CSC486 Lecture 08

Human-Computer Interaction
Visual Attention
(202502)

Transcript

1. Dr. Javier Gonzalez-Sanchez javiergs@calpoly.edu www.javiergs.info o ffi ce: 14 -227 CSC 486 Human-Computer Interaction Lecture 08. Visual Attention
2. Previously …
3. Subscriber 2.0 3 Brain View Controller Blackboard PostOffice Model
4. https://github.com/CSC3100/MQTT
5. Eye 5
6. Data 6
7. Visual Attention
8. What captures attention? • We only perceive a fr a ction of stimuli th a t enter our consciousness (Mor a n & Desimone, 1985) • M a ny stimuli enter our br a in without being detected consciously • [Surviv a l] w a s predic a ted on the a bility to e ff iciently loc a te critic a lly import a nt events in the surroundings. (Öhm a n, Flykt, & Esteves, 2001, p. 466). • There a re br a in regions th a t monitored the surrounding environment for critic a l stimuli (Cosmides & Tooby, 2013, p. 205). • We a re more likely to fe a r events a nd situ a tions th a t provided thre a ts to the surviv a l of our a ncestors, such a s potenti a lly de a dly pred a tors, heights, a nd wide open sp a ces, th a n to fe a r the most frequently encountered potenti a lly de a dly objects in our contempor a ry environment (Öhm a n & Minek a , 2001, p. 483) 8
9. 1. Salience: Color, Dimension, Orientation, Size 9 https://www.kolend a .io/guides/visu a l- a ttention
10. 2. Motion: onset, looming, unpredictable, depicted 10
11. 2. Motion: capacity, body, natural, 11
12. 3. Agents: faces, bodies, animals 12
13. 4. Spatial Cues: Eye gaze, pointing, arrows 13
14. 4. Spatial Cues: directional words 14
15. 5. High Arousal: Threat 15
16. 5. High Arousal: Threat 16 (Algom, Chajut, & Lev, 2004).
17. 5. High Arousal: Sex 17
18. 6. Unexpectedness: Novelty 18
19. 7. Self-relevance: your name, your face, 19 Faces are equally as powerful as names (Tacikowski & Nowicka, 2010).
20. 8. Goal-relevant: no-goal • People a re more likely to notice stimuli when they don’t h a ve a n a ctive go a l. Their cognitive lo a d is lower, which le a ves sp a re room for a ttention (C a rtwright- Finch & L a vie, 2007). • 20
21. 8. Goal-relevant: goal-directed 21 https://www.kolend a .io/guides/visu a l- a ttention
22. Thoughts?
24. Questions 24
25. Assignment 01
26. Part 1. Select a Stimuli 26 1. Image(s) to Analyze with Eye Tracking Technology
27. Part 1. Update your Software 27 2. Subscribe to Eye Tracking data (parse JSON) and store the data (CSV recommended)
28. Part 3. Schedule your time to use Tobii device 28 1. Collect data
29. Part 4. Report Results 29 2. Analyze your data and submit your report
30. Questions 30
31. CSC 509 Software Engineering Javier Gonzalez-Sanchez, Ph.D. javiergs@calpoly.edu Fall 2024 Copyright. These slides can only be used as study material for the class CSC509 at Cal Poly. They cannot be distributed or used for another purpose.