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Poster

Fast Fitting on a Saccadic Eye Movement Model for Decision Making

MPG-Autoren
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Lee,  JJ
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bieg,  H-J
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83839

Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83861

Chuang,  LL
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Lee, J., Bieg, H.-J., Bülthoff, H., & Chuang, L. (2011). Fast Fitting on a Saccadic Eye Movement Model for Decision Making. Poster presented at 12th Conference of Junior Neuroscientists of Tübingen (NeNA 2011), Heiligkreuztal, Germany.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-B9BE-3
Zusammenfassung
How does our visual system decide where to look? The Linear Approach to Threshold with Ergodic Rate (LATER: Carpenter, 1995) is a simple decision-making model for saccadic eye movements. Currently, experimental data suggest that saccadic eye-movements can be discriminated according to whether they are performed for directed fixations or for item recognition (Montagnini Chelazzi, 2005; Bieg et al., submitted). Unfortunately, sufficient goodness-of-fit can only be acquired with large datasets, for each individual participant. Here, we investigate whether adapting LATER with modern computational methods can allow for saccades to be classified for their functionality, with minimal data and in real-time. In doing so, we strive towards the eventual goal of using the LATER model for predicting observer intentions in real-world applications.