{"id":{"repo_id":"tu-berlin","oai_identifier":"oai:depositonce.tu-berlin.de:11303/26280"},"canonical_url":"https://search.dev.ndltd.org/etd/tu-berlin/oai:depositonce.tu-berlin.de:11303/26280","repository":{"repo_id":"tu-berlin","name":"Technische Universität Berlin","base_url":"https://api-depositonce.tu-berlin.de/server/oai/request"},"display":{"title":"Seeing by moving: revisiting pattern vision through fixational eye movements","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Schmittwilken, Lynn"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Marianne, Maertens"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026","date_published":"2026","updated_at":"2026-07-27T21:28:57Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":["https://creativecommons.org/licenses/by-nc-nd/4.0/"],"identifier_entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://doi.org/10.14279/depositonce-25108"],"render_values":[{"text":"https://doi.org/10.14279/depositonce-25108","href":"https://doi.org/10.14279/depositonce-25108","code":true}]}]},"links":{"outbound_url":"https://depositonce.tu-berlin.de/handle/11303/26280","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Marianne, Maertens"]},{"key":"dc:creator","label":"Author","values":["Schmittwilken, Lynn"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-03-27T14:51:01Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-03-27T14:51:01Z"]},{"key":"dc:date.issued","label":"Date","values":["2026"]},{"key":"dc:type","label":"Dc Type","values":["Doctoral Thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://depositonce.tu-berlin.de/handle/11303/26280","https://doi.org/10.14279/depositonce-25108"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.","Visuelle Wahrnehmung wird häufig als Analyse statischer Bilder konzeptionalisiert, die während visueller Fixationen aufgenommen werden. Diese Bilder werden anschließend räumlich in ihre Grundkomponenten zerlegt, welche die Grundlage der visuellen Wahrnehmung bilden. Das Erforschen dieser räumlichen Mechanismen ist ein zentrales Ziel der sogenannten Musterwahrnehmung und hat zu einem erfolgreichen und weit verbreiteten Standardmodell der räumlichen Wahrnehmung geführt. Dieser räumliche Fokus übersieht jedoch einen entscheidenden Aspekt der visuellen Verarbeitung: Die Augen stehen niemals still. Selbst während Fixationen modulieren kleine, unwillkürliche Augenbewegungen kontinuierlich den visuellen Input. Diese Bewegungen stellen die Annahme in Frage, dass visuelle Verarbeitung jemals unabhängig von Bewegung stattfinden kann. In den letzten Jahren gibt es zunehmende empirische Evidenz, die darauf hindeutet, dass insbesondere der sogenannte Augendrift visuelle Prozesse aktiv beeinflusst. Aufbauend auf diesen Erkenntnissen argumentiert diese Dissertation, dass Augendrift wesentlich zum Kodieren räumlicher Strukturen beiträgt – selbst in Abwesenheit externer Bewegung. Darüber hinaus argumentiert sie für einen Paradigmenwechsel: weg von statischen hin zu aktiven, raum-zeitlichen Modellen der Musterwahrnehmung. Um diesen Wandel zu unterstützen, kombiniert sie Modellierung, Psychophysik und neue experimentelle Software, um zu untersuchen, wie Augendrift die Wahrnehmung von Kanten und Mustern beeinflusst. Der erste Teil der Dissertation hinterfragt grundlegende Annahmen der räumlichen Denkschule. Die erste Studie stellt ein Modell vor, das ein klassisches, räumliches Wahrnehmungsmodell um Augendrift und zeitliche Verarbeitung erweitert. Die neuen Modellkomponenten vereinfachen die Extraktion von Kanten. Gleichzeitig offenbaren sie die Schwächen bestehender Datensätze, die statische und dynamische Ansätze der Musterwahrnehmung nicht unterscheiden können. Als Reaktion darauf entwickeln die folgenden beiden Studien neue Software und einen Datensatz, der gezielt die frequenzselektiven Mechanismen der Musterwahrnehmung testet. Auf Grundlage dieses Datensatzes vergleicht die vierte Studie explizit statische und aktive Erklärungsansätze. Die Ergebnisse zeigen, dass Augendrift die Vorhersage menschlicher Kantensensitivität verbessert. Im Vergleich sind traditionelle Modelle auf kompensatorische Mechanismen angewiesen, um das Fehlen von Augendrift auszugleichen. Die letzte Studie stellt einen neuen experimentellen Ansatz vor, bei dem Teilnehmende Kanten in natürlichen Szenen nachzeichnen. Dieser Ansatz ermöglicht die Untersuchung von Musterwahrnehmung in natürlichen Kontexten. Um den Rigor von klassischen Psychophysikansätzen zu erhalten, betten wir den Ansatz in Signalentdeckungstheorie ein. Die resultierenden Daten erlauben sowohl klassische Analysen als auch die Untersuchung individueller Unterschiede und visueller Merkmale, die Kantenwahrnehmung in natürlichen Kontexten steuern. Zusammenfassend integriert diese Arbeit Erkenntnisse der aktiven Wahrnehmung in ein mechanistisches Modell der Musterwahrnehmung. Sie definiert frühe visuelle Verarbeitung als einen aktiven Prozess, der durch Bewegungen des Betrachtenden entsteht. Die Ergebnisse stellen rein räumliche Modelle der frühen visuellen Verarbeitung infrage und befürworten einen Paradigmenwechsel, der Bewegung, Kontext und Umweltinteraktion ins Zentrum der Wahrnehmung rückt. Dies schafft eine Grundlage für zukünftige Forschung zu aktiven Modellen des Sehens – sowohl in theoretischer Hinsicht als auch durch praktische Werkzeuge zur Untersuchung visueller Prozesse unter natürlicheren Bedingungen."]