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Modeling: The Troxler Effect

Modeling: The Troxler Effect. Mattias Nurk Anton Prokopov Anastasia Bolotnikova. 1. Introduction. Discovered in 1804 Ignaz Paul Vital Troxler Published in the “ Ophthalmologische Bibliothek ”. 1.2 Concept.

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Modeling: The Troxler Effect

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  1. Modeling: The Troxler Effect Mattias Nurk Anton Prokopov Anastasia Bolotnikova

  2. 1. Introduction • Discovered in 1804 • Ignaz Paul Vital Troxler • Published in the “OphthalmologischeBibliothek”

  3. 1.2 Concept • When one’s vision is fixed on a central stimulus, objects in their peripheral vision disappear

  4. 1.2 Concept • A result of • Sensory adaptation (retina) • Neural adaptation (LGN) • Filling-in (V1)

  5. 1.3 Consequence

  6. 2. Idea of the model

  7. 3. Experiments: Collecting data • The time of disappearance of the stimulus varies and may depend on many different properties of the stimulus. • Our task was to conduct experiments in order to study and analyze some of the influencing aspects of the stimulus. • We focused on the following parameters: • Size • Blurriness • Distance from the fixation point

  8. 3.1 Data structure In order to study the influence of a concrete parameter on the length of the period of disappearance, we fixate the values of all the other parameters except the one that is to be studied. Table 2. Example of the input and output data

  9. 3.2 Test environment http://math.ut.ee/~prokopov/toxler/

  10. 4. Data analysis

  11. 4.1 Repeated measures ANOVA

  12. 4.2 Post Hoc Tests: Tukey HSD

  13. 4.3 Final Results Blurriness impact

  14. 4.3 Final Results Size impact

  15. 4.3 Final Results Distance impact

  16. 5. Model

  17. 5. Model

  18. Conclusion • As a result, we have a basic model that can give us an idea on how the Troxler effect happens on the level of neural networks and retinal cells. • The model is flexible in terms of parameters and can represent many different experiment of Troxler fading.

  19. Next step… Next step after creating this model will be“going inside the brain” and finding more detailed information about decrease in response to the stimulus so that we can upgrade our model and make it more realistic.

  20. Thank you for your attention!

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