1. Pattern recognition
(…and object perception)

2. Traditional Gestalt Psychology
Topic Outline:
Traditional Gestalt Psychology
Laws of Organization
Problems with this stuff
Solution to Pragnanz
II. Identifying “Figures”
Properties of figures/ground
Intrinsic/extrinsic contours
Illusory contours
III. Models of object recognition
a. Pandemonium Theory
b. Identification by Components
c. Marr’s Computational approach
d. Gibson’s “direct perception”
e. Connectionist/PDP models 

3. Traditional Gestalt Psychology
-originated as a counter to structuralist viewpoint (Koffka, Wertheimer, Kohler)
Structuralist: “the whole is equal to the sum of the parts”
Koffka: “the whole is other than the sum of the parts”
In other words, cannot fully explain object perception based solely on a description of the elements of that perception:
emergent properties
One of the first examples: Phi phenomenon

6. Emergent properties

7. Emergent properties

8. Emergent properties

9. Emergent properties

10. Emergent properties

11. Emergent properties

12. Emergent properties

13. Emergent properties

14. Emergent properties

15. Gestalt laws of Organization

16. Gestalt laws of Organization
Law of Proximity

17. Gestalt laws of Organization
Law of Similarity

18. Gestalt laws of Organization
Good Continuation

19. Gestalt laws of Organization
Closure

20. Gestalt laws of Organization
Common fate

21. Gestalt laws of Organization
Common fate

22. Gestalt laws of Organization
Common fate

23. Gestalt laws of Organization
Common fate

24. Gestalt laws of Organization
Common fate

25. Gestalt laws of Organization
Common fate

26. Gestalt laws of Organization
Common fate

27. Gestalt laws of Organization
All the other laws are subsumed under the Law of Pragnanz:
-patterns will be perceived in such a way that the resulting structure is as simple as possible
Problems:
1. Descriptive, with no indication of underlying mechanisms of how it is accomplished
2. Lack of predictive power

29. Gestalt laws of Organization
All the other laws are subsumed under the Law of Pragnanz:
-patterns will be perceived in such a way that the resulting structure is as simple as possible
Problems:
1. Descriptive, with no indication of underlying mechanisms of how it is accomplished
2. Lack of predictive power
3. No definition of “simplest”

30. More recent research tries to clear up that last one:
“simplest” = least amount of data/information required
2-d = 26 angles
3-d = 24 angles

31. More recent research tries to clear up that last one:
“simplest” = least amount of data/information required
2-d = 18 angles
3-d = 24 angles

32. II. Identifying “Figures”

33. II. Identifying “Figures”
Relative to “grounds”, figures are:
-in front
-a clearly defined shape
-higher perceived contrast
-processed in greater detail
-the process involves distinguishing intrinsic from extrinsic contours

35. -can give rise to complex perceptions
II. Identifying “Figures”
-subjective/illusory contours:
-can give rise to complex perceptions

36. -can give rise to complex perceptions
II. Identifying “Figures”
-subjective/illusory contours:
-can give rise to complex perceptions

37. -can give rise to complex perceptions
II. Identifying “Figures”
-subjective/illusory contours:
-can give rise to complex perceptions

38. III. Models of object recognition
-bottom-up versus top-down distinction
-earliest model: template matching
-discarded due to lack of flexibility, too much storage needed

39. III. Models of object recognition
1. Selfridge’s Pandemonium model

40. III. Models of object recognition
1. Selfridge’s Pandemonium model

41. III. Models of object recognition
1. Selfridge’s Pandemonium model
Problems:
Defining a feature
Overly data-driven (no room for context effects)
Decision demon will need stored templates to compare to and recognize the object
Was originally designed for language only

42. III. Models of object recognition
1. Selfridge’s Pandemonium model
2. Biederman’s identification by components model
-another feature model, but the features are now “Geons”
-small set of shapes that, in combination, can represent objects

43. III. Models of object recognition
1. Selfridge’s Pandemonium model
2. Biederman’s identification by components model
-again, not much room for context to play a role
-again, the job of actually identifying the object is left vague

44. III. Models of object recognition
1. Selfridge’s Pandemonium model
2. Biederman’s identification by components model
3. Marr’s computational approach
-stages of processing from simple to complex
-primal sketch: edges and boundaries identified based on intensity differences (called zero crossings)
-2.5d sketch: that info is processed using grouping rules into larger groups, surfaces and distances interpolated into image (wire frame)
-3d sketch: textures added and volume applied

45. III. Models of object recognition
1. Selfridge’s Pandemonium model
2. Biederman’s identification by components model
3. Marr’s computational approach
4. Gibson’s direct perception
-question is, how do we perceive a constantly changing stimulus array with any consistency?
-light array contains sufficient information that we don’t need internal models or deconstructions
-changing array still has invariant properties
-affordances can be directly perceived
-deletion/accretion, optic flow, motion parallax
-not enough detail to be taken seriously

46. III. Models of object recognition
1. Selfridge’s Pandemonium model
2. Biederman’s identification by components model
3. Marr’s computational approach
4. Gibson’s direct perception
5. Connectionist/PDP models
-neural networks modeled via interconnected nodes
-combinations of nodes converge to give rise to complex codings being programmed at higher layers

48. Different from Selfridge in several ways:
-connections can occur within a single level
-connections can be excitatory or inhibitory
-system can learn through back-propogation and application of delta rule
-apply input, assign initial weights based on nodes firing at the same time (Hebb rule)
-output compared with desired input to compute the degree of error
-this difference is used to compute a delta rule for changing the weights
-reapply the stimulus and recalculate delta, update weights again
-keep cycling through until detection is accurate

49. Context effects

53. No matter how hungry you are, try not to….

55. Is there a sink in the picture?

57. Is there a sink in the picture?

60. Duncker Wheel

61. Duncker Wheel

62. McGurk Effect

63. Word superiority effect
GZQRM
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What were the letters?

64. Word superiority effect
WORKS
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