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THE ROLE OF LATEST FIXATIONS ON ONGOING VISUAL
                          SEARCH
        A MODEL TO EVALUATE THE SELECTION MECHANISM



            Giacomo Veneri, Pamela Federighi,Francesca Rosini, Elena
                 Pretegiani, Antonio Federico, Alessandra Rufa

            Eye tracking & Vision Applications Lab (EVA Lab) Department of Neurological Neurosurgical and Behavioral Science
                                                         University of Siena, Italy




07/01/13                                               www.evalab.unisi.it                                                     1
Outline
• Top-Down vs Bottom-Up on ongoing visual
  search
• Efficiency on Visual search
• (Bayesian) Model on visual search




07/01/13          www.evalab.unisi.it       2
Task
                                  “Visual Search depends
                                   on sensory, perceptual
                                   and cognitive processes”
                                   [colby 2009]
                                  Il TMT misuar la capcità
                                   di esplorare ed eseguire
                                   una sequenza in modo
                                   corretto
                                  TMTB è un mappa
                                   viariante in cui i target
                                   diventano distrattori
              [Wolwer 2003]

07/01/13   www.evalab.unisi.it                            3
Mathematical Method          Fixations




07/01/13          www.evalab.unisi.it               4
Direction vs Previous fixations
   In order to test an overall validity of the model, we calculated for each test
 the DEMINFIX(T) – DEMINFIX and the DEMAXFIX(T) – DEMAXFIX.Figures
    show that saccade direction take in consideration mainly fixations of last
                                    1000ms (1s).




07/01/13                           www.evalab.unisi.it                              5
07/01/13   www.evalab.unisi.it   6
Model




07/01/13   www.evalab.unisi.it   7
Human vs Machine




07/01/13        www.evalab.unisi.it   8
Conclusion
• We found significant differences among tasks
  and a correlation between the efficiency (time
  elapsed) to explore the task and the ability to
  inhibit the BAF.
• The command constraint (goal-driven) which is
  modulated by the image salience versus BAF.
• The selection mechanism that drives this
  competition. Further works will be directed to
  evaluate the relation between the BAF and the
  inhibition of return.
07/01/13             www.evalab.unisi.it            9
Conclusion
• We found significant differences among tasks
  and a correlation between the efficiency (time
  elapsed) to explore the task and the ability to
  inhibit the BAF.
• The command constraint (goal-driven) which is
  modulated by the image salience versus BAF.
• The selection mechanism that drives this
  competition. Further works will be directed to
  evaluate the relation between the BAF and the
  inhibition of return.
07/01/13             www.evalab.unisi.it            9

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THE ROLE OF LATEST FIXATIONS ON ONGOING VISUAL SEARCH

  • 1. THE ROLE OF LATEST FIXATIONS ON ONGOING VISUAL SEARCH A MODEL TO EVALUATE THE SELECTION MECHANISM Giacomo Veneri, Pamela Federighi,Francesca Rosini, Elena Pretegiani, Antonio Federico, Alessandra Rufa Eye tracking & Vision Applications Lab (EVA Lab) Department of Neurological Neurosurgical and Behavioral Science University of Siena, Italy 07/01/13 www.evalab.unisi.it 1
  • 2. Outline • Top-Down vs Bottom-Up on ongoing visual search • Efficiency on Visual search • (Bayesian) Model on visual search 07/01/13 www.evalab.unisi.it 2
  • 3. Task  “Visual Search depends on sensory, perceptual and cognitive processes” [colby 2009]  Il TMT misuar la capcità di esplorare ed eseguire una sequenza in modo corretto  TMTB è un mappa viariante in cui i target diventano distrattori [Wolwer 2003] 07/01/13 www.evalab.unisi.it 3
  • 4. Mathematical Method Fixations 07/01/13 www.evalab.unisi.it 4
  • 5. Direction vs Previous fixations In order to test an overall validity of the model, we calculated for each test the DEMINFIX(T) – DEMINFIX and the DEMAXFIX(T) – DEMAXFIX.Figures show that saccade direction take in consideration mainly fixations of last 1000ms (1s). 07/01/13 www.evalab.unisi.it 5
  • 6. 07/01/13 www.evalab.unisi.it 6
  • 7. Model 07/01/13 www.evalab.unisi.it 7
  • 8. Human vs Machine 07/01/13 www.evalab.unisi.it 8
  • 9. Conclusion • We found significant differences among tasks and a correlation between the efficiency (time elapsed) to explore the task and the ability to inhibit the BAF. • The command constraint (goal-driven) which is modulated by the image salience versus BAF. • The selection mechanism that drives this competition. Further works will be directed to evaluate the relation between the BAF and the inhibition of return. 07/01/13 www.evalab.unisi.it 9
  • 10. Conclusion • We found significant differences among tasks and a correlation between the efficiency (time elapsed) to explore the task and the ability to inhibit the BAF. • The command constraint (goal-driven) which is modulated by the image salience versus BAF. • The selection mechanism that drives this competition. Further works will be directed to evaluate the relation between the BAF and the inhibition of return. 07/01/13 www.evalab.unisi.it 9