Cite this DOI

First Author Dr Zoi Zoupanou, & Second Author Dr Max Tookey (2026). Attention-Driven Rendering and Interface Adaptation Using Eye-Tracking Data. *Journal of Science & Technology*, *11*(02), 01. https://doi.org/10.46243/jst.2026.v11.i02.pp01-15

@article{firstauthordrzoizoupanou2026attentiondriven,
  author    = {First Author Dr Zoi Zoupanou and Second Author Dr Max Tookey},
  title     = {{Attention-Driven Rendering and Interface Adaptation Using Eye-Tracking Data}},
  journal   = {Journal of Science \& Technology},
  year      = {2026},
  month     = {feb},
  volume    = {11},
  number    = {02},
  pages     = {01},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2026.v11.i02.pp01-15},
  url       = {https://doi.org/10.46243/jst.2026.v11.i02.pp01-15},
  language  = {en},
  abstract  = {This study investigates computational attention mechanisms in digital luxury interfaces using eye-tracking and predictive modeling. Across three experiments (N = 60), ocular biometric measures, including fixation duration, saccadic frequency, and fixation density, were analyzed during exposure to high-fidelity fashion stimuli containing aesthetic and brand-specific elements. Results showed that aesthetic fixations were the strongest predictors of brand attention, accounting for 40.3\% of variance in brand engagement, while materialism dimensions selectively influenced attention toward symbolic status cues The findings support a gaze-driven Human–Computer Interaction (HCI) framework in which aesthetic salience functions as a primary attentional signal within visually dense digital environments. From a systems-design perspective, the study proposes an attention-adaptive interface model integrating real-time ocular metrics into rendering and layout optimization processes. The research contributes a computational framework linking biometric attention sensing with adaptive interface engineering for mobile commerce, social media, and immersive digital environments.}
}

TY  - JOUR
TI  - Attention-Driven Rendering and Interface Adaptation Using Eye-Tracking Data
AU  - First Author Dr Zoi Zoupanou
AU  - Second Author Dr Max Tookey
JO  - Journal of Science & Technology
PY  - 2026
DA  - 2026/02/17/
VL  - 11
IS  - 02
SP  - 01
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - This study investigates computational attention mechanisms in digital luxury interfaces using eye-tracking and predictive modeling. Across three experiments (N = 60), ocular biometric measures, including fixation duration, saccadic frequency, and fixation density, were analyzed during exposure to high-fidelity fashion stimuli containing aesthetic and brand-specific elements. Results showed that aesthetic fixations were the strongest predictors of brand attention, accounting for 40.3% of variance in brand engagement, while materialism dimensions selectively influenced attention toward symbolic status cues The findings support a gaze-driven Human–Computer Interaction (HCI) framework in which aesthetic salience functions as a primary attentional signal within visually dense digital environments. From a systems-design perspective, the study proposes an attention-adaptive interface model integrating real-time ocular metrics into rendering and layout optimization processes. The research contributes a computational framework linking biometric attention sensing with adaptive interface engineering for mobile commerce, social media, and immersive digital environments.
DO  - 10.46243/jst.2026.v11.i02.pp01-15
UR  - https://doi.org/10.46243/jst.2026.v11.i02.pp01-15
ER  -

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    "DOI": "10.46243/jst.2026.v11.i02.pp01-15",
    "URL": "https://doi.org/10.46243/jst.2026.v11.i02.pp01-15",
    "title": "Attention-Driven Rendering and Interface Adaptation Using Eye-Tracking Data",
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    "container-title": "Journal of Science & Technology",
    "author": [
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            "family": "First Author Dr Zoi Zoupanou"
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    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "This study investigates computational attention mechanisms in digital luxury interfaces using eye-tracking and predictive modeling. Across three experiments (N = 60), ocular biometric measures, including fixation duration, saccadic frequency, and fixation density, were analyzed during exposure to high-fidelity fashion stimuli containing aesthetic and brand-specific elements. Results showed that aesthetic fixations were the strongest predictors of brand attention, accounting for 40.3% of variance in brand engagement, while materialism dimensions selectively influenced attention toward symbolic status cues The findings support a gaze-driven Human–Computer Interaction (HCI) framework in which aesthetic salience functions as a primary attentional signal within visually dense digital environments. From a systems-design perspective, the study proposes an attention-adaptive interface model integrating real-time ocular metrics into rendering and layout optimization processes. The research contributes a computational framework linking biometric attention sensing with adaptive interface engineering for mobile commerce, social media, and immersive digital environments.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2026.v11.i02.pp01-15 gives all four in one JSON answer.