1. Lab 2 Issues: Needed to adapt to the “normal environment”. We would have liked to see more rapid adjustment and a stable baseline. Most subjects adapted in the transformed environment but variability made it hard to tell. Needed more trials or a simpler task. Big picture – everyone was able to do the task reasonably well despite the novel visuo-motor transformation but data was really too noisy/variable to say much.

2. Control of Attention and Gaze in Natural Environments Lab 3

3. Selecting information from visual scenes What controls the selection process?

4. Foot placement Obstacle avoidance Heading Top-down factors

5. To what extent is the selection of information from scenes determined by cognitive goals (ie top-down) and how much by the stimulus itself (ie salient regions - bottom-up effects)?

6. Dynamic Environments

7. The Problem Any selective perceptual system must choose the right visual computations, and when to carry them out. How do we deal with the unpredictability of the natural world? Answer 1. - it’s not all that unpredictable and we’re really good at learning it. (Top Down) Answer 2. – There must be some Bottom Up mechanism for attracting attention. Lab 3.

8. Time fixating Intersection. “Follow the car.” or “Follow the car and obey traffic rules.” CarRoadsideRoadIntersection Shinoda et al. (2001) Detection of signs at intersection results from frequent looks. Top Down strategies: Learn where to look

9. Looming – a potential bottom up mechanism Neurons in Area MT sensitive to looming stimuli.

10. Experimental Question: How sensitive are subjects to unexpected salient events (looming)? General Design: Subjects walked along a footpath in a virtual environment while avoiding pedestrians. Do subjects detect unexpected potential collisions?

11. Virtual Walking Environment Virtual Research V8 Head Mounted Display with 3 rd Tech HiBall Wide Area motion tracker V8 optics with ASL501 Video Based Eye Tracker (Left) and ASL 210 Limbus Tracker (Right) D&c emily Video Based Tracker Limbus Tracker

12. Virtual Environment Bird’s Eye view of the virtual walking environment. Monument

13. 1 - Normal Walking: Avoid the pedestrians while walking at a normal pace and staying on the sidewalk. 2 - Added Task: Identical to condition 1. However, the additional instruction of following a yellow pedestrian was given Normal walking Follow leader Experimental Protocol

14. Pedestrians’ paths Colliding pedestrian path What Happens to Gaze in Response to an Unexpected Salient Event? The Unexpected Event: Pedestrians on a non-colliding path changed onto a collision course for 1 second (10% frequency). Change occurs during a saccade. Does a potential collision (looming) attract gaze?

15. Fixation on Collider

16. No Fixation During Collider Period

17. Probability of Fixation During Collision Period Pedestrians’ paths Colliding pedestrian path More fixations on colliders in normal walking. No effect in Leader condition Controls Colliders Normal Walking Follow Leader

18. Small increase in probability of fixating the collider. Failure of collider to attract attention with an added task (following) suggests that detections result from top-down monitoring. Why are colliders fixated?

19. Detecting a Collider Changes Fixation Strategy Longer fixation on pedestrians following a detection of a collider “Miss”“Hit” Time fixating normal pedestrians following detection of a collider Normal Walking Follow Leader

20. To make a top-down system work, Subjects need to learn statistics of environmental events and distribute gaze/attention based on these expectations. Subjects rely on active search to detect potentially hazardous events like collisions, rather than reacting to bottom-up, looming signals.

21. Possible reservations… Perhaps looming robots not similar enough to real pedestrians to evoke a bottom-up response.

22. Our Experiment: Allocation of gaze when driving. Does deviation in the flow field cause bottom up attraction of gaze? Drive along street with other cars and pedestrians. Instructions - drive normally, maintain speed, left lane, be aware of surroundings. Measure fixations on oncoming cars (swerving and controls)

23. Optic Flow

25. Person walking in a simulated environment. The spots on the wall(s) and floor would normally flow past the walker as he or she walked forward (visual flow), but in a simulator they can be made to move faster or slower than they normally would. If the spots are taken away, no visual speed is present. A person in a speed discrimination experiment would be presented with one set of spots moving at one speed (relative to the person) and, after a short blank, a second set of spots, moving at a different speed. The person’s task is to judge which speed was faster. Visual-flow speeds that are near walking speed look slower and are easier to tell apart when you are walking than when you are standing, though the speeds in the retinal image are the same. Idea is that humans subtract out the optic flow generated by self motion, making them more sensitive to object motion in the visual field.

28. Subjects must learn the probabilistic structure of the world and allocate gaze accordingly. That is, gaze control is model-based. Subjects behave very similarly despite unconstrained environment and absence of instructions. Control of gaze is proactive, not reactive, and thus is model based. Anticipatory use of gaze is probably necessary for much visually guided behavior.

29. Certain stimuli thought to capture attention bottom-up (eg Theeuwes et al, 2001 etc ) Looming stimuli seem like good candidates for bottom-up attentional capture (Regan & Gray, 200; Franceroni & Simons,2003).

31. Other evidence for detection of colliders? Do subjects slow down during collider period? Subjects slow down, but only when they fixate collider. Implies fixation measures “detection”. Slowing is greater if not previously fixated. Consistent with peripheral monitoring of previously fixated pedestrians.