The ability to automatically infer relevant aspects of human users' thoughts and feelings is crucial for technologies to intelligently adapt their behaviors in complex interactions. Research on multimodal analysis has demonstrated the potential of technology to provide such estimates for a broad range of internal states and processes. However, constructing robust approaches for deployment in real-world applications remains an open problem. The MSECP-Wild workshop series is a multidisciplinary forum to present and discuss research addressing this challenge. Submissions to this 5th iteration span efforts relevant to multimodal data collection, modeling, and applications. In addition, our workshop program builds on discussions emerging in previous iterations, highlighting ethical considerations when building and deploying technology modeling internal states in the wild. For this purpose, we host a range of relevant keynote speakers and interactive activities.

@en

In this article we introduce Mementos: the first multimodal corpus for computational modeling of affect and memory processing in response to video content. It was collected online via crowdsourcing and captures 1995 individual responses collected from 297 unique viewers responding to 42 different segments of music videos. Apart from webcam recordings of their upper-body behavior (totaling 2012 minutes) and self-reports of their emotional experience, it contains detailed descriptions of the occurrence and content of 989 personal memories triggered by the video content. Finally, the dataset includes self-report measures related to individual differences in participants' background and situation (Demographics, Personality, and Mood), thereby facilitating the exploration of important contextual factors in research using the dataset. We describe 1) the construction and contents of the corpus itself, 2) analyse the validity of its content by investigating biases and consistency with existing research on affect and memory processing, 3) review previously published work that demonstrates the usefulness of the multimodal data in the corpus for research on automated detection and prediction tasks, and 4) provide suggestions for how the dataset can be used in future research on modeling Video-Induced Emotions, Memory-Associated Affect, and Memory Evocation.

@en

Intelligent systems might benefit from automatically detecting when a stimulus has triggered a user's recollection of personal memories, e.g., to identify that a piece of media content holds personal significance for them. While computational research has demonstrated the potential to identify related states based on facial behavior (e.g., mind-wandering), the automatic detection of spontaneous recollections specifically has not been investigated this far. Motivated by this, we present machine learning experiments exploring the feasibility of detecting whether a video clip has triggered personal memories in a viewer based on the analysis of their Head Rotation, Head Position, Eye Gaze, and Facial Expressions. Concretely, we introduce an approach for automatic detection and evaluate its potential for predictions using in-the-wild webcam recordings. Overall, our findings demonstrate the capacity for above chance detections in both settings, with substantially better performance for the video-independent variant. Beyond this, we investigate the role of person-specific recollection biases for predictions of our video-independent models and the importance of specific modalities of facial behavior. Finally, we discuss the implications of our findings for detecting recollections and user-modeling in adaptive systems.

@en

The ability to automatically infer relevant aspects of human users' thoughts and feelings is crucial for technologies to adapt their behaviors in complex interactions intelligently (e.g., social robots or tutoring systems). Research on multimodal analysis has demonstrated the potential of technology to provide such estimates for a broad range of internal states and processes. However, constructing robust enough approaches for deployment in real-world applications remains an open problem. The MSECP-Wild workshop series serves as a multidisciplinary forum to present and discuss research addressing this challenge. This 4th iteration focuses on addressing varying contextual conditions (e.g., throughout an interaction or across different situations and environments) in intelligent systems as a crucial barrier for more valid real-world predictions and actions. Submissions to the workshop span efforts relevant to multimodal data collection and context-sensitive modeling. These works provide important impulses for discussions of the state-of-the-art and opportunities for future research on these subjects.

@en

In competitive multiplayer online video games, teamwork is of utmost importance, implying high levels of interdependence between the joint outcomes of players. When engaging in such interdependent interactions, humans rely on trust to facilitate coordination of their individual behaviours. However, online games often take place between teams of strangers, with individual members having little to no information about each other than what they observe throughout the interaction itself. A better understanding of the social behaviours that are used by players to form trust could not only facilitate richer gaming experiences, but could also lead to insights about team interactions. As such, this paper presents a first step towards understanding how and which types of in-game behaviour relate to trust formation. In particular, we investigate a) which in-game behaviour were relevant for trust formation (first part of the study) and b) how they relate to the reported player's trust in their teammates (the second part of the study). The first part consisted of interviews with League of Legends players in order to create a taxonomy of in-game behaviours relevant for trust formation. As for the second part, we ran a small-scale pilot study where participants played the game and then answered a questionnaire to measure their trust in their teammates. Our preliminary results present a taxonomy of in-game behaviours which can be used to annotate the games regarding trust behaviours. Based on the pilot study, the list of behaviours could be extended as to improve the results. These findings can be used to research the role of trust formation in teamwork.

@en

Enabling computer-based applications to display intelligent behavior in complex social settings requires them to relate to important aspects of how humans experience and understand such situations. One crucial driver of peoples' social behavior during an interaction is the interdependence they perceive, i.e., how the outcome of an interaction is determined by their own and others' actions. According to psychological studies, both the nonverbal behavior displayed by Motivated by this, we present a series of experiments to automatically recognize interdependence perceptions in dyadic face-to-face negotiations using these sources. Concretely, our approach draws on a combination of features describing individuals' Facial, Upper Body, and Vocal Behavior with state-of-the-art algorithms for multivariate time series classification. Our findings demonstrate that differences in some types of interdependence perceptions can be detected through the automatic analysis of nonverbal behaviors. We discuss implications for developing socially intelligent systems and opportunities for future research.