},{"key":"dc:title","label":"Title","values":["Seeing by moving: revisiting pattern vision through fixational eye movements"]}]}],"canonical_facts":{"dc:contributor.advisor":["Marianne, Maertens"],"dc:creator":["Schmittwilken, Lynn"],"dc:date.accessioned":["2026-03-27T14:51:01Z"],"dc:date.available":["2026-03-27T14:51:01Z"],"dc:date.issued":["2026"],"dc:description.abstract":["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.","Visuelle Wahrnehmung wird häufig als Analyse statischer Bilder konzeptionalisiert, die während visueller Fixationen aufgenommen werden. Diese Bilder werden anschließend räumlich in ihre Grundkomponenten zerlegt, welche die Grundlage der visuellen Wahrnehmung bilden. Das Erforschen dieser räumlichen Mechanismen ist ein zentrales Ziel der sogenannten Musterwahrnehmung und hat zu einem erfolgreichen und weit verbreiteten Standardmodell der räumlichen Wahrnehmung geführt. Dieser räumliche Fokus übersieht jedoch einen entscheidenden Aspekt der visuellen Verarbeitung: Die Augen stehen niemals still. Selbst während Fixationen modulieren kleine, unwillkürliche Augenbewegungen kontinuierlich den visuellen Input. Diese Bewegungen stellen die Annahme in Frage, dass visuelle Verarbeitung jemals unabhängig von Bewegung stattfinden kann. In den letzten Jahren gibt es zunehmende empirische Evidenz, die darauf hindeutet, dass insbesondere der sogenannte Augendrift visuelle Prozesse aktiv beeinflusst. Aufbauend auf diesen Erkenntnissen argumentiert diese Dissertation, dass Augendrift wesentlich zum Kodieren räumlicher Strukturen beiträgt – selbst in Abwesenheit externer Bewegung. Darüber hinaus argumentiert sie für einen Paradigmenwechsel: weg von statischen hin zu aktiven, raum-zeitlichen Modellen der Musterwahrnehmung. Um diesen Wandel zu unterstützen, kombiniert sie Modellierung, Psychophysik und neue experimentelle Software, um zu untersuchen, wie Augendrift die Wahrnehmung von Kanten und Mustern beeinflusst. Der erste Teil der Dissertation hinterfragt grundlegende Annahmen der räumlichen Denkschule. Die erste Studie stellt ein Modell vor, das ein klassisches, räumliches Wahrnehmungsmodell um Augendrift und zeitliche Verarbeitung erweitert. Die neuen Modellkomponenten vereinfachen die Extraktion von Kanten. Gleichzeitig offenbaren sie die Schwächen bestehender Datensätze, die statische und dynamische Ansätze der Musterwahrnehmung nicht unterscheiden können. Als Reaktion darauf entwickeln die folgenden beiden Studien neue Software und einen Datensatz, der gezielt die frequenzselektiven Mechanismen der Musterwahrnehmung testet. Auf Grundlage dieses Datensatzes vergleicht die vierte Studie explizit statische und aktive Erklärungsansätze. Die Ergebnisse zeigen, dass Augendrift die Vorhersage menschlicher Kantensensitivität verbessert. Im Vergleich sind traditionelle Modelle auf kompensatorische Mechanismen angewiesen, um das Fehlen von Augendrift auszugleichen. Die letzte Studie stellt einen neuen experimentellen Ansatz vor, bei dem Teilnehmende Kanten in natürlichen Szenen nachzeichnen. Dieser Ansatz ermöglicht die Untersuchung von Musterwahrnehmung in natürlichen Kontexten. Um den Rigor von klassischen Psychophysikansätzen zu erhalten, betten wir den Ansatz in Signalentdeckungstheorie ein. Die resultierenden Daten erlauben sowohl klassische Analysen als auch die Untersuchung individueller Unterschiede und visueller Merkmale, die Kantenwahrnehmung in natürlichen Kontexten steuern. Zusammenfassend integriert diese Arbeit Erkenntnisse der aktiven Wahrnehmung in ein mechanistisches Modell der Musterwahrnehmung. Sie definiert frühe visuelle Verarbeitung als einen aktiven Prozess, der durch Bewegungen des Betrachtenden entsteht. Die Ergebnisse stellen rein räumliche Modelle der frühen visuellen Verarbeitung infrage und befürworten einen Paradigmenwechsel, der Bewegung, Kontext und Umweltinteraktion ins Zentrum der Wahrnehmung rückt. Dies schafft eine Grundlage für zukünftige Forschung zu aktiven Modellen des Sehens – sowohl in theoretischer Hinsicht als auch durch praktische Werkzeuge zur Untersuchung visueller Prozesse unter natürlicheren Bedingungen."],"dc:identifier.uri":["https://depositonce.tu-berlin.de/handle/11303/26280","https://doi.org/10.14279/depositonce-25108"],"dc:language.iso":["en"],"dc:rights.uri":["https://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:title":["Seeing by moving: revisiting pattern vision through fixational eye movements"],"dc:type":["Doctoral Thesis"]},"updated_at":"2026-07-27T21:28:57Z"}