheel-of-fortune draw. Results indicate that self-reported mood instability is associated with a positive-feedback effect of emotional state on the perception of outcomes. We then use theoretical simulations to demonstrate that such positive feedback would result in mood destabilization. Taken together, our results suggest that the interaction between emotional state and learning may play a significant role in the emergence of mood instability.

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Section: Resultsmentioning

confidence: 96%

Interaction between emotional state and learning underlies mood instability

2015

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“…We report on our momentary mood to convey to others an impression of our wellbeing in everyday life 1,2 ; clinically, self reports of momentary mood form a cornerstone of psychiatric interviewing 3,4 ; in research, momentary mood is widely used to quantify human emotional responses, such as in ecological momentary assessment [5][6][7] . Moreover, theoretical accounts suggest that when we report on our mood, we integrate over the history of our experiences with the environment [8][9][10][11][12] . In this paper we address the fundamental question of the time pattern of this integration-what is the timing of events, e.g.…”

Section: Introductionmentioning

confidence: 99%

Timing matters: The temporal representation of experience in subjective mood reports

Keren

Zheng

Jangraw

et al. 2019

Preprint

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Mood is thought to integrate across our experiences, yet we do not know how the relative timing of past events shapes how we feel in the moment. Here, we investigate the relationship between the timing of previous experiences and mood by combining a novel closed-loop mood controller alongside computational modelling and neural data. We first present the development of a Mood Machine Interface which allows us to individualize rewards in real-time in order to generate substantial mood transitions, across healthy as well as depressed, adolescents and adults. We then show that early-experiences have a larger effect on mood than recent ones, and that the longer one is exposed to a given context, the harder it is for new events to change mood. We find that ACC neural activity underlies the influence of early experiences on mood. This provides a neuro-computational account of mood regulation by early events and suggests new directions for individualized mood interventions.

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“…The best obtained performance is 69% accuracy for overall mood. Another similar approach is proposed in [14]. In this study, a set of computational models is proposed to estimate mood of an affective episode, using a known sequence of emotions.…”

Section: Related Workmentioning

confidence: 97%

“…Although the two latter studies propose mood recognition approaches via non-intrusive ways, but one of the limitation with these two approaches is that the mood labels are collected via experts' annotations [14] or crowdsourcing [13]. Obviously these two approaches are actually playing the role of human judgments of each other's mood, and low obtained accuracy may be caused by this fact.…”

Section: Related Workmentioning

confidence: 97%

Determining mood via emotions observed in face by induction

Hashemian

Moradi

Mirian

et al. 2014

2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)

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Determining the mood of a person is an important step in the Human-Robot interaction. In this paper, we propose a human-inspired approach in which the changes in emotions, done using emotion induction, can be used to determine the mood of a person. The emotion induction, which can be done through robot actions or through showing video clips, stimulates changes in the emotions of the person, reducing the observation time needed to estimate mood. Consequently, the changes in the emotions, which are biased by his/her mood can be used by a robot to determine the mood of a person. To do so, we induced happy emotions by showing a comical clip and measured the intensity of each happy and sad emotions, and also the intensity of the neutral state. Then we extracted a feature set, including both time and frequency domain features, which is used to determine the mood of the person. The approach has been implemented and compared to no-emotion-induction approach and shows better results. Based on the classification results, the approach is able to distinguish between good vs. bad moods with the accuracy of 91.5% with 0.1 mean absolute error. But the neutral state was not distinguishable well.

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A Computational Model for Mood Recognition

Cited by 11 publications

References 22 publications

Interaction between emotional state and learning underlies mood instability

Interaction between emotional state and learning underlies mood instability

Timing matters: The temporal representation of experience in subjective mood reports

Determining mood via emotions observed in face by induction

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