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201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
201103 emotional impacts on digital media
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201103 emotional impacts on digital media

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  • 1. Emotional Impacts of Digital Media
    Robert Atkinson
    Maria-Elena Chavez-Echeagaray
    Robert M. Christopherson
    David Gibson
    Javier Gonzalez-Sanchez
    This research was supported by Office of Naval Research under Grant N00014-10-1-0143 awarded to Dr. Robert Atkinson
  • 2. Research Questions
    At what moments does a digital media user experience identifiable physical, emotional and cognitive attributes?
    Are those facilitating or impeding moment-by-moment learning and performance?
  • 3. Research Questions
    What patterns are found within & between sensors?
    How do these patterns relate to baseline and experimental activities?
  • 4. Sensors
    Wireless EEG
    Facial muscles, emotional clusters, raw EEG
    Wireless Galvanic Skin Conductance
    Arousal level
    Eye Tracker
    Gaze-point, duration, mouse-clicks
    Haptics
    Button presses, head tilt
  • 5. Anatomy of the System
  • 6. Anatomy of theSystem
    Wireless Emotiv® EPOC Headset(report data with intervals of 125 ms).
    The output includes 14 sensors or channels (7 on each brain hemisphere: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, and AF4) and two values of the acceleration of the head when leaning (gyrox and gyroy).
    Also it reports Engagement, Boredom, Excitement , Frustration, Meditation.
    Javier Gonzalez-Sanchez | Maria-Elena Chavez-Echeagaray
  • 7. Anatomy of theSystem
    Tobii®Eye Tracker report data with intervals of 100 ms.
    provides data concerning attention direction and time of focus during individual use of a computer.
    Javier Gonzalez-Sanchez | Maria-Elena Chavez-Echeagaray
  • 8. Anatomy of theSystem
    MindReader Software from MIT Media Lab. This is about inferring a person mental state from non-verbal cues. Visual system infers mental states from head gestures and facial expressions in a video stream in real-time at data intervals of 100 ms approximately.
    This system infer six emotions: agreeing, concentrating, disagreeing, interested, thinking and unsure.
    Javier Gonzalez-Sanchez | Maria-Elena Chavez-Echeagaray
  • 9. Anatomy of theSystem
    Skin electrical conductance sensordesigned by MIT Media Lab for Arousal Sensing. Reports conductance data in intervals of 500 ms
    This sensor measures the electrical conductance of the skin, which varies with its moisture level that depends on the sweat glands, which are controlled by the sympathetic, and parasympathetic nervous systems.
    Javier Gonzalez-Sanchez | Maria-Elena Chavez-Echeagaray
  • 10. Method
    20 users play Guitar Hero sessions
    Easy and hard songs
    10 minutes of recording per session
    Over 76000 records per user per session
  • 11. Video
  • 12. Data
  • 13. Thinking States
    Rise in
    uncertainty
    and interest
    During thinking
    Agreement & concentration drop
  • 14. Analysis
    Inductive structural equation modeling
  • 15. Analysis
    Network analysis
    Adjacency tables
    Centrality
    Digraphs
  • 16. Analysis
    Digraphs illustrate structural relationships in the causative factors during a time slice.
  • 17. Findings
    Near real-time displays are possible that relate to hidden psychological variables
    Time slices of performance can be analyzed to give statistically accurate operational models of brain states during specific tasks
  • 18. Findings
    Learner analytics can use these analyses to
    Personalize digital media experiences
    Control devices (e.g. cars, computers, lighting)
    Study emotion and learning
    Research seductive details in hypertexts
    Improve affective & metacognitive tutors

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