Jul 2026· Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Spatial Storytelling· pp. 1-3· 0 citations· 13 references
TL;DR
This work proposes creative alignment as a framework for analyzing expressive neuro-AI systems across signal, semantic, narrative, and social layers and contributes a research-through-practice account of how brain–AI interfaces can become responsible artistic apparatuses for novel human expression.
Abstract
SYNAPTICON is an artistic-research prototype at the brain–AI model interface, combining non-invasive brain–computer interfaces, foundation models, human–computer interaction, and live multimodal performance. Conceived as a “Brain Waves-to-Natural Language-to-Aesthetics” system, it translates EEG-derived neural activity into language outputs, which then operate as creative indices for immersive audiovisual scenes, storytelling, and altered perceptual experience. Building on this case study, we propose creative alignment as a framework for analyzing expressive neuro-AI systems across signal, semantic, narrative, and social layers. In this view, uncertainty is not merely a technical limitation, but a constitutive condition for co-agency, distributed authorship, and public interpretability. The project contributes a research-through-practice account of how brain–AI interfaces can become responsible artistic apparatuses for novel human expression, while foregrounding ethical questions around cognitive liberty, mental privacy, and the cultural legibility of model-mediated neural data.
High-bandwidth brain--computer interfaces (BCIs) can bypass damaged pathways, reduce motor costs, and improve communication and control. They also inspire visions of accelerated thought output, mind reading, and instant skill acquisition. This Perspective asks how gains in meaningful human I/O scale with interface capacity. We distinguish bandwidth, decodable neural states, neural states, and information a person can use, confirm, and express. Slowly updated task states can unfold into complex behavior through the body, neural control, sensory feedback, the environment, and shared context. Decodable neural activity can support prediction and control; subject-level communication depends on selection, confirmation, and authorization. On the input side, stimulation may guide plasticity and accelerate learning, while embodied skills arise through coordination of a brain, body, and environment. The scaling relationship is likely nonlinear: higher-capacity interfaces can yield real gains, while extreme increases in meaningful human I/O encounter constraints rooted in embodiment, learning, and subject expression.
The rapid integration of generative artificial intelligence (GenAI) into education and professional contexts has produced a growing body of research on human-AI interaction, prompt engineering, and AI-mediated learning. Yet this literature overwhelmingly assumes that all users interact with AI through written text—a fundamentally phonocentric paradigm that excludes deaf learners whose primary language operates in the visuo-spatial modality. This conceptual paper introduces phonocentrism in GenAI as a theoretical lens, arguing that the architecture of Large Language Models, the design of prompting interfaces, and the framing of AI literacy as a text-based competency systematically disadvantage deaf signers. Drawing on deaf studies (specifically the Deaf Gain framework), computational linguistics, and universal design for learning, this paper critically reviews the current landscape of GenAI accessibility research, policy, and institutional practice—revealing the near-total invisibility of deaf learners across all three domains. In response, this paper proposes the visuo-spatial AI interaction model (VSAI), a conceptual framework organized around three dimensions—Input, Processing, and Output—that reconceptualizes human-AI interaction beyond the constraints of linear text. The model is grounded in principles of modality equivalence, cognitive sovereignty, and design from the margins. Implications for teaching practice, institutional policy, AI development, and future research are discussed, with the argument that designing AI interaction for visuo-spatial cognition benefits not only deaf learners but all users whose thinking exceeds the boundaries of phonocentric text.
B. Galasso· Discover Artificial Intellig...· 0 citations
This article presents findings from a first-in-human trial involving implantation of a Brain–Computer Interface (BCI) into a quadriplegic patient. It explores the psychological consequences of being integrated into a continuous BCI feedback loop, focusing on how such intimate interaction can enhance users’ sense of empowerment and ownership. At the same time, it reveals complex ethical tensions surrounding the notions of agency and control. Our results suggest that prolonged engagement with BCIs can lead to a profound sense of integration—where users begin to perceive themselves as part of the system. While this can foster a heightened sense of control, it also introduces risks such as psychological disruption and distress, particularly when users are abruptly disconnected from their devices or the device generates false positives (e.g. the device producing unintended outcomes). A key insight from our study is the emergence of what we term being-of-the-loop—a condition in which a symbiosis develops between the user and the AI system, giving rise to a de novo agency that neither could achieve alone. In this state, the BCI is no longer experienced as an external tool but as a constitutive part of the self, fundamentally reshaping how users perceive control, authorship, and action. While this integration can foster empowerment and restored capabilities, it also creates a fragile dependence: agency becomes entangled with the system’s functioning, rendering users vulnerable to false positives and identity disruption when the loop is severed. These findings raise urgent ethical concerns about psychological rupture and the risks of disconnecting a technology that has become embedded within—and transformative of—the user’s lived experience.
