Abstract

Color science psychophysics measures the human visual system by relating controlled visual stimuli to observer responses, often through forced-choice paradigms such as two-alternative forced choice (2AFC) (Kingdom and Prins 2016). These studies require stimuli specified in physically meaningful units, technically correct display emission, and reproducible execution (Fairchild 2013; Lin et al. 2023). In practice, graduate-level experiments often rely on bespoke MATLAB or Python scripts, standard dynamic range (SDR)-first toolchains such as Psychtoolbox and PsychoPy, manual stimulus duplication, and incomplete capture of calibration, colorimetric state, and display capability (Brainard 1997; Peirce et al. 2019). These limitations increase experimental friction and create reproducibility risks, particularly for High Dynamic Range (HDR) research where incorrect presentation paths can silently collapse HDR intent into SDR output (International Telecommunication Union 2025; Society of Motion Picture and Television Engineers 2014; Apple Developer 2021; Liu et al. 2015; Hexley et al. 2020). This thesis positions HDR-Research-App as research infrastructure for reproducible HDR/SDR psychophysics in color science by defining a platform architecture centered on declarative study definitions, deterministic execution, provenance-complete metadata capture, requested/realized/measured display-state reporting, integrated response logging, and a unified psychometric-analysis pathway. The contribution is infrastructural rather than perceptual: the thesis does not claim a new visual finding, but evaluates whether a platform architecture can make HDR and SDR psychophysics more auditable, easier to inspect and repeat, and less dependent on bespoke code. Validation is structured around three graduate-level case-study validation targets (one HDR and two SDR), comparison against reference analysis tools, and evidence gates for determinism, provenance completeness, friction reduction, and HDR luminance and chromatic auditability. Where final app-produced evidence is not archived, the thesis reports the validation method, implementation status, and remaining evidence boundary rather than substituting manual records for platform evidence. The conceptual contributions are framed as five components: a deterministic execution model, a provenance framework, a requested/realized/measured display-state model, psychometric analysis integration, and a declarative study schema. These contributions are intended to be platform-agnostic, even though the reference implementation uses macOS and Extended Dynamic Range (EDR).

Publication Date

6-2026

Document Type

Thesis

Student Type

Graduate

Degree Name

Color Science (MS)

Department, Program, or Center

Color Science

College

College of Science

Advisor

Mark D. Fairchild

Advisor/Committee Member

Susan Farnand

Campus

RIT – Main Campus

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