Psychogeotherapy and a Framework of Collective Augmented Reality Game
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".