Psy 260 Announcements

1. Psy 260 Announcements • All late CogLab Assignment #1’s due today • CogLab #2 (Attention) is due Thurs. 9/21 at the beginning of class • Coglab booklets and disks--along with a printer that usually works--are available for use in the Psychology Resource Room (enter through Psych B 120) • Quiz alert!

2. Neural network models • Nodes - processing units used to abstractly represent elements such as features, letters, and words • Links, or connections between nodes • Activation - excitation or inhibition that spreads from one node to another

3. Word superiority effect, revisited

4. Word superiority effect, revisited Cond. 1: Cond. 2: Cond. 3: WORD ORWD D XXXXX XXXXX XXXXX Test: Which one did you see? K K K D D D

5. Word superiority effect, revisited Word level Letter level Feature level Input See Reed, p. 36

6. Word superiority effect: An interactive activation model WORK K | / \ Input: K or WORK or ORWD See Reed, p. 36

7. Interactive Activation Model of the word superiority effect(McClelland & Rumelhart, 1981)

8. Interactive Activation Model of the word superiority effect(McClelland & Rumelhart, 1981)

9. (Email example of mangled text!!)

10. James Cattell, 1886: Word superiority effect(Reicher, 1969; Cattell, 1886) Subjects recognized flashed words more accurately than flashed letters. He proposed a word shape model.

11. Evidence for word shape model: • Word superiority effect • Lowercase text is read faster than uppercase. • Proofreading errors tend to be consistent with word shape.

12. Evidence for word shape model: • Word superiority effect • Lowercase text is read faster than uppercase. • Proofreading errors tend to be consistent with word shape. • It’S dIfFiCuLt To ReAd WoRdS iN aLtErNaTiNg CaSe.

13. Perception and Pattern Recognition III:Faces

14. How do people recognize faces? Consider these types of theories: • Template theories • Feature theories • Structure theories • Prototype theories

15. Feature theories • Patterns are represented in memory by their parts. • In perception, the parts are first recognized and then assembled into a meaningful pattern. • Piecemeal (as opposed to holistic)

16. What are the distinctive features for faces ? Eyes, nose, mouth - NOT!

17. What are the distinctive features for faces ? Eyes, nose, mouth - NOT! Revisit Eleanor Gibson’s criteria: • Each feature should be present in some patterns and absent in others • A feature should be invariant (unchanged) for all instances of a particular pattern • Each pattern has a unique combination of features • The number of features should be fairly small A set of features is evaluated by how well it can predict perceptual confusions.

18. Who are these people? Same or different?

19. Who are these people? Same or different?

20. Inspiration: Caricatures • “More like the face than the face itself” • What are the distinctive features of a face - say, Richard Nixon’s??? • Ski jump nose • Jowly face • Curly-textured hair • Receding bays in hairline • Boxy chin (David Perkins, 1975)

21. Contraindicated features: Worse than missing features (Perkins, 1975) A B C D E F

22. Revisit: Problems w/ feature theories • How to determine the right set of features? • What about the relationships between features? • What if all the features are present in the pattern, but scrambled? Features theories predict: No problem! (and that’s the problem.)

23. Face recognition is holistic (Tanaka & Farah, 1993)

24. Structure theories • Build on feature theories • Patterns are represented in memory by features AND by the relations between them. • Holistic • The context of the pattern plays an important role in pattern recognition.

25. A structure theory: RBC (Biederman) • Recognition by Components • Geons: simple volumes (~35 of them) • Construct objects by combining geons

26. RBC Theory • Analyze an object into geons • Determine relations among the geons • The relation among geons is critical!

27. RBC Theory • It’s hard to recognize an object without the information about relations among geons. Hard!

28. RBC Theory • It’s hard to recognize an object without the information about relations among geons. Easier!

29. RBC Theory • Basic properties of Geons • View invariance • Discriminability • Resistance to visual noise

30. RBC Theory - Problems • Explains how people distinguish categories of objects (types) - like cups vs. briefcases. But how do people distinguish individual objects (tokens) that come from the same category (like faces)?? • Neurons are to tuned respond to much smaller elements than those represented by geons!

31. Recap so far: Theory: What it explains: Template Bar codes (by machines) Feature Letter learning & confusions Structural Biederman’s data (geons) Prototype

32. Face recognition(Piecemeal or holistic?)(A “special” case of pattern recognition?)

33. We see faces everywhere. • Image from Mars’ surface by Viking Orbiter 1 (Mcneill, 1998, p. 5)

34. Are faces “special”? • How many faces can you recognize?

35. Are faces “special”? • How many faces can you recognize? • Gibson: Patterns are easier to encode as faces than as writing

36. Are faces “special”? • How many faces can you recognize? • Gibson: Patterns are easier to encode as faces than as writing

37. Faces vs. writing

38. Are faces “special”? • How many faces can you recognize? • Gibson: Patterns are easier to encode as faces than as writing • Prosopagnosia

39. We don’t need much information to recognize a familiar face. Guess who?

40. We don’t need much information to recognize a familiar face. Guess who?

41. Why is face recognition so interesting? • It’s important! • Faces are highly similar to one another. • Yet we’re really good at it: we can tell an astounding number of faces apart. • Not all facial information is created equal. • Could machines ever do as well as people? Or even better? • Are faces somehow “special”?

42. Why is face recognition so interesting? • It’s important! • Faces are highly similar to one another. • Yet we’re really good at it: we can tell an astounding number of faces apart. • Not all facial information is created equal. • Could machines ever do as well as people? Or even better? • Are faces somehow “special”?

43. Faces are hard to recognize in photographic negative (Galper & Hochberg, 1971)

44. Faces are hard to recognize upside down (Yin, 1969)

45. Faces are hard to recognize upside down (Yin, 1969) “Early processing in the recognition of faces” http://www.diss.fu-berlin.de/2003/35/Kap4.pdf

46. Faces are hard to recognize upside down (Yin, 1969) “Early processing in the recognition of faces” http://www.diss.fu-berlin.de/2003/35/Kap4.pdf

47. Margaret Thatcher effect (Thomson, 1980)

48. Margaret Thatcher effect (Thomson, 1980)

49. Why? • The configural processing hypothesis: When faces are inverted, the relationships among features are disturbed. So we don’t notice the odd configuration in the Thatcher illusion. (Bartlett & Searcy, 1993)

50. Faces are hard to recognize upside down (Yin, 1969) “Early processing in the recognition of faces” http://www.diss.fu-berlin.de/2003/35/Kap4.pdf