Seeing by moving: revisiting pattern vision through fixational eye movements

Visual perception is often conceptualized as the analysis of static images which are acquired during fixations. These images are then spatially decomposed into their basic components, which form the fundament of visual perception. Understanding these spatial mechanisms has been a central aim of pattern vision, and has led to a successful and widely adopted standard model of spatial vision. The spatial view on pattern vision, however, overlooks a key aspect of visual processing: the eyes are never still. Even during fixations, involuntary eye movements incessantly modulate the visual input. These eye movements challenge the assumption that visual processing can occur independently of motion. In recent years, empirical evidence has accumulated which suggests that fixational eye movements, particularly ocular drift, actively shape visual processing. Building on these findings, this thesis argues that ocular drift is essential to how the visual system encodes spatial structure, even in the absence of external motion. It calls for a shift from static to active, spatiotemporal models of pattern vision. To support this shift, it integrates computational modeling, psychophysics, and new experimental tools to explore how ocular drift influences edge and pattern perception. The first part of this thesis revisits foundational assumptions in spatial vision. In a first study, it presents a proof-of-concept model that extends a standard spatial vision model by ocular drift and temporal processing. The active model components facilitate edge extraction, but also reveal limitations in current datasets which cannot clearly distinguish between static and active accounts of pattern vision. In response, the next two studies introduce new software tools and a benchmark dataset that specifically target the spatial frequency selective mechanisms underlying pattern vision. Using this dataset, the fourth study explicitly contrasts spatial and active accounts of pattern vision. The results show that incorporating ocular drift improves predictions of human edge sensitivity and reveals that traditional models may rely on compensatory biases to account for the absence of these eye movements. The final study introduces a behavioral task in which participants trace edges in natural scenes. This approach enables the study of pattern vision in more naturalistic contexts while maintaining the analytical rigor of traditional psychophysics through signal detection theory. The resulting dataset supports both standard analyses and investigations into individual differences and the visual features that guide edge perception in real-world settings. Altogether, this thesis integrates insights from active perception into a mechanistically grounded framework of pattern vision. It redefines early visual processing as an active, embodied process shaped by the observer’s own movements. These findings challenge static models of early visual processes and advocate for a broader paradigm shift that places motion, context, and environmental interaction at the core of perception. Finally, it lays a foundation for future research into active models of vision, offering both theoretical direction and practical tools to study visual perception under more natural conditions.