Stage lighting in contemporary performance is increasingly recognised as a dramaturgical mechanism that shapes perception, space and spectatorship. However, the role of controlled visual limitation in generating perceptual tension has received limited scholarly attention within scenographic discourse. This study introduces Visual Pressure Theory (VPT) as a lighting-centred conceptual framework that explains how lighting generates dramaturgical effects through the strategic deployment of light and darkness. Using a practice-based and autoethnographic approach, the study draws upon the researcher’s role as the lighting designer for EPHEMERAL: An Evening of Contemporary Dance at DPAC, Malaysia. Analysis of lighting compositions, photographs, lighting plots and supplementary audience perception data examines five analytical dimensions, namely perceptual restriction, cognitive completion, spatial fragmentation, affective tension and temporal instability. The findings demonstrate that controlled visual limitation regulates spectators’ perceptual engagement by directing attention, intensifying emotional responses, encouraging embodied spectatorship and supporting meaning-making through absence and ambiguity. Supplementary audience perception data corroborate the perceptual conditions identified through the practice-based analysis. The study contributes VPT as a theoretical extension to scenographic discourse by explaining how controlled visual limitation functions as a dramaturgical mechanism that organises perception and structures spectatorship through lighting composition in contemporary performance.
This paper presents a field-based method for assessing personal light exposure using wearable spectral light sensors under real-world conditions. The method calculates melanopic irradiance and melanopic equivalent daylight illuminance from continuous spectral measurements and evaluates these measures in relation to self-reported sleep, alertness and mood data collected during daily life. The approach is demonstrated using one-week monitoring data collected from 50 university students and is evaluated against commonly used circadian lighting metrics, including WELL, Brown et al. recommendations and non-visual direct response. Results showed that participants frequently did not meet the recommended thresholds, while continuous melanopic exposure was associated with sleep and alertness. These results highlight the complementary value of continuous personal monitoring and the need for controlled studies to test the effects of meeting the recommendations.
Lighting conditions can alter thermal perception, but the magnitude of these shifts on a temperature-equivalent scale is uncertain when physiological state is measured concurrently. This secondary analysis re-examined climate-chamber data from 54 young Japanese women exposed at 26 degrees C, 28 degrees C and 30 degrees C to 30 combinations of illuminance (100 lx to 2900 lx) and correlated colour temperature (CCT; 2800 K to 5200 K). Conduction-corrected modified effective temperature (ETF) was used to express lighting-related differences in thermal appraisal. Mean skin temperature showed no consistent lighting-related shift. At neutral visual thermal sensation, ETF-equivalent values for six illuminance levels spanned approximately 2 degrees C, with a maximum difference of 1.7 degrees C between 250 lx and 1700 lx; however, the values were non-monotonic and bootstrap confidence intervals were wider than the ETF range studied. CCT showed no statistically supported association with visual thermal comfort. The ETF-equivalent values therefore describe perceptual appraisal rather than a change in physical temperature or thermophysiological heat balance. Under these short, near-neutral exposures, illuminance may influence thermal appraisal, but the present data are insufficient to support specific lighting or HVAC control recommendations.
The review provides a focused analysis of machine learning (ML) and deep learning (DL) models for managing correlated colour temperature (CCT) in adaptive road lighting systems. As urban infrastructure evolves towards smart, responsive technologies, the ability to dynamically control lighting parameters is critical for enhancing safety and energy efficiency. We systematically evaluate the motivation for moving beyond traditional control logic to data-driven ML methodologies. We conduct a detailed survey of applicable ML paradigms, including supervised, unsupervised and reinforcement learning. Specific techniques such as regression models, support vector machines and fuzzy logic systems are critically assessed for their utility in CCT selection. Furthermore, we explore the advanced capabilities of DL architectures, examining how convolutional neural networks can leverage visual data for rich contextual awareness and how recurrent neural networks can process time-series data for predictive control. A comprehensive comparative analysis evaluates these techniques against key criteria, including accuracy, computational complexity, data requirements and real-time applicability. This review synthesizes the key advantages and trade-offs of each approach, offering guidance for researchers and practitioners in selecting and implementing the most suitable computational strategies for developing next generation, intelligent road lighting systems.
