Psychogeotherapy and a Framework of Collective Augmented Reality Game
Notice bibliographique
Résumé
Seize is an Augmented Reality (AR) collective game project that invites players to reawaken their past bodily experiences through AR doodling in the public urban landscape, serving as a psychogeotherapy practice. This project began with the development of an AR doodling game designed for players to visualize their lockdown experiences in Shanghai, China, during the pandemic in 2022. Seize offers two playing modes. In single-player mode, players can use the mobile AR game app to wander the city and doodle virtually in the cityscape. In group-player mode, participants can gather together to translate their bodily memories into doodles by following prompts. We have organized four collective AR game workshops including three in Shanghai and one reenactment in Cape Town in 2023. These workshops invite players with diverse backgrounds to engage in the game, create 3D doodles, and collectively discuss personal experiences and game mechanics. AR game workshops are seen as simulation plays that allow players to simulate and address realities and personal emotions through collective creativities. Participants are not only players but also co-creators of the AR game, reflecting and altering the rules of play during the workshops. The goal of this paper is to theorize a framework for an AR collective game by examining our four AR game workshops as case studies, following the principles of psychogeotherapy. We aim to theorize the openness, multiplicities, and sensual experience inherent in psychogeotherapy practice and apply them to the design framework of AR game workshops. We will address how the collective AR game can serve as a form of healing through collective play in the public urban space. This project is situated within the framework of psychogeotherapy, which originated from psychogeotherapy defined by Guy Debord as “the study of the precise laws and specific effects of the geographical environment, consciously organized or not, on the emotions and behaviour of individuals’” (Debord, 1955). The concept of walking in the city as a flâneur, introduced by Charles Baudelaire and adopted by Walter Benjamin, characterizes “aimless walking” as a typical example of psychogeography. This approach establishes a new understanding of psychotherapeutic processes by involving body and memory through the lens of depth-psychology (Singer, 2010; Rose,2019; Chrześcijańska, 2020) This approach differs significantly from the traditional psychotherapeutics conducted in a closed and safe space between the doctor and the patient. Another crucial theoretical framework is critical game making, as elaborated by game scholars and designer like Mary Flanagan (2009), Lindsay Grace (2011), Wafaa Bilal (2013), and Rilla Khaled (2018), who leverage game as a means of engaging in critical dialectics. The collective game workshop is constructed based on community-led design and participatory design principles, challenging the boundary between player and designer (Taylor, 2006; Sanders & Stappers, 2008; Costanza-Chock, 2020; Burkett, 2012). We aim to use AR game workshops as case studies to review and reflect on the AR game design principles. This involves examining video and image documentations of AR game workshops, interviewing participants, organizing game AR doodling creations, and comparing them to psychogeotherapy studies.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,018 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».