Perceptual Reality Transformer: Neural Architectures for Simulating Neurological Perception Conditions

TL;DR

This paper introduces the Perceptual Reality Transformer, a neural framework that simulates various neurological perception conditions to aid medical education and empathy training, outperforming traditional methods in fidelity.

Contribution

It presents the first systematic benchmark for neurological perception simulation, with novel, clinically-grounded perturbation functions and performance evaluation metrics.

Findings

Vision Transformer architectures outperform CNNs in simulation accuracy.

The framework accurately models eight neurological perception conditions.

Provides quantitative metrics for assessing perceptual simulation fidelity.

Abstract

Neurological conditions affecting visual perception create profound experiential divides between affected individuals and their caregivers, families, and medical professionals. We present the Perceptual Reality Transformer, a comprehensive framework employing six distinct neural architectures to simulate eight neurological perception conditions with scientifically-grounded visual transformations. Our system learns mappings from natural images to condition-specific perceptual states, enabling others to experience approximations of simultanagnosia, prosopagnosia, ADHD attention deficits, visual agnosia, depression-related changes, anxiety tunnel vision, and Alzheimer's memory effects. Through systematic evaluation across ImageNet and CIFAR-10 datasets, we demonstrate that Vision Transformer architectures achieve optimal performance, outperforming traditional CNN and generative approaches. Our work establishes the first systematic benchmark for neurological perception simulation, contributes novel condition-specific perturbation functions grounded in clinical literature, and provides quantitative metrics for evaluating simulation fidelity. The framework has immediate applications in medical education, empathy training, and assistive technology development, while advancing our fundamental understanding of how neural networks can model atypical human perception.