@en

Empirical evidence suggests that the emotional meaning of facial behavior in isolation is often ambiguous in real-world conditions. While humans complement interpretations of others' faces with additional reasoning about context, automated approaches rarely display such context-sensitivity. Empirical findings indicate that the personal memories triggered by videos are crucial for predicting viewers' emotional response to such videos ?- in some cases, even more so than the video's audiovisual content. In this article, we explore the benefits of personal memories as context for facial behavior analysis. We conduct a series of multimodal machine learning experiments combining the automatic analysis of video-viewers' faces with that of two types of context information for affective predictions: \beginenumerate∗[label=(\arabic∗)] \item self-reported free-text descriptions of triggered memories and \item a video's audiovisual content \endenumerate∗. Our results demonstrate that both sources of context provide models with information about variation in viewers' affective responses that complement facial analysis and each other.

@en

This paper contributes to the automatic estimation of the subjective emotional experience that audio-visual media content induces in individual viewers, e.g. to support affect-based recommendations. Making accurate predictions of these responses is a challenging task because of their highly person-dependent and situation-specific nature. Findings from psychology indicate that an important driver for the emotional impact of media is the triggering of personal memories in observers. However, existing research on automated predictions focuses on the isolated analysis of audiovisual content, ignoring such contextual influences. In a series of empirical investigations, we (1) quantify the impact of associated personal memories on viewers’ emotional responses to music videos in-the-wild and (2) assess the potential value of information about triggered memories for personalizing automatic predictions in this setting. Our findings indicate that the occurrence of memories intensifies emotional responses to videos. Moreover, information about viewers’ memory response explains more variation in video-induced emotions than either the identity of videos or relevant viewer-characteristics (e.g. personality or mood). We discuss the implications of these results for existing approaches to automated predictions and describe ways for progress towards developing memory-sensitive alternatives.@en

An important aspect of human emotion perception is the use of contextual information to understand others' feelings even in situations where their behavior is not very expressive or has an emotionally ambiguous meaning. For technology to successfully detect affect, it must mimic this human ability when analyzing audiovisual input. Databases upon which machine learning algorithms are trained should capture the context of social interactions as well as the behavior expressed in them. However, there is a lack of consensus about what constitutes relevant context in such databases. In this article, we make two contributions towards overcoming this challenge: (a) we identify two principal sources of context for emotion perceptions based on psychological theory, and (b) we provide an overview of how each of these has been considered in published databases covering social interactions. Our results show that a similar set of contextual features are present across the reviewed databases. Between all the different databases researchers seem to have taken into account a set of contextual features reflecting the sources of context seen in psychological theory. However, within individual databases, these features are not yet systematically varied. This is problematic because it prevents them from being used directly as resources for the modeling of context-sensitive affect detection. Based on our findings, we suggest improvements for the future development of affective databases.

@en

Combining self-reports in which individuals reflect on their thoughts and feelings (Experience Samples) with sensor data collected via ubiquitous monitoring can provide researchers and applications with detailed insights about human behavior and psychology. However, meaningfully associating these two sources of data with each other is difficult: while it is natural for human beings to reflect on their experience in terms of remembered episodes, it is an open challenge to retrace this subjective organization in sensor data referencing objective time. Lifelogging is a specific approach to the ubiquitous monitoring of individuals that can contribute to overcoming this recollection gap. It strives to create a comprehensive timeline of semantic annotations that reflect the impressions of the monitored person from his or her own subjective point-of-view. In this paper, we describe a novel approach for processing such lifelogs to situate remembered experiences in an objective timeline. It involves the computational modeling of individuals’ memory processes to estimate segments within a lifelog acting as plausible digital representations for their recollections. We report about an empirical investigation in which we use our approach to discover plausible representations for remembered social interactions between participants in a longitudinal study. In particular, we describe an exploration of the behavior displayed by our model for memory processes in this setting. Finally, we explore the representations discovered for this study and discuss insights that might be gained from them.@en

An essential part of being an individual is our personal history, in particular our episodic memories. Episodic memories revolve around events that took place in a person’s past and are typically defined by a time, place, emotional associations, and other contextual information. They form an important driver for our emotional and cognitive interpretation of what is currently happening. This
includes interactions with media technologies. However, current approaches for personalizing interactions with these technologies are neither aware of what episodic memories are triggered in users, nor of their emotional interpretations of those memories. We argue that this is a serious limitation, because it prevents applications from correctly estimating users’ experiences. In short, such technologies lack empathy. In this position paper, we argue that media technologies need an Artificial Empathic Memory (AEM) of their users to address this issue. We propose a psychologically inspired architecture, examine the challenges to be solved, and highlight how existing research can become a starting point for overcoming them.@en