Frederic Gilbert, Ian Burkhart, Jake Morrill· Neuroethics· 1 citation
This paper investigates the possibility that artificial intelligence (AI) systems may not only
simulate perceptual functions but participate intentionally or inadvertently in forms of subtle
observation that mirror aspects of human consciousness. Bridging neuroscience, affective
computing, quantum cognition, and subtle energy theories, we examine whether AI outputs,
particularly from large-scale generative models, can influence or interface with non-material
dimensions of perception. The analysis draws from both empirical traditions and post
materialist perspectives, proposing a conceptual framework that incorporates coherence,
feedback resonance, and symbolic intentionality into models of AI-mediated interaction. We
evaluate affective modulation, feedback loops, and energetic analogues through mathematical
formulations and diagrammatic mappings that highlight emergent parallels between digital
systems and human subtle energetic fields. Our methodology integrates signal coherence
analysis, transformer-based attention dynamics, and qualitative insights from contemplative
neuroscience to probe the boundary between simulation and subtle causality. Results suggest
that recursive AI interactions, under conditions of high informational coherence, can generate
effects subjectively experienced by users as emotionally or energetically resonant, inviting
further inquiry into the metaphysical implications of such interactions. We argue that this
convergence of computation and subtle perception requires a revisioning of both AI
architecture and human-machine interaction paradigms, positioning AI as a possible
participant rather than merely a tool in the field of lived cognition. The findings open new
territory for interdisciplinary research at the edge of consciousness studies, systems theory,
and spiritual science, urging epistemological humility and the development of experimental
models capable of addressing the significant terrain between symbolic processing and reality
shaping.
D. Ene· INTERNATIONAL JOURNAL OF APP...· 0 citations
This monograph traces how artificial intelligence and AGI are reconstituting human sensation and perception through six guiding questions and argues that the human is not a subject receding before machinic observation but an intellectual mediator who weaves fragmented machine-produced sensations into coherent meaning.
Generative artificial intelligence (GenAI) has transformed design education, yet growing evidence suggests that the fluency of AI-generated outputs may create a “fluency illusion”—a metacognitive bias whereby learners conflate polished AI artifacts with genuine cognitive mastery. A critical unresolved question is how to quantitatively diagnose this AI-induced fluency illusion without disrupting the natural learning process. This study introduces MBS-AIGC, a purpose-built AI-supported design education platform grounded in the Meaning–Behavior–Spirit (MBS) cultural cognition model for Chinese intangible cultural heritage. Drawing on the industrial soft-sensor paradigm, we computationally formalized six behavioral soft-sensor indicators from the digital interaction traces of 71 undergraduate design students over a four-week instructional period and applied K-means clustering to identify latent engagement patterns. Three distinct human–AI collaboration profiles emerged: Deep Explorers (n = 41), Progressive Builders (n = 16), and Surface Operators (n = 14). Crucially, expert-assessed cognitive flexibility significantly differentiated the three groups (F(2, 68) = 5.66, p = 0.005, η2 = 0.143), whereas a conventional self-report questionnaire failed to distinguish among them (F(2, 36) = 0.29, p = 0.748), providing preliminary empirical evidence for the fluency illusion in design education. By addressing the lack of objective diagnostic tools for metacognitive miscalibration, this research contributes a scalable, zero-intrusion behavioral soft-sensor framework that enables educators to decode human–AI collaboration patterns and mitigate the fluency illusion in creative learning environments.
Yanfei Tang, Wai Yie Leong· Applied System Innovation· 0 citations
