Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer
A core endeavour in current affective computing and social signal processing research is the construction of datasets embedding suitable ground truths to foster machine learning methods. This practice brings up hitherto overlooked intricacies. In this paper, we consider causal factors potentially ar...
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/6/2885 |
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| author | Alessandro D’Amelio Sabrina Patania Sathya Buršić Vittorio Cuculo Giuseppe Boccignone |
| author_facet | Alessandro D’Amelio Sabrina Patania Sathya Buršić Vittorio Cuculo Giuseppe Boccignone |
| author_sort | Alessandro D’Amelio |
| collection | DOAJ |
| description | A core endeavour in current affective computing and social signal processing research is the construction of datasets embedding suitable ground truths to foster machine learning methods. This practice brings up hitherto overlooked intricacies. In this paper, we consider causal factors potentially arising when human raters evaluate the affect fluctuations of subjects involved in dyadic interactions and subsequently categorise them in terms of social participation traits. To gauge such factors, we propose an emulator as a statistical approximation of the human rater, and we first discuss the motivations and the rationale behind the approach.The emulator is laid down in the next section as a phenomenological model where the core affect stochastic dynamics as perceived by the rater are captured through an Ornstein–Uhlenbeck process; its parameters are then exploited to infer potential causal effects in the attribution of social traits. Following that, by resorting to a publicly available dataset, the adequacy of the model is evaluated in terms of both human raters’ emulation and machine learning predictive capabilities. We then present the results, which are followed by a general discussion concerning findings and their implications, together with advantages and potential applications of the approach. |
| first_indexed | 2024-03-11T05:56:39Z |
| format | Article |
| id | doaj.art-d4ac5618e0ca4fe4a88ad04754ff5b91 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:56:39Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d4ac5618e0ca4fe4a88ad04754ff5b912023-11-17T13:42:53ZengMDPI AGSensors1424-82202023-03-01236288510.3390/s23062885Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the ObserverAlessandro D’Amelio0Sabrina Patania1Sathya Buršić2Vittorio Cuculo3Giuseppe Boccignone4PHuSe Lab, Department of Computer Science, University of Milano Statale, Via Celoria 18, 20133 Milan, ItalyPHuSe Lab, Department of Computer Science, University of Milano Statale, Via Celoria 18, 20133 Milan, ItalyPHuSe Lab, Department of Computer Science, University of Milano Statale, Via Celoria 18, 20133 Milan, ItalyPHuSe Lab, Department of Computer Science, University of Milano Statale, Via Celoria 18, 20133 Milan, ItalyPHuSe Lab, Department of Computer Science, University of Milano Statale, Via Celoria 18, 20133 Milan, ItalyA core endeavour in current affective computing and social signal processing research is the construction of datasets embedding suitable ground truths to foster machine learning methods. This practice brings up hitherto overlooked intricacies. In this paper, we consider causal factors potentially arising when human raters evaluate the affect fluctuations of subjects involved in dyadic interactions and subsequently categorise them in terms of social participation traits. To gauge such factors, we propose an emulator as a statistical approximation of the human rater, and we first discuss the motivations and the rationale behind the approach.The emulator is laid down in the next section as a phenomenological model where the core affect stochastic dynamics as perceived by the rater are captured through an Ornstein–Uhlenbeck process; its parameters are then exploited to infer potential causal effects in the attribution of social traits. Following that, by resorting to a publicly available dataset, the adequacy of the model is evaluated in terms of both human raters’ emulation and machine learning predictive capabilities. We then present the results, which are followed by a general discussion concerning findings and their implications, together with advantages and potential applications of the approach.https://www.mdpi.com/1424-8220/23/6/2885affective computingsocial perceptioncausal inferencestochastic processesBayesian inference |
| spellingShingle | Alessandro D’Amelio Sabrina Patania Sathya Buršić Vittorio Cuculo Giuseppe Boccignone Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer Sensors affective computing social perception causal inference stochastic processes Bayesian inference |
| title | Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer |
| title_full | Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer |
| title_fullStr | Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer |
| title_full_unstemmed | Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer |
| title_short | Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer |
| title_sort | inferring causal factors of core affect dynamics on social participation through the lens of the observer |
| topic | affective computing social perception causal inference stochastic processes Bayesian inference |
| url | https://www.mdpi.com/1424-8220/23/6/2885 |
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