Anatomical connections and receptive fields

1. Anatomical connections and receptive fields Image Analysis in the Visual System

2. Schematic representation of the ventral visual pathway

6. Conjunction search and the binding problem

7. Visual tasks with differing demands on visual processing Pre- attentive Attentive

8. Anne Treisman’s Feature Integration Theory (1980)

9. The Salience Map Early Vision

10. BUT • complex features • pop-out too: • 3D images Enns & Rensink, 1990

11. complex feature pop-out: depth & • shape-from-shading Ramachandran, 1988

13. The need for spatial attention

14. Attention effects in V4/IT:shrinking the RF FP Effective stimulus Ineffective stimulus RF Effective stimulus Effective stimulus

15. Feature based attention effects

16. Feature-based attention in MT

17. Visual masking: Interrupting the feedback loop Masking interrupts recurrent interactions

18. Masking effects in V1 Population average Single unit Macknik & Livingstone, 1998

19. Animal/ non animal speeded response Behavioral performance Thorpe et al., 1998

20. Is there a car? RSVP1

22. Evoked potential data EEG scalp measurements

24. Averaged event related potentials Difference in EEG can be detected at around 150ms after stimulus onset

25. Hierarchy of visual processing stages? For each Feed-forward path there’s a feedback pathway

26. Reverse Hierarchy Theory Perception proceeds as a countercurrent along the cortical hierarchy. “Vision at a glance” vs. “vision with scrutiny”. (Ahissar & Hochstein, Nature 1997 Hochstein & Ahissar, Neuron 2002)

27. Surround effects in V1 V1 cells are sequentially selective for various aspects of a stimulus orientation pref Non-pref Fig.2. V1 cells are sequentially selective for various aspects of a stimulus. (a) Responses are compared with the receptive fields (RF) (circles, marked a to f) of V1 neurons at different locations of a texture. Comparing responses to differently orientated textures (c versus f, left graph, yellow shading indicates difference) shows that cells are selective for orientation of textures at 55 ms (arrow indicates moment of first significant difference). The same cells are selective for the boundary between figure and ground (b versus e, middle graph) at 80 ms, and show an enhanced response when the RF covers the figure surface compared to the background surface at 100 ms, even though the stimulus within the RF is identical in both situations (a versus d, right graph).

28. The figure-ground problem

29. Co-linearity is a strong grouping cue

30. Neurons in V1 are sensitive to co-linearity effects

31. Curve tracing is a serial task

32. Response enhancement in area V1 during curve tracing

33. Attention effects in V1: curve tracing example

34. Reverse Hierarchy Theory Perception proceeds as a countercurrent along the cortical hierarchy. “Vision at a glance” vs. “vision with scrutiny”. (Ahissar & Hochstein, Nature 1997 Hochstein & Ahissar, Neuron 2002)

35. Reverse Hierarchy & Perceptual learning Perceptual learning can be seen for elementary tasks 1. Detection of primitives can be improved with training in adult humans 2. Improvement has a characteristic pattern of dynamics and stimulus specificity 3. These can tell us about how the system operates.

36. Perceptual learning: Is there an odd element?

37. Detecting the presence of an oddly oriented bar (Ahissar & Hochstein, 1996)

38. How specific is this improvement? Sometimes very specific: to the eye, orientation and retinal position trained. Ahissar & Hochstein, 1998

39. Which led people to claim that this learning occurs at low levels of the visual pathways (where neuronal receptive fields are small and orientation selective) But sometimes learning is very general – consistent with modifications at high levels of the visual pathways What determines degree of generalization? Is there a rule, consistent pattern?

40. threshold (SOA)

41. Easy positions

42. 140 100 60 120 20 0 10 20 session # 100 80 60 40 20 0 10 20 learning specificity for 4 training conditions 80 o o all pos 90 90 easy 60 o easy hard 30 hard o 16 40 2 all # of target positions 220 20 o 0 10 20 o 2 pos 30 all pos 30 odd element anywhere (30o difference) 180 odd element in 1 of 2 positions (30o difference) 140 100 60 swap 20 o 2 pos 16 transfer specificity swap

43. 140 100 60 120 20 0 10 20 session # 100 80 60 40 20 0 10 20 learning specificity for 4 training conditions 80 o o all pos 90 90 easy 60 o easy hard 30 hard o 16 40 2 all # of target positions 220 20 o 0 10 20 o 2 pos 30 all pos 30 odd element in 1 of 2 positions (30o difference) 180 140 100 60 swap 20 o 2 pos 16 (16o difference) transfer specificity swap

44. b odd element anywhere (90o difference) odd element in 1 of 2 positions (30o difference) (30o difference) (16o difference)

45. Summary: When training conditions are easy – learning is general, When training conditions are difficult – learning is specific Thus – the same task can be learned at high levels (easy) and at low levels (when difficult). Knowing neuronal receptive field properties guides us in predicting patterns of transfer and specificity (e.g. when difficult –it is specific to position, orientation and size).

46. Easy SOAs are learned first and show greater transfer SOA= 83 66 50 33ms

47. Putting it all in a unified conceptual framework- Reverse Hierarchy Theory (Ahissar & Hochstein, 1997): Visual processing and representations are hierarchical. While stimulus processing is achieved bottom-up, perceptual learning is achieved top-down (along the reverse hierarchy) and subserved by the massive feedback connections.

48. Reverse Hierarchy Theory Perception proceeds as a countercurrent along the cortical hierarchy. “Vision at a glance” vs. “vision with scrutiny”. (Ahissar & Hochstein, Nature 1997 Hochstein & Ahissar, Neuron 2002)

49. Initial improvement is broad. Thus, initial learning is high-level and general. However, it only solves easy cases. Learning difficult cases is more specific. Thus, learning of difficult cases occurs at lower-levels along the processing hierarchy. Learning begins with easy and continues with difficult cases. Thus, learning begins high and proceeds to lower-level areas. With no initial easy cases, no learning of difficult cases occurs even with massive training. Low-level learning requires allocation of the appropriate low-level populations. This is done using a backward search that requires an initial starting point (Eureka – one easy example)