Developing Robust Methods and Tools for Advancing Perceptual Learning Research
Abstract
Perceptual Learning (PL) refers to experience-based changes enhancing the ability to extract sensory information from the environment leading to alterations in perceptual processing. A pivotal inquiry in this field investigates the potential for adult perceptual systems to undergo modifications through experience. While historically, research in the field has primarily delved into isolating and understanding individual visual processes, recent years have witnessed a growing interest in harnessing PL for therapeutic interventions in visual impairments. However, the translational potential of such interventions is impeded by methodological constraints, including small sample sizes, homogeneity within participant populations, and challenges in replications. The core objective of my dissertation is to advance our understanding of visual PL by designing innovative methodologies and tools to explore its potential for translational applications. Each chapter of my dissertation contributes distinctively to this overarching aim: Chapter 1 provides a comprehensive review of extant PL literature, pinpointing prevailing limitations and gaps in the field; Chapter 2 introduces and validates PLFest, a cross platform open-source tool for PL research, fostering collaboration and data sharing within the scientific community; Chapter 3 introduces a gaze contingent display framework for PL research, utilizing simulated central vision loss as a model to assess specificity and generalizability of learning; and Chapter 4 examines the implications of a gamified visual rehabilitation strategy for promoting learning and designing targeted interventions for patients with schizophrenia. Through these multifaceted investigations, my thesis aims to deepen our understanding of visual PL dynamics and lay foundations for its broader application in clinical contexts.