1. Course Overview Knowledge ch. 3: Vision. How are objects recognized? -It looks easy but it’s not The Brain Acquisition (perception) ch.4: Attention. Use

2. Object Recognition

4. Visual System Recognizes: –Object Identities (man, woman, child, oar, lake) –Spatial Layout of Scene –Properties of Surfaces(color, texture, etc.) Are these real psychological dimensions? ( What about Small vs. big? Alive vs. Inanimate? Tool vs. Non-tool? soft vs. hard? ) - Are these dimensions processed independently by the mind/brain? (after all, our subjective experience is that of an integrated scene)

5. Specific Disorders suggest functionally independent types of visual analysis 1. Visual Agnosia:(object identity) loss of ability to recognize objects preserved ability to navigate, reach Bilateral damage to occipital/temporal Prosopagnosia (specific to face recognition) 2. Balint’s Syndrome:(spatial layout) inability to navigate, reach preserved ability to recognize objects damage to parietal areas 3. Cerebral Achromatopsia (color) inability to discriminate colors distinct from color blindness--Color blindness results from abnormalities in the photoreceptors of the eye. But cerebral achromatopsia results from damage to posterior visual areas in the brain

6. fMRI studies have confirmed the anatomical Segregation of these functions 1. Object recognition:(lateral occipital cortex) face recognition (medio-temporal) 2. spatial layout and spatial attention parietal areas 3. Color medio-temporal

7. What is difficult about object recognition?

8. 1. The visual system must carry out “image segmentation”, but object boundaries are not easily determined

9. 3. Objects may occlude each other or they may be superimposed on each other.

10. 2. Objects may appear anywhere on retina in any size A A A A A A + A A A A A A A A A Perhaps the brain is able to represent these objects in a way that is “translationally invariant” and “size invariant”.

11. 4. Same object category (‘e’) may have different shapes E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E

12. Perceptual constancy: Shape 4. Same object (‘this door’) may have different shapes

13. The many shapes of Hillary…

14. 5. Object may be abnormally oriented Letters & Digits: Small Effects Words: Moderate Effects

15. How is object recognition accomplished? Simplest idea: “Template Model”: Store in brain a copy of what every possible input will look like. Match observed object to the proper image in memory

16. Perceptual Representation Memory Representations Template Theory

17. Problems with Template Theory: (1) Massive numbers of templates are required (remember all those E’s?). (2) Predicts no transfer to novel views of the same object (unlikely for different retinal positions) (3) Objects are often obstructed (remember the baby?)

18. Feature Analysis Theory A fixed set of elementary properties are analyzed Independently and in parallel across visual field. Possible examples Line Orientations: Different Sizes: Curvature: +45deg. -10deg. Free line endings: Colors:

19. Perceptual Representation 3 Horizontal lines 1 Vertical line 4 Right angles Memory Representation 3 Horizontal lines 1 Vertical line 4 Right angles A Simple Version of Feature Theory E F 2 Horizontal lines 1 Vertical line 3 Right angles

20. Evidence that Features are really basic elements of visual processing Physiological Evidence –Individual neurons respond preferentially to different kinds of simple visual features simple cells--respond best to lines or angles of a specific orientation and retinal position complex cells--fire maximally to lines or angles without respect to location. They often also have preference for stimuli moving in certain directions… hypercomplex cells--fire maximally have even more complicated sets of requirements for maximal firing. (e.g. corners, notches….etc.)

21. Adaptation Effects are indicators of elementary visual features... Your eyes are always moving, even when “fixated”(microsaccades) An image completely still on the retina will slowly fade from view, because individual feature detectors become habituated (fatigued) with prolonged, sustained stimulation. Retinal stabilization procedure allows direct demonstration of this phenomenon.

23. More Evidence for Features... In a Visual Search Task: (1)Targets defined by a single feature are easy to detect(the red item; the square) (2) Targets defined by a combination of features are difficult to detect (the red square). Let’s try it out

24. Call out “now!” when you see the horizontal line.

26. Typical Results for “Feature Search” # of items in display 2 4 6 10 20 30 “No” “Yes” Reaction Time (msec) These results suggests parallel analysis and detection of simple visual features.

27. Conjunction Search Combination of features (e.g., red AND horizontal) Spatial arrangements of features (e.g. black above white) When targets are defined by:

28. Call out “Now!” when you find the black square above the white square:

29. Treisman’s Results for “Conjunction Search” # of items in display 2 4 6 10 20 30 “No” “Yes” Reaction Time (msec) When higher order analysis or integration of multiple features is required, search is much harder, and reaction time rises with number of distractors.

30. Detecting absence of a feature Look for circle missing the free line ending among

32. Detecting presence of a feature Look for circle with the free line ending among

34. How do we make an object out of a pile of features?

35. Word Recognition: A Case Study Frequency Effects Word Frequency: Frequent words are recognized more easily Repetition Priming:Words seen recently are perceived more easily Context Effects Word Superiority: Individual letters are easiest to identify when they are part of a word (work vs. orwk) Well-formedness: Individual letters are recognized more easily as part of “word-like” stimuli than in a random strings of letters (lipe vs. lpei).

36. w o r d An item will appear

37. k An letter will appear

38. Word superiority WORD_ _ D _ WORD WORK WORM WORE WORN Guess = 1/5 correct Guess = 1/26 correct

39. WORD RWOD XXXX K XXXX D K XXXX D D XXXX K XXXX D This “forced-choice” procedure controls for the effects of guessing, and confirms the validity of the word superiority effect.

40. WORD ZORD XXXX K XXXX D K XXXX D D XXXX K XXXX D

41. Interactive Activation Model explains the word superiority effect Letters in words benefit from bottom-up and top-down activation But letters alone receive only bottom-up activation.

42. features WORD features D D WORK K WORD D K XXXX D K K XXXX D

44. Spared slides

45. A simple “feature net” model of word recognition baseline activation: in the absence of direct stimulation. This is sensitive to recency and frequency of stimulation. activation level: how active is a particular detector at a given moment response threshold: how much excitatory input does a detector require before it “fires” and sends excitatory input further downstream. TORQUE vs.

46. CQRN

48. Interactive Activation Model (IAM): (McClelland & Rumelhart) Previous models posed a bottom-up flow of information (from features to letters to words). IAM also poses a top-down flows of information In addition, detectors at the same level interact with each other. Another important aspect of this theory is the presence of inhibitory connections between detectors that are inconsistent with each other...

49. Interactive Activation Model RAP

50. Interactive Activation Model explains the word superiority effect Letters in words benefit from bottom-up and top-down activation But letters alone receive only bottom-up activation.

51. features WORD features D D WORK K WORD D K XXXX D K K XXXX D