1. Neuropsychology of Vision Anthony Cate April 19, 2001

2. Dolphins have amazing brains!
Holy cow!

3. The problem of vision and how it relates to PDP modeling
Narrowing down the problem:
Perception vs. Action
Look at the P, the D and the P

4. Parallel?
The visual system is parallel on at least two levels:

5. The early visual system has a massively parallel architecture

6. Visual input from early visual areas is processed in 2 distinct ways in parallel

7. The dorsal and the ventral stream can be dissociated by brain injury

8. Distributed?
No grandmother cells.

9. Distributed?
Localist or distributed representations of form?

10. Processing?
How best to describe the processing performed by higher-order brain areas?
In terms of function or computation?

11. Is there a limit to the parallel stage of visual processing?

12. Is there a limit to the parallel stage of visual processing?

13. Effects of context at all stages of processing.
High level:
Model via constraint satisfaction?

14. Effects of context at all stages of processing.
Extremely low level:
The result of either feedback from “higher layers” or from lateral connections.

15. Point:
Think of vision not as
- reproducing patterns of light
- matching inputs to stored templates
But as a process wherein visual input interacts with many kinds of information stored in a network.
No single process.

16. A short PDP tour of the visual system:
Attention (Posner vs. Cohen)
Object Recognition (RBC vs. Edelman)
Category specificity (Kanwisher vs. Gauthier)

17. Visual attention as the product of a distributed network of brain areas:

18. Damage to the parietal lobe produces what appears to be a “disengage” problem:

19. But how to characterize the behavior produced by this network?
By assigning functions to each anatomical region…
Posner et al., 1984

20. … or by describing the computational process by which the network might produce the behavior?
Cohen et al., 1994

21. Damage to the simple model also produces a “disengage” problem, without the need for any subdivision of processing:

22. Disadvantages of the PDP account:
The “disengage” model exists for a reason: different lesions produce somewhat different kinds of disorders.
Perhaps inappropriate to capture entire network in such a simple model.

23. Object recognition
How do we generalize across different viewpoints of one objects?
How do we generalize across the many individual objects that make up a category?
Great differences in the images produced by both of these factors

24. Marr’s solution:
Represent 3-D volumes, not 2-D images

25. Marr was unfortunate, so…

26. Recognition by Components (RBC)

27. Recognition by Components (RBC)

28. Recognition by Components (RBC)

29. Recognition by Components (RBC)
JIM
(“John & Irv’s Model”)
Hummel & Biederman, 1992

30. Recognition by Components (RBC)

31. Recognition by Components (RBC)

32. Recognition by Components (RBC)
So perhaps object recognition not based on representations of 3-D structure, but on 2-D views.

33. Difficulties in representing objects via 2-D images:
How to associated different views of same object?
How to generalize based on a view?
The space of all possible 2-D images is of a much higher dimensionality than the space of geons.

34. Dimensionality reduction

35. Dimensionality reduction by PCA

36. Potential problem with principle component representations:
What about similarity?
As long as second-order isomorphism preserved, not necessarily a problem

37. Second-order isomorphism

38. Autoencoders can do PCA

39. Autoencoders can do PCA

46. Problem with stored views approach:
Do we represent prototypes?
How do we “decide” which prototypes to represent?
(Is there really a “chorus?”)

47. Do we represent prototypes?
At least for faces, yes!

48. Do we represent prototypes?
How distributed are prototype representations?

49. Sparse population coding of faces

50. Sparse population coding of faces:
Suggests that neurons do represent a distributed code, but…
Only a very small subset of neurons necessary to encode a particular category of stimuli (faces)
Does this imply that the brain is organized in terms of function?

51. PCA and prototype encoding discussed above is entirely holistic.
The image is not segmented into parts which are represented independently.
This is not how we represent most images, except for faces.

52. Larry

55. Tanaka & Farah, 1993
Subjects more accurate at identifying whole faces than parts
Isolated part
Whole face

56. Tanaka & Farah, 1993
This part/whole difference did not hold for other kinds of stimuli: scrambled faces, inverted faces, houses

57. So faces are special, then, right?
Perhaps, but examine what faces as a class of stimuli have in common with other classes of stimuli
Subordinate level categorization
(We don’t look at a person and say “face!”)
Expertise
(We are all “face experts”)

58. Subordinate level categorization and expertise lead to more holistic representations of non-face objects

59. “Greeble experts” show the whole-over-part advantage that is found for faces

60. Greebles and several other kinds of “subordinate” stimuli activate the “fusiform face area”

61. What does this have to do with PDP?
Describe functions both of ensembles of neurons and gross brain areas in terms of computational principles (i.e. PCA analysis), not functional goal.
No accident that most object-selective cells/regions found are face-selective?

62. Problem:
How are objects with recognizable parts represented?
Unclear:
Ensembles of prototypes of parts?
Would these be tantamount to geons?

63. Resources:
Object recognition:
Face specificity: