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Abstract

Factor-analytic evidence has led most psychologists to describe affect as a set of dimensions, such as displeasure, distress, depression, excitement, and so on, with each dimension varying independently of the others. However, there is other evidence that rather than being independent, these affective dimensions are interrelated in a highly systematic fashion. The evidence suggests that these interrelationships can be represented by a spatial model in which affective concepts fall in a circle in the following order: pleasure (0), excitement (45), arousal (90), distress (135), displeasure (180), depression (225), sleepiness (270), and relaxation (315). This model was offered both as a way psychologists can represent the structure of affective experience, as assessed through self-report, and as a representation of the cognitive structure that laymen utilize in conceptualizing affect. Supportive evidence was obtained by scaling 28 emotion-denoting adjectives in 4 different ways: R. T. Ross's (1938) technique for a circular ordering of variables, a multidimensional scaling procedure based on perceived similarity among the terms, a unidimensional scaling on hypothesized pleasure–displeasure and degree-of-arousal dimensions, and a principal-components analysis of 343 Ss' self-reports of their current affective states. (70 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
... In the current experiment, we were especially interested in investigating the impact of the valence and origin of emotion on decisions concerning ambiguous stimuli. The dimension of valence refers to the pleasantness vs. unpleasantness of the stimuli [24] and is considered one of the most basic dimensions underlying emotional experiences (a crucial part of the core affect theory) [24,25], shaping its nature and, as a consequence, the attitude or behavior of an individual [26]. In our study, we decided to distinguish three levels of valence: negative, neutral and positive [27]. ...
... In the current experiment, we were especially interested in investigating the impact of the valence and origin of emotion on decisions concerning ambiguous stimuli. The dimension of valence refers to the pleasantness vs. unpleasantness of the stimuli [24] and is considered one of the most basic dimensions underlying emotional experiences (a crucial part of the core affect theory) [24,25], shaping its nature and, as a consequence, the attitude or behavior of an individual [26]. In our study, we decided to distinguish three levels of valence: negative, neutral and positive [27]. ...
... It shows that the distinctiveness of the origin of words may be successfully observed in this stage in the early perception of the stimulus. This is especially interesting when we contrast this result with the fact that we did not obtain any result of valence, contrary to previous studies [46], and to the general definition of valence being the most basic dimension [24]. However, as in our study's design, we crossed the levels of valence and origin and only obtained results of origin. ...
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Warmth and competence are fundamental dimensions of social cognition. This also applies to the interpretation of ambiguous symbolic stimuli in terms of their relation to warmth or competence. The affective state of an individual may affect the way people interpret the neutral stimuli in the environment. As previous findings have shown, it is possible to alter the perception of neutral social stimuli in terms of warmth vs. competence by eliciting an incidental affect with the use of emotion-laden words. In the current experiment, we expected the valence and origin of an affective state, factors ascribing emotionally laden words, to be able to switch the interpretation of the neutral objects. We have shown in behavioural results that negative valence and reflective origins promote the interpretation of unknown objects in terms of competence rather than warmth. Furthermore, electrophysiological-response-locked analyses revealed differences specific to negative valence while making the decision in the ambiguous task and while executing it. The results of the current experiment show that the usage of warmth and competence in social cognition is susceptible to affective state manipulation. In addition, the results are coherent with the evolutionary perspective on social cognition (valence effects) as well as with predictions of the dual mind model of emotion (origin effects).
... Different two-dimensional models of mood have been proposed, of which the four models depicted in Fig. 1 are the most influential (Yik, Russell, & Feldman Barrett, 1999 The poles of the bipolar axes differ only slightly between the models. In Russell's (1980) model, for instance, "depression" is the opposite pole of "excitement," whereas states similar to depression (misery, unpleasant mood, tense-tiredness) are considered the opposite pole of pleasant mood in the other models. Moreover, "sleep" is the opposite pole of "arousal" ...
... in Russell's (1980) model, whereas "sleepy" is an indicator of the pleasant deactivation pole in Thayer's (1996) and Watson and Tellegen's (1985) models. ...
... Models shown are based on those presented by Russell (1980Russell ( , p. 1164, Watson and Tellegen (1985, p. 221), Larsen andDiener (1992, p. 39), andThayer (1996, p. 150). Here, the dimensions are rotated in order to highlight the similarity among the four models. ...
... Categorical emotions are based on the six basic emotions proposed by Ekman: happiness, sadness, fear, anger, disgust, and surprise [22]. Dimensional approaches mainly use two dimensions: valence (pleasure) and arousal which are based on Russell's circumplex model of emotions [42]. Valence refers to how negative to positive a person feels and arousal refers to how sleepy to active a person feels. ...
... Valence refers to how negative to positive a person feels and arousal refers to how sleepy to active a person feels. Using these two dimensions, several categorical emotions can be placed and grouped into the four quadrants: high arousal and negative valence (e.g., angry), low arousal and negative valence (e.g., depressed), low arousal and positive valence (e.g., relaxed), and high arousal and positive valence (e.g., excited) [42]. ...
