Outline drawing across dimensions: Recognizing minimal-information artefacts in 2D and 3D.

Human visual recognition critically depends on edge-based information, which forms the fundamental cues for identifying objects by their outlines and shapes. Where two-dimensional studies have established that line drawings enable recognition performance comparable to full-color images, the extension of these principles to three-dimensional artefacts within immersive virtual reality (VR) remains underexplored. The precise impact of 3D visualization on recognition accuracy, especially under conditions of minimal visual cues, raises unresolved questions about how the human visual system integrates outline information volumetrically. This gap is important because VR is increasingly employed for education, training, and interactive systems, where understanding how visualization techniques affect object recognition can determine design effectiveness. Despite the well-founded evidence supporting edge-based recognition in 2D, challenges persist in generalizing these findings to 3D objects due to variations in dimensionality, complexity, and familiarity with the artefact. It remains unclear whether volumetric 3D cues provide consistent advantages across different familiarity levels or how these benefits interact with cognitive processes such as memory and spatial integration. This study aims to address these gaps by directly comparing human recognition performance for animal artefacts presented as outline - based 2D images and immersive 3D models in VR. By systematically varying artefact familiarity and analysing detailed response patterns across segmented artefact regions, the research seeks to clarify the extent to which outline drawing mechanisms generalize to 3D object recognition and inform practical applications in immersive learning and interactive technology.