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Record W4410313967 · doi:10.32920/ifmj.v4i1-2.2061

Exploring Virtual Bodies and Invisible Avatars as Storytelling Tools in Contemporary Narrative-Based Virtual Reality Projects

2024· article· en· W4410313967 on OpenAlexvenueno aff
Kath Dooley

Bibliographic record

VenueInteractive Film and Media Journal · 2024
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsStorytellingNarrativeVirtual realityArtMetaverseVisual artsAestheticsHuman–computer interactionComputer scienceLiterature

Abstract

fetched live from OpenAlex

Contemporary narrative-based virtual reality (VR) projects engage with interactors in a variety of ways. While some projects offer a virtual body or avatar that is physically manifested, other projects might position the user as an invisible body within a 360-degree story world or offer them virtual hands that are operated by hand controllers. This presentation explores the narrative impacts of these varying representations, and the way that they foster a sense of embodiment for the interactor. Moreover, the presentation explores the ways that the interactor’s body, as a physically manifested or invisible avatar, might be harnessed as a storytelling tool to evoke or advance a narrative. Scholars across various disciplines, including psychology, narratology, and computer science, have provided converging perspectives on VR’s ability to generate feelings of 'presence' and 'immersion.' Generally, these terms refer to a medium's capacity to position users within a dynamic spatial and potentially social environment that is distinct from the 'real' world. Regarding VR, Riva et al. note that, ‘there is consensus that the experience of presence is a complex, multidimensional perception, formed through an interplay of raw (multi-) sensory data and various cognitive processes’ (2007, p. 46). Further to the psychological state of presence, the concept of embodiment captures the corporeal experience of feeling present in a virtual environment. Biocca (1997) suggests that embodiment leads to 'self-presence,' involving salient mental models of the self within a virtual setting. In the context of contemporary narrative-based VR projects, advanced graphics and head tracking technologies contribute to this sense of self- presence, as well as embodiment and immersion in contemporary VR projects’ realistically or imaginatively rendered story worlds. This area is explored in this presentation via a close reading and phenomenological analysis of three recent narrative-based VR projects. These projects offer varying bodily representations and expressions of agency for the participant. The presentation interrogates the interactive devices and stylistic features of the three projects, noting how these allow the interactor to participate in the unfolding narratives. This analysis is guided by Kilteri et al.'s definition of sense of embodiment (SoE) in virtual reality (2012). This contends that a SoE can consist of three subcomponents: sense of self-location, sense of agency, and sense of body ownership (p. 375-377). Ultimately, the presentation will argue that the specificity of VR as a storytelling format is its emphasis on embodied interactions between users, represented characters, and other story agents. This is particularly evident in works featuring a digital body representation or avatar that serves as a surrogate for the interactor's 'real' body. The digital avatar empowers the interactor to shape the narrative through bodily movements and interactions with story agents, allowing real-time actions to meaningfully influence a narrative's progression. However, the presentation emphasizes that a virtual body representation or avatar is not a prerequisite for a powerful corporeal engagement with a VR work. Projects positioning the interactor as an unseen observer can also leverage their physical body as a storytelling tool by necessitating user interaction with and contemplation of a scene.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.326
GPT teacher head0.426
Teacher spread0.100 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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