1. Sensation & Perception

2. Motion Vision I: Basic Motion Vision

3. Organization 1.How to do well in class 2.Some Clarifications on V1 3.Motion Vision

4. 1. How to do well in class?

5. 1.: Attend class! Class attendance correlates with GPA typically around 0.3 (Schuman et al., 1985) but can reach correlations of up to 0.4! (Larrieu, 2004)

6. 2.: Study! (a lot) The effect of studying seems to be highly nonlinear. It only makes a significant difference if one studies more than 5 hours per day! (Schuman et al., 1985).

7. 3. Don’t drink! Effects of alcohol consumption on GPA have been consistently found to be negative and significant (Finnell & Jones, 1975). The magnitude of the correlations is in the ballpark of -0.2 (Larrieu, 2004).

8. 4. Don’t do drugs! Effects of Marihuana consumption on GPA have been found to be significantly negative. The magnitude of the correlation is in the ballpark of -0.25 (Larrieu, 2004).

9. Summary Attend as many classes as possible Study at least 5 hours a day (outside of class) Don’t drink Don’t do drugs  Revenge of the Nerds. Caveat: The effects outlined above might be non-independent and might not be causal.

10. 2. Some clarifications on V1 Fourier Decomposition of an Image in V1? But why? How does this relate to actual images?

11. Fouriers theorem EVERY waveform can be decomposed into simple sine-waves with the right amplitude and frequency. EVERY waveform can be synthesized by adding sine-waves with the right amplitude and frequency.

13. Picture representation in V1

15. Filter!

16. 3. Motion Vision

17. Why care about Motion?

18. Most animals have some form of motion vision (vs. Color) A large part of the brain is devoted to motion processing in these animals

19. Motion processing in the brain

20. Why such an emphasis on motion?

21. Motion as a rich source of survival-relevant information : Ecologically relevant information Breaks camouflage Heading perception, time to collision Image segmentation, figure/ground Object perception, structure from motion Depth perception Attention guidance

22. Some demonstrations... 1.Structure from motion 2.Breaking camouflage 3.Depth (Parallax)

23. Structure from Motion

24. Breaking Camouflage

25. Depth perception

26. Motion = Displacement in space over time?

27. Color = Wavelength? Monnier & Shevell (2003, 2004)

28. Motion as a separate perceptual dimension The motion aftereffect (MAE) Apparent motion Motion illusions, induced movement Motion = Displacement in space over time?

29. Some demonstrations...

30. The motion aftereffect

31. Apparent motion

32. Induced motion

33. How hard can it be?

34. Challenges Relation between movement of luminance on retina and object movement in world is weak. Apparent motion, flicker, correspondence problem Image segmentation

35. The motion correspondence problem

36. Yet...

37. Basic principles Reichardt detectors in V1 integrate luminance signals from the LGN. Retina  LGN  V1

38. Reichardt detectors V1 cell (integrator) LGN cells (input) t Hz V1 response 1 * 2 * 3 ** 4 5 6 X

39. Reichardt detectors This model allows the extraction of motion information by nonlinear summation of the luminance inputs. It achieves Direction and Speed selectivity of the neural response It is physiologically plausible.

40. Review: Orientation tuning in V1 V1 neurons have orientation preferences. These can be understood by analyzing the receptive field properties of the respective cell.

41. Review: Orientation tuning in V1 x y + - - x y + - - x y + - - Hz

42. Space-time plots Motion as orientation/tilt in space-time x t x t x t x t

43. Motion as a tilt in space-time V1 neurons tuned for orientation in space time  Extracting motion information by Direction, speed selectivity. x t + - - x t + - - x t + - - x t + - -

44. Adding direction to speed x y “Tilted hot-dog” y t x

45. So computationally, motion detection is essentially the same as edge detection. The RFs are just oriented in different dimensions. How to construct a space-time receptive field?

46. Constructing the Space-Time RF A B C D E x Delay decreases from A to E t X (A, B, C, D, E) The space- time RF emerges

47. Single neuron response Hz s Time course in response to motion stimulus Stimulus

48. Single neuron level Can tell what the preference of each is, given we know what object is moving in which direction. n = 1

49. Examples Tuning curves

50. Some demonstrations Motion receptive field in single MT neuron

52. Some demonstrations Gaussian direction tuning in single MT neuron

54. Population response Brain wants to know about the properties of objects in the world, given activation states in the brain. Inference possible if preferences of each neuron are known by brain (Labeled line) Direction-Preference of respective neuron in ° n = many

55. Summary Motion perception is very common among animals. It´s evolutionary survival value is high. Motion detection is an inference made by the brain about the dynamics of objects in the world

56. Summary The perception of motion is a fundamental perceptual quality. It is not derived from other qualities. Motion extraction in V1 can be understood by the concepts of Reichardt detectors and Orientation tuning in space-time.

57. There is much more...

58. Multi-step model of motion perception 1: Local measurement of motion information (direction, speed), esp. V1 2: Constrained integration of local motion information (particularly in MT) 3: Utilizing motion information for action guidance (e.g. MST, FEF, etc.)

59. Higher upward: Perception of biological motion Perception of structure from motion Perception of heading (optic flow), taking eye movements into account, etc.

60. Motion processing in the brain

61. Thank you!