Attention Looking without Seeing Why Have Attention Limited
- Slides: 28
Attention
Looking without Seeing
Why Have Attention? • Limited resources – Too much information • Attention: 1. selects important/relevant information 2. modulates it in the context of the task at hand
Attention Mechanisms • Top-Down – Goal-driven • Bottom-up – Stimulus-driven (“attention capture”) – There is debate if total bottom-up really exists • Attention capture is shown to be modulated by task goals • Early Selection vs. Late Selection
Visual Attention 15. 1 Evidence for Early Selection in Audition Shadowing paradigm What can be followed? + + X Position (left/right)? Pitch (male/female)? Language (English/French)? --> Early Selection © Stephen E. Palmer, 2002
15. 1 Visual Attention Evidence for Late Selection in Audition Cocktail Party phenomenon Unselected information can get in: Subject’s own name Words expected from context --> Early and late selection © Stephen E. Palmer, 2002
15. 1 Visual Attention Inattention Paradigm (Mack & Rock) What do we see without attention? © Stephen E. Palmer, 2002
Visual Attention 15. 1 Inattention Paradigm Results: © Stephen E. Palmer, 2002
Visual Attention 15. 1 Inattentional Blindness: On many trials, subjects report seeing NOTHING if the test object is at fixation. Square at Fixation: 50 -75% IB Own Name at Fixation: 5% IB Other’s name at Fixation: 35% IB Variant of own name at Fixation: 60% IB (e. g. , JECK instead of JACK) © Stephen E. Palmer, 2002
Show movie…
Visual Attention 15. 1 Change Blindness © Stephen E. Palmer, 2002
Visual Attention 15. 1 Change Blindness © Stephen E. Palmer, 2002
Eye Movements Q: Why? A: Limitations of the eye – only fovea is high-res enough for many tasks Two types: – Saccades • Rapid motion (25 -30 ms) between fixations • Saccades occur every 250 -300 ms • Also evidence for “micro-saccades” – Smooth-pursuit (tracking) movements • Require feedback
Eye-Tracking Alfred Yarbus
goal-attenuated Alfred Yarbus
Saliency Maps • Itti et al proposed that bottom-up attention can be predicted from low-level visual features. • Eye-tracking can be used to validate the predictions • What are the problems with this idea?
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