1. © 2016 by W. W. Norton & Company Recognizing Objects Chapter 4 Lecture Outline

2. Chapter 4: Recognizing Objects Lecture Outline  Object Recognition  Word Recognition  Feature Nets  Different Objects, Different Recognition Systems?  The Importance of Larger Contexts ©2016 W. W. Norton & Company

3. Recognizing Objects Patients with associative agnosia see but cannot link this to visual knowledge.  They can draw well from memory. ©2016 W. W. Norton & Company

4. Recognizing Objects Why is object recognition important?  Crucial for applying your knowledge  Crucial for learning ©2016 W. W. Norton & Company

5. Object Recognition Can recognize objects even when incomplete Incomplete information From the back From the front Context helps ©2016 W. W. Norton & Company

6. Object Recognition Same stimulus H A ©2016 W. W. Norton & Company

7. Object Recognition Bottom-up (or data-driven) processing  Stimulus-driven effects Top-down (or concept-driven) processing  Knowledge- or expectation-driven effects ©2016 W. W. Norton & Company

8. Object Recognition Recognition begins with features—the small elements that result from the organized perception of form. ©2016 W. W. Norton & Company

9. Object Recognition Features  Building blocks  Commonalities for variable objects  Play a role in visual search ©2016 W. W. Norton & Company

10. Object Recognition Visual search demo ©2016 W. W. Norton & Company

11. Object Recognition Find the vertical line (the one standing up). ©2016 W. W. Norton & Company

12. Object Recognition ©2016 W. W. Norton & Company

13. Object Recognition Find the green-colored line. ©2016 W. W. Norton & Company

14. Object Recognition ©2016 W. W. Norton & Company

15. Object Recognition Find the vertical red-colored line (the one standing up). ©2016 W. W. Norton & Company

16. Object Recognition ©2016 W. W. Norton & Company

17. Object Recognition Which one was harder? ©2016 W. W. Norton & Company

18. Object Recognition Difficulty in judging how more than one feature is bound together in objects.  Integrative agnosia, parietal cortex damage  Disruption of parietal cortex via transcranial magnetic stimulation (TMS) ©2016 W. W. Norton & Company

19. Word Recognition Some methodology for studying word recognition  From tachistoscope to computers ©2016 W. W. Norton & Company

20. Word Recognition ©2016 W. W. Norton & Company

21. Word Recognition Masked words Repeated words ©2016 W. W. Norton & Company

22. Word Recognition Word-superiority effect: people’s response when asked whether “DARK” has an “E” or a “K” is faster than when searching within a letter string such as “JPERW.” ©2016 W. W. Norton & Company

23. Word Recognition Better at identifying letters in a word ©2016 W. W. Norton & Company

24. Word Recognition Why word superiority?  Probability How likely is it that letter combinations appear in English? ©2016 W. W. Norton & Company

25. Word Recognition Errors also driven by probability  Likely to misread words predictably ©2016 W. W. Norton & Company

26. Feature Nets Complex Simple ©2016 W. W. Norton & Company

27. Feature Nets Neural network  With input, activation level increases  Have receptive fields  Fire above threshold  Complex assemblies of neurons ©2016 W. W. Norton & Company

28. Feature Nets Recent firing results in a higher starting activation level.  Frequency leads to higher recency.  Repetition also increases recency. ©2016 W. W. Norton & Company

29. Feature Nets ©2016 W. W. Norton & Company

30. Feature Nets Stronger baseline activity Better recognition ©2016 W. W. Norton & Company

31. Feature Nets TH is more frequent CA and AT are more frequent ©2016 W. W. Norton & Company

32. Feature Nets Stronger baseline activity Will correct recognition ©2016 W. W. Norton & Company

33. Feature Nets Knowledge is not locally represented. Rather, feature nets contain distributed knowledge. ©2016 W. W. Norton & Company

34. Feature Nets Errors arise from limits in the network’s ability to deal with ambiguous inputs and to recover from errors. Accuracy is sacrificed for efficiency. ©2016 W. W. Norton & Company

