Art Practice for Sustainability: A Cognitive-Affective-Systemic Framework
October 20, 2025 Β· Declared Dead Β· + Add venue
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ivan C. H. Liu
arXiv ID
2510.17083
Category
cs.HC: Human-Computer Interaction
Citations
0
Last Checked
4 months ago
Abstract
This paper proposes a cognitive-Affective-Systemic (CAS) framework that integrates cognition, emotion, and systemic understanding to cultivate sustainability awareness through art. Drawing from eco-aesthetics, affect theory, complexity science, and posthuman ethics, the framework defines artistic practice as both epistemic and performative--a way of knowing through making and feeling. Central to this is logomotion, an aesthetic mode where comprehension and emotion move together as a unified experience. Two artworks, SPill, visualizing antimicrobial resistance through avalanche dynamics, and Echoes of the Land, modeling anthropogenic seismicity, demonstrate how systemic modeling and sensory immersion transform complex science into embodied ecological understanding. The framework offers a methodological foundation for artists, theorists, and activists to translate awareness into engagement, advancing collective creativity toward sustainable futures.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted