Action Video Games and “Learning to Learn” C. Shawn Green Daphne Bavelier Alex Pouget Supported by NEI, ONR, The James S. ...
Specificity of Perceptual Learning <ul><ul><li>Training leads to lasting improvement </li></ul></ul>Fiorentini, A. & Berar...
Action Video Games <ul><li>In contrast to the typical specificity of learning, our research has demonstrated that action v...
Action Video Games
Temporal Processing Green and Bavelier,  Nature  (2003)
Temporal Processing Green and Bavelier,  Nature  (2003) B L R S W C Q X Y J G D T P
Temporal Processing Green and Bavelier,  Nature  (2003) 100 200 Time since white letter (ms) 400 500 600 700 800 300
Spatial Processing Green and Bavelier,  Psychological Science  (2007) Crowding Spatial Processing T T T T T T T T T
Spatial Processing Crowding Green and Bavelier,  Psychological Science  (2007) 0 10 25 Eccentricity (deg)
What Do Action Video Games Teach? <ul><li>The list of tasks on which performance is improved by action video game experien...
A Single Mechanism? Dye, Green and Bavelier,  Current Directions in Psychological Science  (2009) VGP RT  = NVGP RT *.88 R...
A Single Mechanism? <ul><li>Improvement in the ability to use sensory data to perform probabilistic inference </li></ul>
Perception as Inference <ul><li>The “problem” of perception  </li></ul><ul><li>The brain must determine what actual “world...
Perception as Inference O y x
Perception as Inference Infinite number of 3D shapes can map to the same 2D image s x z y
Perception as Inference Deer or mailbox? p (deer| sensory data)?
Action Video Game Experience Improves Inference
Action Video Game Experience Improves Inference How close is subjects’ estimate of:  p (deer| sensory data) to the actual ...
Action Video Game Experience Improves Inference <ul><li>Result: Action video game experience leads to more efficient use o...
Learn to Learn? p (deer| sensory data)  α  p (sensory data | deer) <ul><li>p (sensory data | deer): “likelihood” </li></ul...
Implications <ul><li>Rehabilitation </li></ul><ul><li>Slowing/reversing the normal decline with aging </li></ul><ul><li>Su...
Thank You Acknowledgements PhD Advisor Daphne Bavelier Bavelier Lab: Rebecca Achtman Ashley Anderson Matt Dye David Feder ...
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Action Video Games and “Learning to Learn”

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  • Specific even for eye – a problem with goal of rehabilitation or job training, etc
  • Experience here really means “experience” – we always start out by testing VGPS and NVGPs, but then do training study
  • While there are no hard and fast quantitative rules that one can apply to perfectly classify video games into their various genres, action video games all share a set of qualitative features including extraordinary speed (both in terms of very transient events and in terms of the velocity of moving objects), a high degree of perceptual, cognitive, and motor load in the service of an accurate motor plan (multiple items that need to tracked and/or kept in memory, multiple action plans that need to be considered and quickly executed typically through precise and timely aiming at a target), unpredictability (both temporal and spatial), and an emphasis on peripheral processing (with important items most often appearing away from the center of the screen). First-person shooters and third-person shooters are prototypical examples of action video games. Hence, when we discuss ‘action video games’ or ‘action video game players’, we are referring specifically to this category and not to all video games in general. Indeed, one remarkable feature of the work reviewed below is that it demonstrates that not all types of complex and rich visual environments equally foster perceptual learning. Understanding those components specific to action video games that promote learning across a wide range of visual tasks may hold the key to the development of general-learning training paradigms.
  • NO DIFFERENCE IN ACCURACY! NOT A SPEED-ACCURACY TRADEOFF
  • NO DIFFERENCE IN ACCURACY! NOT A SPEED-ACCURACY TRADEOFF
  • Again a preview - through Bayes rule this computation requires knowledge of the statistics of the evidence - p(e | c) - likelihood -
  • Best you can do is take each snippet of sensory data and compute the probability – the closer you are to the real probability, the “better”
  • Again a preview - through Bayes rule this computation requires knowledge of the statistics of the evidence - p(e | c) - likelihood -
  • Again a preview - through Bayes rule this computation requires knowledge of the statistics of the evidence - p(e | c) - likelihood -
  • Again a preview - through Bayes rule this computation requires knowledge of the statistics of the evidence - p(e | c) - likelihood -
  • Action Video Games and “Learning to Learn”

    1. 1. Action Video Games and “Learning to Learn” C. Shawn Green Daphne Bavelier Alex Pouget Supported by NEI, ONR, The James S. McDonnell Foundation
    2. 2. Specificity of Perceptual Learning <ul><ul><li>Training leads to lasting improvement </li></ul></ul>Fiorentini, A. & Berardi, N. (1980). Perceptual learning specific for orientation and spatial frequency. Nature, 287, 43-44. <ul><ul><li>But only for the trained orientation and spatial frequency </li></ul></ul>
    3. 3. Action Video Games <ul><li>In contrast to the typical specificity of learning, our research has demonstrated that action video game experience transfers broadly </li></ul>
    4. 4. Action Video Games
    5. 5. Temporal Processing Green and Bavelier, Nature (2003)
    6. 6. Temporal Processing Green and Bavelier, Nature (2003) B L R S W C Q X Y J G D T P
    7. 7. Temporal Processing Green and Bavelier, Nature (2003) 100 200 Time since white letter (ms) 400 500 600 700 800 300
    8. 8. Spatial Processing Green and Bavelier, Psychological Science (2007) Crowding Spatial Processing T T T T T T T T T
    9. 9. Spatial Processing Crowding Green and Bavelier, Psychological Science (2007) 0 10 25 Eccentricity (deg)
    10. 10. What Do Action Video Games Teach? <ul><li>The list of tasks on which performance is improved by action video game experience is long and still expanding </li></ul><ul><li>Does this reflect many truly independent improvements? Or an improvement in ONE single skill that underlies all of these tasks? </li></ul>
    11. 11. A Single Mechanism? Dye, Green and Bavelier, Current Directions in Psychological Science (2009) VGP RT = NVGP RT *.88 R 2 = .98
    12. 12. A Single Mechanism? <ul><li>Improvement in the ability to use sensory data to perform probabilistic inference </li></ul>
    13. 13. Perception as Inference <ul><li>The “problem” of perception </li></ul><ul><li>The brain must determine what actual “world” created the pattern of data that it observes </li></ul><ul><li>Because the data is always ambiguous, the best the brain can do is compute the probability that various “worlds” created the pattern of data </li></ul>
    14. 14. Perception as Inference O y x
    15. 15. Perception as Inference Infinite number of 3D shapes can map to the same 2D image s x z y
    16. 16. Perception as Inference Deer or mailbox? p (deer| sensory data)?
    17. 17. Action Video Game Experience Improves Inference
    18. 18. Action Video Game Experience Improves Inference How close is subjects’ estimate of: p (deer| sensory data) to the actual value?
    19. 19. Action Video Game Experience Improves Inference <ul><li>Result: Action video game experience leads to more efficient use of sensory evidence. </li></ul><ul><li>Essentially, each little piece of evidence moves them further toward the true probability estimate. </li></ul>
    20. 20. Learn to Learn? p (deer| sensory data) α p (sensory data | deer) <ul><li>p (sensory data | deer): “likelihood” </li></ul><ul><li>isn’t known before the experiment </li></ul><ul><li>must be learned through experience </li></ul><ul><li>Conclusion </li></ul><ul><li>action video game experience teaches individuals to learn how bits of sensory data change the probability of outcomes </li></ul>
    21. 21. Implications <ul><li>Rehabilitation </li></ul><ul><li>Slowing/reversing the normal decline with aging </li></ul><ul><li>Surgeons/soldiers </li></ul><ul><li>Education??? </li></ul>
    22. 22. Thank You Acknowledgements PhD Advisor Daphne Bavelier Bavelier Lab: Rebecca Achtman Ashley Anderson Matt Dye David Feder Nina Fernandez Renjie Li Mike Sugarman Alex Pouget Jeff Beck Weiji Ma Current Advisors: Daniel Kersten Paul Schrater

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