35. Feature Nets A much more complex feature net with feedforward and feedback loops More like a brain ©2016 W. W. Norton & Company

36. Feature Nets Building blocks for objects ©2016 W. W. Norton & Company

37. Feature Nets Bottom-up recognition  Geon recognition leads to object recognition  Viewpoint invariant ©2016 W. W. Norton & Company

38. Feature Nets Recognition via multiple views  Alternative is storing multiple views of objects  But still needs some rotation  So speed of recognition will be viewpoint dependent ©2016 W. W. Norton & Company

39. Feature Nets Hierarchy of detectors  Each successive layer processes more complex aspects.  Detectors represent a particular viewpoint.  Some inferotemporal cortex cells (what pathway) are object specific or view specific. ©2016 W. W. Norton & Company

40. Feature Nets ©2016 W. W. Norton & Company

41. Different Objects, Different Recognition Systems? Some categories are special.  Faces ©2016 W. W. Norton & Company

42. Different Objects, Different Recognition Systems? Prosopagnosia is a type of agnosia also known as face blindness. ©2016 W. W. Norton & Company

43. Different Objects, Different Recognition Systems? Houses about the same upright as inverted Faces much worse inverted and much better upright ©2016 W. W. Norton & Company

44. Different Objects, Different Recognition Systems? Do these two faces look different? ©2016 W. W. Norton & Company

45. Different Objects, Different Recognition Systems? Do these two faces look different? ©2016 W. W. Norton & Company

46. Different Objects, Different Recognition Systems? Viewpoint dependence appears when  Interpreting faces  Expertise is high (e.g., dog judges)  Specific individuals have to be recognized  Configurations of component parts are important ©2016 W. W. Norton & Company

47. Different Objects, Different Recognition Systems? Some researchers suggest face recognition is not the only thing that is special. For example, a bird-watcher with prosopagnosia lost the ability to see faces and also types of warblers. Another lost the ability to distinguish types of cars. ©2016 W. W. Norton & Company

48. Different Objects, Different Recognition Systems? Face expertiseCar expertise Bird expertise ©2016 W. W. Norton & Company

49. Different Objects, Different Recognition Systems? Holistic processing  Composite faces  Features matter but cannot be considered individually  The relationships guide face recognition ©2016 W. W. Norton & Company

50. The Importance of Larger Contexts However, there is a great deal of knowledge that guides our recognition. Words are easier to recognize as part of a sentence than in isolation. Semantic priming helps recognition.  This knowledge is outside of object recognition per se. ©2016 W. W. Norton & Company

51. The Importance of Larger Contexts ©2016 W. W. Norton & Company

52. Chapter 4 Questions

53. 1.Which of the following is evidence for a feature theory of perception? a) The visual system is specialized, with cells that detect single features. b) When researchers are able to stabilize the retinal image for an individual, preventing tiny eye movements (saccades) that refresh the rods and cones, the image stays the same. c) In visual search paradigms, in which a single target must be found in an array of other items, target identification is faster when it shares features with the distractors. d) Detecting an embedded figure (including its features) is independent of the way the form is parsed. ©2016 W. W. Norton & Company

54. 2. When Betty (an English speaker) is shown strings of letters tachistoscopically, they are overregularized to follow the rules of common English spelling. This is because a) of the word superiority effect. b) all humans are genetically predisposed toward the visual configurations evident in “regular” bigrams; this is why English uses them. c) of a lifetime of strengthening the bigram detectors for common English letter pairs. d) Betty is reluctant to give answers that she cannot easily pronounce. ©2016 W. W. Norton & Company

55. 3. The use of geons is associated with a) the recognition-by-components (RBC) model. b) the word superiority effect. c) visual masking. d) feature nets. ©2016 W. W. Norton & Company

56. 4. The “recognition-via-multiple-views” approach to object recognition is also known as __________ recognition. a) viewpoint-dependent b) viewpoint-independent c) object d) face ©2016 W. W. Norton & Company