Basic Affective States Research Articles

Basic Affective Systems and Sex Differences in the Relationship between Anger and Fear.

The possible interactions between anger and fear have not been widely explored in the psychological literature. Fear and anger are currently beginning to be studied by looking at their interrelationships, rather than seeing them as simply opposing emotions. Furthermore, there is a tendency to think that anger is more typical of men and fear of women. Our contribution proposes a particular perspective of affective neuroscience. The objectives of the study are as follows: (1) to assess possible differences in affective systems, and states and traits of anger in relation to biological sex; (2) to assess correlations between ANGER, FEAR, and SADNESS, as well as state and trait anger in both a female and male sample; (3) to assess possible differences in basic affective systems in relation to different levels of ANGER, FEAR, and SADNESS, state and trait anger, in female and male samples. A non-clinical sample of 339 females and 99 males completed the ANPS 3.1 to assess basic affective states and the STAXI-2 to assess anger states and traits. No significant differences were found for ANGER and FEAR scores and for state and trait scores between the male and female samples. Clear correlations emerged (p < 0.01) between SADNESS and FEAR in both the female and male samples. Among the differences that emerged in the affective systems, we emphasise that in the female group, the highest scores on the SEEKING and PLAY scales are expressed by the group of women who have the lowest scores in FEAR; PLAY and CARE also vary in relation to different scores in SADNESS. Given the importance of the SEEKING and PLAY variables, it is of paramount importance to monitor the environmental and relational situations to guarantee that women, too, are provided with the conditions of safety and protection that are prerequisites for their well-being and the positive expression of their resources.

Affective states and nonsuicidal self-injury (NSSI): Results from an ecological momentary assessment study of veterans with NSSI disorder.

The affective states most strongly associated with nonsuicidal self-injury (NSSI) remain poorly understood, particularly among veterans. This study used ecological momentary assessment (EMA) to examine relationships between affect ratings and NSSI urges and behaviors among veterans with NSSI disorder. Participants (N=40) completed EMA entries via mobile phone for 28days (3722 total entries). Entries included intensity ratings for five basic affective states, as well as NSSI urges and behaviors, during the past 4hours. Bivariate analyses indicated that each affect variable was significantly associated with both NSSI urges and behaviors. Angry/hostile and sad were most strongly associated with both NSSI urges and behaviors. A multivariate regression revealed that angry/hostile, disgusted with self, and happy (inversely related) were contemporaneously (within the same period) associated with NSSI behaviors, whereas all five basic affective states were contemporaneously associated with NSSI urges. In a lagged model, angry/hostile and sad were associated with subsequent NSSI urges but not behaviors. Findings highlight the relevance of particular affective states to NSSI and the potential utility of targeting anger in treatments for NSSI among veterans. There is a need for future EMA research study to further investigate temporal relationships between these variables.

Affective Neuroscience: The Suitability of a Web App to Monitor Affective States at Work.

This work describes in detail the use of a new tool, a web-app, based on the conceptual framework of affective neuroscience, in particular on Panksepp’s 7 basic emotional systems. Affective neuroscience has been used effectively in many areas, but there have been very few applications in the workplace, due to the lack of a smart implementation tool. The novelty of this work does not lie in the new information, but in a new “clinical” approach. There is a theoretical framework that allows data to be interpreted rather than simply described. Furthermore, the knowledge of working realities through the web app is specific and longitudinal. Finally, emotions are detected in hic et nunc, so the role of reflexive-cognitive mediation and recall bias are minor. This “more situated” knowledge can then guide specific leadership strategies. This paper presents the results of the tool’s application in a company in Northern Italy. The findings of our project, which recorded basic affective states and the functioning of several working teams, are detailed herein. The project’s 488 web-app records are summarized in this report, alongside our examination of related mood tags. Through this project, our analysis has enabled to determine affective neuroscience profiles of the teams analyzed, allowing the researchers to identify areas of possible interventions. The data appear very encouraging.