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Couples generally manage chronic diseases together and the management takes an emotional toll on both patients and their romantic partners. Consequently, recognizing the emotions of each partner in daily life could provide an insight into their emotional well-being in chronic disease management. Currently, the process of assessing each partner's emotions is manual, time-intensive, and costly. Despite the existence of works on emotion recognition among couples, none of these works have used data collected from couples' interactions in daily life. In this work, we collected 85 hours (1,021 5-minute samples) of real-world multimodal smartwatch sensor data (speech, heart rate, accelerometer, and gyroscope) and self-reported emotion data (n=612) from 26 partners (13 couples) managing diabetes mellitus type 2 in daily life. We extracted physiological, movement, acoustic, and linguistic features, and trained machine learning models (support vector machine and random forest) to recognize each partner's self-reported emotions (valence and arousal). Our results from the best models were better than chance with balanced accuracies of 63.8% and 78.1% for arousal and valence respectively. This work contributes toward building automated emotion recognition systems that would eventually enable partners to monitor their emotions in daily life and enable the delivery of interventions to improve their emotional well-being.
... In this paper, we employ a simulation to reality (Sim2Real) Fig. 1: Circumplex Model of Affect adapted from [29]. ...
... 1) Collection of in-the-wild confusion, anger, and disgust videos: Confusion is an affective state conveyed through varied and multiple expressions, as are both anger and disgust. Some of these expressions (such as frowning) are common between the three, resulting in some expressions being easily mistaken as another emotion, possibly due to these three emotions appearing very close together in the CMA [29]. We therefore focus on making a dataset for these three emotions. ...
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Robots and artificial agents that interact with humans should be able to do so without bias and inequity, but facial perception systems have notoriously been found to work more poorly for certain groups of people than others. In our work, we aim to build a system that can perceive humans in a more transparent and inclusive manner. Specifically, we focus on dynamic expressions on the human face, which are difficult to collect for a broad set of people due to privacy concerns and the fact that faces are inherently identifiable. Furthermore, datasets collected from the Internet are not necessarily representative of the general population. We address this problem by offering a Sim2Real approach in which we use a suite of 3D simulated human models that enables us to create an auditable synthetic dataset covering 1) underrepresented facial expressions, outside of the six basic emotions, such as confusion; 2) ethnic or gender minority groups; and 3) a wide range of viewing angles that a robot may encounter a human in the real world. By augmenting a small dynamic emotional expression dataset containing 123 samples with a synthetic dataset containing 4536 samples, we achieved an improvement in accuracy of 15% on our own dataset and 11% on an external benchmark dataset, compared to the performance of the same model architecture without synthetic training data. We also show that this additional step improves accuracy specifically for racial minorities when the architecture's feature extraction weights are trained from scratch.
... There is greater diversity in how the second general dimension of affect has been understood. For Russell (1980) the bi-polar dimensions are Arousal-Sleep and Misery-Pleasure; for Larsen and Diener (1992) they are High Activation-Low Activation and Unpleasant-Pleasant, while for Thayer (1989) these reflect Energy-Tiredness and Tension-Calmness. The first dimension in each case has clear conceptual overlap with sleep loss and reduced arousal. ...
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Laboratory-based sleep manipulations show asymmetries between positive and negative affect, but say little about how more specific moods might change. We report extensive analyzes of items from the Positive and Negative Affect Scale (PANAS) during days following nights of chronic sleep restriction (6 h sleep opportunity), during 40 h of acute sleep deprivation under constant routine conditions, and during a week-long forced desynchrony protocol in which participants lived on a 28-h day. Living in the laboratory resulted in medium effects sizes on all positive moods (Attentiveness, General Positive Affect, Joviality, Assuredness), with a general deterioration as the days wore on. These effects were not found with negative moods. Sleep restriction reduced some positive moods, particularly Attentiveness (also General Positive), and increased Hostility. A burden of chronic sleep loss also led to lower positive moods when participants confronted the acute sleep loss challenge, and all positive moods, as well as Fearfulness, General Negative Affect and Hostility were affected. Sleeping at atypical circadian phases resulted in mood changes: all positive moods reduced, Hostility and General Negative Affect increased. Deteriorations increased the further participants slept from their typical nocturnal sleep. In most cases the changes induced by chronic or acute sleep loss or mistimed sleep waxed or waned across the waking day, with linear or various non-linear trends best fitting these time-awake-based changes. While extended laboratory stays do not emulate the fluctuating emotional demands of everyday living, these findings demonstrate that even in controlled settings mood changes systematically as sleep is shortened or mistimed.
... The estimations of the SLAM were used as input in an appraisal model to perform the Stimulus Evaluation Checks (SECs) that were independent modules in which the agent did its subjective assessment of the situation based on personal needs, goals, and values. Then, a categorization module was used to map the pleasure and arousal space defined by Russell [33]. ...
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