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Modulation of Saccadic Curvature by Spatial Memory and Associative Learning

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Modulation of Saccadic Curvature by Spatial Memory and Associative Learning Modulation of Saccadic Curvature by Spatial Memory and Associative Learning Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften (Dr. rer. nat.) dem Fachbereich Psychologie der Philipps-Universität Marburg vorgelegt von Stephan König aus Arnsberg Marburg/Lahn, Oktober 2010 Vom Fachbereich Psychologie der Philipps-Universität Marburg als Dissertation angenommen am _________________ Erstgutachter: Prof. Dr. Harald Lachnit, Philipps Universität Marburg Zweitgutachter: Prof. Dr. John Pearce, Cardiff University Tag der mündlichen Prüfung am: 27.10.2010 Acknowledgment I would like to thank Prof. Dr. Harald Lachnit for providing the opportunity for my work on this thesis. I would like to thank Prof. Dr. Harald Lachnit, Dr. Anja Lotz and Prof. Dr. John Pearce for proofreading, reviewing and providing many valuable suggestions during the process of writing. I would like to thank Peter Nauroth, Sarah Keller, Gloria-Mona Knospe, Sara Lucke and Anja Riesner who helped in recruiting participants as well as collecting data. I would like to thank the Deutsche Forschungsgemeinschaft for their support through the graduate program NeuroAct (DFG 885/1), and project Blickbewegungen als Indikatoren assoziativen Lernens (DFG LA 564/20-1) granted to Prof. Dr. Harald Lachnit. Summary The way the eye travels during a saccade typically does not follow a straight line but rather shows some curvature instead. Converging empirical evidence has demonstrated that curvature results from conflicting saccade goals when multiple stimuli in the visual periphery compete for selection as the saccade target (Van der Stigchel, Meeter, & Theeuwes, 2006). Curvature away from a competing stimulus has been proposed to result from the inhibitory deselection of the motor program representing the saccade towards that stimulus (Sheliga, Riggio, & Rizzolatti, 1994; Tipper, Howard, & Houghton, 2000). For example, if participants are instructed to perform a saccade towards a defined target stimulus and to ignore a simultaneously presented nearby distractor stimulus, a saccade landing on the target typically exhibits curvature away from the distractor (e. g. Doyle & Walker, 2001). The present thesis reports how trajectories of saccadic eye movements are affected by spatial memory and associative learning. The final objective was to explore if the curvature effect can be used to investigate associative learning in an experimental paradigm where competing saccade targets are retrieved from associative memory rather than being sensory events. The thesis incorporates manuscripts on the following working steps to accomplish this objective: The first manuscript presents the computer software that was written in order to derive measure of saccadic curvature from the recorded eye movement traces. The second manuscript replicates and extends prior reports on the effect of (non-associative) spatial working memory on saccade deviations (Theeuwes, Olivers, & Chizk, 2005). The third manuscript uses a novel associative learning task to demonstrate that changes in saccadic curvature during associative learning comply with the acquisition and extinction of competing associations as predicted by the Rescorla-Wagner model (Rescorla & Wagner, 1972), originally put forward to explain classical conditioning in animals. Zusammenfassung Die Trajektorie einer sakkadischen Blickbewegung weist im Allgemeinen eine leichte Krümmung auf. Empirische Befunde sprechen dafür, dass eine gekrümmte Trajektorie vor allem dann resultiert, wenn in der visuellen Peripherie mehrere Reize als potentielle Sakkadenziele dargeboten werden, und so um die Selektion als Sakkadenziel konkurrieren (Van der Stigchel, Meeter, & Theeuwes, 2006). Eine Abweichung der Trajektorie in Gegenrichtung zur Position eines konkurrierenden Reizes ist durch die Inhibition des Motorprogramms erklärt worden, das die Sakkade in Richtung des konkurrierenden Reizes repräsentiert (Sheliga, Riggio, & Rizzolatti, 1994; Tipper, Howard, & Houghton, 2000). Ist eine Versuchsperson zum Beispiel instruiert eine Blickbewegung auf einen als Zielreiz definierten Stimulus hin auszuführen, und dabei einen gleichzeitig dargebotenen Distraktorreiz zu ignorieren, so krümmt sich die Sakkade weg von der Distraktorposition (e. g. Doyle & Walker, 2001). Die vorliegende Dissertation untersucht Einflüsse des räumlichen Gedächtnisses und des assoziativen Lernens auf die Krümmung von Sakkadentrajektorien. Die übergeordnete Fragestellung ist, ob der Krümmungseffekt Aufschluss über assoziative Lernprozesse gibt, wenn im Lernexperiment Konkurrenz nicht zwischen sensorischen Reizen sondern zwischen konfligierenden Vorhersagen des Sakkadenziels besteht. Die Dissertation umfasst folgende Manuskripte, die einzelne Arbeitsschritte auf dem Weg zur Beantwortung dieser Frage dokumentieren: Das erste Manuskript dokumentiert die Computersoftware, die im Rahmen der Dissertation zur Parametrisierung der aufgezeichneten Blickbewegungssignale programmiert wurde. Das zweite Manuskript repliziert und erweitert empirische Befunde über den Einfluss des räumlichen Arbeitsgedächtnisses auf die Auslenkung von Trajektorien (Theeuwes, Olivers, & Chizk, 2005). Das dritte Manuskript beschreibt ein neues assoziatives Lernparadigma und demonstriert, wie die Krümmung von Sakkadentrajektorien im Lernverlauf durch die Akquisition und Extinktion von Gedächtnisinterferenz moduliert wird. Die beobachtete Modulation steht im Einklang mit den Vorhersagen der Theorie von Rescorla und Wagner (1972) zur Erklärung von klassischer Konditionierung im Tierbereich. Table of Contents 1 Proposed Manuscripts................................................................................................14 2 Saccadic Curvature, Spatial Memory and Associative Learning...........................15 Introduction.................................................................................................................15 Saccadic Eye Movements......................................................................................16 Curvature of Trajectories........................................................................................17 Covert Visual Attention..........................................................................................17 Population Coding .................................................................................................19 Distractor Inhibition...............................................................................................20 Outline of the Present Thesis......................................................................................22 Chapter 3: Computer Software...............................................................................23 Chapter 4: Empirical Study I..................................................................................24 Chapter 5: Empirical Study II................................................................................28 References...................................................................................................................32 3 A Software Package for the Analysis of Eye Movement Trajectories with MATLAB..................................................................................................................35 Abstract.......................................................................................................................36 Introduction.................................................................................................................37 The Matlab Eye Browser............................................................................................38 Basic Steps of Eye Movement Analysis.....................................................................41 Importing data........................................................................................................41 Signal conditioning................................................................................................42 Response Identification and Parametrization.........................................................44 Saving Your Data...................................................................................................47 EMA Scripting Capabilities........................................................................................47 Data Structures and Functions in the EMA Library...............................................48 Batch Processing Examples...................................................................................49 Conclusion..................................................................................................................50 References...................................................................................................................52 Figures.........................................................................................................................54 Tables..........................................................................................................................59 - 11 - 4 Empirical Study I: Revisiting the Memory-Based Curvature Effect....................65 Abstract.......................................................................................................................66 Introduction.................................................................................................................67 Distractor Induced Curvature.................................................................................67 Selection From Population Codes..........................................................................68
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