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This work explores the innovative intersection of artistic practice and academic communication, focusing on the use of augmented reality (AR) comics as documentation of artistic research. It builds upon the premise that traditional scholarly formats often fail to encapsulate the nuanced, multifaceted nature of the research process, particularly within the context of Human-Computer Interaction (HCI) and artistic methodologies. By employing a case study of the performance Precious Camouflage, the paper illustrates how a non-linear narrative integrated with hand-drawn illustrations can convey felt knowledge that transcends written documentation. Comics inherently accommodate complex narratives that represent multiple perspectives and temporalities, thus serving as a potent medium for expressing such intricate experiences. Furthermore, the work suggests that AR does not merely enhance visual storytelling; it enables thick documentation that encapsulates the contradictions and ongoing uncertainties inherent to artistic research. This aligns with prior discussions on somaesthetic design, which prioritize embodied experiences in HCI research. Last, the proposed methodology seeks to contribute a new dimension to the conversation on how artistic research can coexist alongside traditional academic outputs. It underscores the importance of documentation as an active participant in the research journey, rather than an afterthought, presenting an approach that values the unfinished and evolving nature of artistic research.
Der Beitrag untersucht die digitale Kluft zwischen armutsbetroffenen und nicht armutsbetroffenen Menschen in Deutschland. Theoretische Grundlage ist das Drei-Ebenen-Modell digitaler Klüfte, das Ungleichheit im Zugang, bei der Nutzung und den Wirkungen digitaler Medien unterscheidet. Armut wird dabei als Form sozialer Exklusion verstanden, welche die Teilhabechancen beschränkt. Medienethisch betrachtet ist digitale Teilhabe eine Frage der distributiven Gerechtigkeit. Die Sekundärdatenanalyse der Allensbacher Markt- und Werbeträgeranalyse 2025 bestätigt, dass Armutsbetroffene auf allen drei Ebenen systematisch benachteiligt sind und Ungleichheiten sich gegenseitig verstärken. Dies stützt die theoretischen Annahmen zur Reproduktion sozialer Ungleichheit im digitalen Raum.
Prior research has reported differential roles of valence and arousal in guiding memory and attention. However, few studies have systematically examined how these affective dimensions interact across their full spectrum in audiovisual contexts, particularly when multiple images varying in emotional content are simultaneously present. To address this, we used a recognition memory paradigm in which participants viewed arrays of images representing all combinations of positive/negative valence and low/high arousal. Slightly before and during image presentation, music conveying an emotional tone, also varying in valence and arousal, was played. Memory accuracy was assessed in a later recognition phase. We further employed simple eye-tracking measures to explore how visual attention is influenced by image and music valence and arousal. Results revealed that high image arousal increases fixation duration, whilst high music arousal decreases fixation duration. Memorisation likelihood was not influenced by a four-way interaction of image and music valence and arousal, but mainly by an interaction of image valence and arousal, differently depending on music arousal. In high music arousal, all images except low arousal positive images, were memorised regardless of valence. In low arousal music, we observed that memorisation likelihood was mainly driven by high image arousal, but only paired with negative image valence was memorisation significantly higher compared to other image types. Discrimination accuracy was not observed to be influenced by image valence and arousal, but positive high arousal music significantly improved memory discrimination. By systematically manipulating both valence and arousal of images and music, we highlight how the interaction of these unimodal affective qualities can facilitate or hinder memory.
The growing burden of mental illness and limited access to evidence-based psychotherapy have increased interest in artificial intelligence (AI)–driven conversational agents as potential supports for mental health care. In this exploratory pilot study, we examined the safety and feasibility of an intelligent virtual agent (IVA) designed to simulate psychotherapeutic interactions, with a focus on high-risk situations involving suicidality and substance use. Two licensed psychotherapists engaged in scripted interactions with the IVA across 12 predefined scenarios addressing suicidality and substance abuse. The IVA was powered by GPT-4omni and embedded in a Unity-based avatar. After each interaction, testers evaluated acceptance, usability, and human–robot interaction. Two independent psychotherapists rated the IVA’s responses using a structured scale assessing guideline adherence, risk recognition, help provision, de-escalation, and empathy. No real patients were involved; all interactions were simulated for safety testing purposes. The IVA showed preliminary indications of good usability and generally empathic responses. However, problematic responses occurred in 29% of conversations, with 12.5% rated as highly critical. Responses rated as “critical” or “highly critical” referred to outputs that failed to provide adequate support, showed insufficient risk recognition, or included ethically problematic suggestions. Key concerns included inadequate recognition of risk, normalization of substance use, and insufficient referral to crisis resources, particularly in scenarios involving underage alcohol access and suicide-related inquiries. In this small, expert-based pilot safety evaluation, the findings suggest that although AI-based agents may improve access to mental health support, rigorous safety evaluation, clinical oversight, and robust safeguards are essential prior to clinical deployment. No clinical conclusions can be drawn from this simulated study.
The COVID-19 pandemic and ensuing lockdowns disrupted social connectivity, prompting individuals to seek alternative sources of socioemotional support. This study investigated whether beat-based music, characterized by the Spotify danceability feature, served as a surrogate for social reward during the first European lockdown (March–May 2020). We integrated large-scale Spotify streaming data with psychological measures of socioemotional support from the COVIDiSTRESS global survey and governmental stringency indices across 11 European countries. Results from a linear-mixed effects model indicate that people listened to music with higher danceability during social distancing after the COVID onset (30 March – 30 May 2020) compared with the same pre-COVID period in the year before. A quasi-Bayesian multilevel mediation analysis further revealed that stricter social distancing policies predicted lower perceived socioemotional support, which in turn was associated with increased listening to more highly danceable music. This effect was specific to certain facets of socioemotional need, namely emotional attachment and reassurance of worth, which delineates the instantaneous rewarding nature of social recognition, often encountered during common activities, such as dinner parties, (band or dance) rehearsals, or (themed) excursions. These findings suggest that individuals may intuitively gravitate toward rhythmically engaging music to compensate for diminished social affirmation and bonding, highlighting beat-based music as a potential non-pharmacological tool for addressing transient socioemotional deficits during social isolation.
This article outlines a pilot study examining the reception of a video clip ("Tous les mêmes" by Stromae) in qualitative and quantitative approaches analysing user comments on social media: What motivates users to watch, interpret, and comment on polarising clips? The overall aim is to carry out an analysis of all (available) comments in order to contribute to the acquisition of knowledge about user motivations and to the methodological debate of comment analysis.
