The perception and learning of 4D object geometry in humans

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master_Gafur_Raihan_2026.pdf (5.73 MB)

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School of Science | Master's thesis
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Date

2025-12-17

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Machine Learning, Data Science and Artificial Intelligence

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Degree programme

Master's Programme in Computer, Communication and Information Sciences
Master's Programme in Computer, Communication and Information Sciences
Master's Programme in Computer, Communication and Information Sciences

Language

en

Pages

50

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Abstract

Human object recognition is known to be robust even when objects are seen from different angles, yet it remains unclear how far this ability generalizes beyond familiar three-dimensional (3D) objects. This thesis investigates whether humans can recognize and learn object geometry beyond familiar three-dimensional space by studying perception of four-dimensional (4D) objects. To do so, a complete computational pipeline was developed to generate, rotate, and render 4D Shepard–Metzler shaped objects composed of multiple tesseracts, which were projected into 2D images for behavioral testing. A pilot mental-rotation experiment was conducted using a same-versus–mirrored discrimination task under three conditions: random 4D shapes without feedback, random 4D shapes with immediate feedback, and a single familiar 4D shape without feedback. Results show that performance remains close to chance when both object identity and viewpoint vary without feedback, improves reliably above chance when feedback is provided, and approaches ceiling when a single 4D object becomes familiar, even without feedback. These findings suggest that humans can learn to discriminate projections of genuinely novel 4D objects, but that successful performance depends strongly on feedback and object-specific familiarity. Beyond the pilot results, the thesis introduces a reusable experimental framework for studying higher-dimensional object perception and mental rotation in humans.

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Deny, Stephane

Keywords

mental rotation, four-dimensional (4D) objects, Shepard-Metzler shapes, tesseracts, object symmetry recognition, 4D Shepard-Metzler shapes

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https://urn.fi/URN:NBN:fi:aalto-202601221843

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[dipl] Perustieteiden korkeakoulu / SCI