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  <doi_batch_id>aspg-25-4394-1792223490</doi_batch_id>
  <timestamp>20261017075130</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Cognitive Human-Computer Interaction</full_title>
    <abbrev_title>JCHCI</abbrev_title>
    <issn media_type="print">2771-1471</issn>
    <issn media_type="electronic">2771-1463</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2026</year>
    </publication_date>
    <journal_volume>
     <volume>11</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Scene-Level Assessment of Comfort, Legibility, and Spatial Control in Virtual Reality Interfaces</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Massila</given_name>
      <surname>Kamalrudin</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Malaysia</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Mustafa</given_name>
      <surname>Musa</surname>
      <affiliations>
       <institution>
        <institution_name>Center of Research and Innovation Management, Universiti Teknikal Malaysia Melaka, Malaysia</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Virtual reality interface quality is not determined by visual appeal alone. A scene may look convincing while still producing unstable gaze, uncomfortable depth switching, excessive head movement, or slow target selection. This paper presents a scene-level assessment framework for measuring comfort, legibility, and spatial control in VR interfaces. The work is deliberately organized as a design-science evaluation rather than as a conventional classifier study: it begins with interface failure mechanisms, defines observable headset and scene variables, computes a Virtual Reality Interface Comfort score, and then translates the results into review actions. The empirical analysis uses a processed feature-level extract aligned with public VR eye-tracking task structures and combines gaze stability, pupil variability, vergence error, head-turn demand, tracking loss, selection latency, contrast balance, target comfort, depth pressure, and spatial-memory support. The results indicate that comfortable VR scenes are characterized by stable fixation, consistent depth placement, strong spatial memory support, and modest interaction latency, while high-risk scenes are mainly associated with head-turn demand, tracking loss, pupil variability, and depth pressure. The paper contributes a transparent measurement model, a set of scene pattern diagnostics, and a practical governance workflow for deciding when a VR interface should be released, revised, or retested.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2026</year>
    </publication_date>
    <pages>
     <first_page>42</first_page>
     <last_page>49</last_page>
    </pages>
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     <item_number item_number_type="article-number">4394</item_number>
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     <doi>10.54216/JCHCI.110207</doi>
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