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Dr. Javier Gonzalez-Sanchez [email protected] www.javiergs.info o ffi ce: 14 -227 CSC 486 Human-Computer Interaction Lecture 08. Visual Attention

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Previously …

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Subscriber 2.0 3 Brain View Controller Blackboard PostOffice Model

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https://github.com/CSC3100/MQTT

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Eye 5

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Data 6

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Visual Attention

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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

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1. Salience: Color, Dimension, Orientation, Size 9 https://www.kolend a .io/guides/visu a l- a ttention

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2. Motion: onset, looming, unpredictable, depicted 10

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2. Motion: capacity, body, natural, 11

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3. Agents: faces, bodies, animals 12

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4. Spatial Cues: Eye gaze, pointing, arrows 13

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4. Spatial Cues: directional words 14

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5. High Arousal: Threat 15

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5. High Arousal: Threat 16 (Algom, Chajut, & Lev, 2004).

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5. High Arousal: Sex 17

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6. Unexpectedness: Novelty 18

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7. Self-relevance: your name, your face, 19 Faces are equally as powerful as names (Tacikowski & Nowicka, 2010).

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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

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8. Goal-relevant: goal-directed 21 https://www.kolend a .io/guides/visu a l- a ttention

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Thoughts?

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No content

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Questions 24

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Assignment 01

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Part 1. Select a Stimuli 26 1. Image(s) to Analyze with Eye Tracking Technology

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Part 1. Update your Software 27 2. Subscribe to Eye Tracking data (parse JSON) and store the data (CSV recommended)

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Part 3. Schedule your time to use Tobii device 28 1. Collect data

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Part 4. Report Results 29 2. Analyze your data and submit your report

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Questions 30

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CSC 509 Software Engineering Javier Gonzalez-Sanchez, Ph.D. [email protected] 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.