Implementation of Wavelet Analysis on Thermal Images for Affective States Recognition of Children With Autism Spectrum Disorder

Children with Autism Spectrum Disorder are identified as a group of people who has difficulties in socio-emotional interaction. Most of them lack the proper context in producing social response through facial expression and speech. Since emotion is the key for effective social interaction, it is justifiably vital for them to comprehend the correct emotion expressions and recognitions. Emotion is a type of affective states and can be detected through physical reaction and physiological signals. In general, recognition of affective states from physical reaction such as facial expression and speech for autistic children is often unpredictable. Hence, an alternative method of identifying the affective states through physiological signals is proposed. Though considered non-invasive, most of the current recognition methods require sensors to be patched on to the skin body to measure the signals. This would most likely cause discomfort to the children and mask their “true” affective states. The study proposed the use of thermal imaging modality as a passive medium to analyze the physiological signals associated with the affective states nonobtrusively. The study hypothesized that, the impact of cutaneous temperature changes due to the pulsating blood flow in the blood vessels at the frontal face area measured from the modality could have a direct impact to the different affective states of autistic children. A structured experimental setup was designed to measure thermal imaging data generated from different affective state expressions induced using different sets of audio-video stimuli. A wavelet-based technique for pattern detection in time series was deployed to spot the changes measured from the region of interest. In the study, the affective state model for typical developing children aged between 5 and 9 years old was used as the baseline to evaluate the performance of the affective state classifier for autistic children. The results from the classifier showed the efficacy of the technique and accorded good performance of classification accuracy at 88% in identifying the affective states of autistic children. The results were momentous in distinguishing basic affective states and the information could provide a more effective response towards improving social-emotion interaction amongst the autistic children.

Mean of Correlation Method for Optimization of Affective States Detection in Children

At the moment, most of the studies on classification of affective states for children focus on visual observations and physiological cues, where all data collection for measuring physiological signals are contact-based and invasive. With the requirement of having the measuring device attached to the body approach, distraction of the subject normally masks the true affective states of the subject due to discomfort. In this paper, a non-invasive, contactless, and less distraction method is proposed to measure the physiological cues of the subjects using their thermal imprints from frontal face imaging. A thermal image camera is used to identify basic affective states, where it is a contactless and seamless device with ability to read the radiated thermal imprint of the subjects’ facial skin temperature. This paper proposes an effective algorithm of texture analysis based on novel technique using Gray Level Co-occurrence Matrix approach to be applied so as to identify blood-flow region. The cues from the first order statistics are computed in the identified blood flow region and concatenated along with second order statistics cues, in order to construct feature vectors to administer the vital and distinguishable characteristic pattern between affective states in thermal images. Result from the fine k-NN classifier obtained promises the efficacy of the proposed approach to be applied in our future work in human–robot interaction for autistic children learning and training.

Heterogeneity of long-history migration explains cultural differences in reports of emotional expressivity and the functions of smiles

A small number of facial expressions may be universal in that they are produced by the same basic affective states and recognized as such throughout the world. However, other aspects of emotionally expressive behavior vary widely across culture. Just why do they vary? We propose that some cultural differences in expressive behavior are determined by historical heterogeneity, or the extent to which a country's present-day population descended from migration from numerous vs. few source countries over a period of 500 y. Our reanalysis of data on cultural rules for displaying emotion from 32 countries [n = 5,340; Matsumoto D, Yoo S, Fontaine J (2008) J Cross Cult Psychol 39(1):55-74] reveals that historical heterogeneity explains substantial, unique variance in the degree to which individuals believe that emotions should be openly expressed. We also report an original study of the underlying states that people believe are signified by a smile. Cluster analysis applied to data from nine countries (n = 726), including Canada, France, Germany, India, Indonesia, Israel, Japan, New Zealand, and the United States, reveals that countries group into "cultures of smiling" determined by historical heterogeneity. Factor analysis shows that smiles sort into three social-functional subtypes: pleasure, affiliative, and dominance. The relative importance of these smile subtypes varies as a function of historical heterogeneity. These findings thus highlight the power of social-historical factors to explain cross-cultural variation in emotional expression and smile behavior.

Audio-Visual Affect Recognition

The ability of a computer to detect and appropriately respond to changes in a user's affective state has significant implications to human-computer interaction (HCI). In this paper, we present our efforts toward audio-visual affect recognition on 11 affective states customized for HCI application (four cognitive/motivational and seven basic affective states) of 20 nonactor subjects. A smoothing method is proposed to reduce the detrimental influence of speech on facial expression recognition. The feature selection analysis shows that subjects are prone to use brow movement in face, pitch and energy in prosody to express their affects while speaking. For person-dependent recognition, we apply the voting method to combine the frame-based classification results from both audio and visual channels. The result shows 7.5% improvement over the best unimodal performance. For person-independent test, we apply multistream HMM to combine the information from multiple component streams. This test shows 6.1% improvement over the best component performance