Cognition and Perception

1. Cognition and Perception • This is not a rose. This is not a pipe. “Just try stuffing tobacco in it!” – Rene Magritte, 1930

2. The myth of vision as a faithful record ·       Concentric circles or continuous spiral? ·       The pattern of light is of concentric circles ·       Human vision sees a continuous spiral

3. Gestalt • The whole is greater than the sum of its parts • Law of Pragnanz (“good figure”): We perceive things in the way that is simplest to organize them into cohesive and constant objects.

4. Gestalt Laws • Laws of Figure-Ground Segregation • 1. Convex region becomes figure • 2. Smaller region becomes figure • 3. Moving region becomes figure • 4. Symmetric ("good") region becomes figure • 5. Nearer region becomes figure (multiple depth cues apply)

5. Gestalt Laws Laws of Grouping • 1. Proximity • 2. Similarity • 3. Common fate • 4. Good continuation • 5. Closure/ convexity • 6. Common region • 7. Connectedness • 8. Parse regions at deep concavities

7. Common Fate • http://dragon.uml.edu/psych/commfate.html

8. Figure 1. A: Kanizsa figure. B: Tse’s volumetric worm. C: Idesawa’s spiky sphere. D: Tse’s sea monster

9. Gestalt Laws Laws of Grouping Closure/ convexity

10. The Myth of vision as a passive process • The Grand illusion of complete perception • (1) Vision is not rich in detail • the size of a thumbnail at arm’s length is all that gets processed • (2) Attention is limited: the law of ONEs • vision sees one object, one event, one location • These two factors are illustrated by • Impossible triangle • Escher drawings • Bistable images

11. Brains construct a well-behaved 3-D world so we cannot experience a world that is not. Here we see an ordinary triangle and building with normal corners and angles instead of the shocking reality. Why?

12. ·       A perceptually ambiguous wire cube ·       How many different interpretations can you see? Go to: http://mindbluff.com/necker.htm

13. Figure 1.5. “Subjective” perceptions are not necessarily “arbitrary” perceptions Brains see two instead of all of these interpretations? Why not? Humans bring shared assumptions to the vision project, (1) that objects are generally convex, (2) that straight lines in a picture represent straight edges in an object, and (3) that three-edge junctions are generally right-angled corners.

14. Bi-stable Images

15. Bi-stable Images

20. Law of One in Audition • Shepard Tone • http://www.youtube.com/watch?v=DfJa3IC1txI • Each square in the figure indicates a tone, any set of squares in vertical alignment together making one Shepard tone. The color of each square indicates the loudness of the note, with purple being the quietest and green the loudest. Overlapping notes that play at the same time are exactly one octave apart, and each scale fades in and fades out so that hearing the beginning or end of any given scale is impossible.

21. Demos • Charlie Chaplin mask demo • http://www.youtube.com/watch?v=QbKw0_v2clo&feature=related • Visual Illusions • http://www.michaelbach.de/ot/ • Moving random dot stereogram • http://dragon.uml.edu/psych/commfate.html • Spinning silhouette • http://www.youtube.com/watch?v=uBTvKboX84E • Gestalt Illusions • http://www.opprints.co.uk/gallery.php

22. Object Recognition • Mike the blind guy given sight • http://www.youtube.com/watch?v=VVgfC_FV2hI&feature=PlayList&p=32BC95C9D7E5959C&index=1

23. Object Recognition(Called Pattern Recognition in Book) • How do you solve problem of Object Constancy? • How does the brain know the objects are the same despite change in perspective?

24. What letter are these, and how do you know? A A A A A A A A A A A A A A A A A A A A

25. Object Recognition

27. Receptive Fields of cortical neurons—Primary Visual cortex • 1. Simple Cells --respond to points of light or bars of light in a particular orientation • 2. Complex cells --respond to bars of light in a particular orientation moving in a specific direction. 3. Hypercomplex Cells: respond to bars of light in a particular orientation, moving in a specific direction, & of a specific line length.

28. What is the organization of the visual cortex? • Hubel & Wiesel found that the visual cortex is organized into columns. • Location specific: For each place on the retina there is a column of cells in cortex. • Two columns next to one another in the cortex respond to stimulation of two adjacent points on the retina.

29. Spatial Frequency • These grids are low to high spatial frequencies. • Many light bars / square = High S.F. • Few light bars / square = Low S.F. • Part of vision’s organization

32. Spatial Frequency • By playing with spatial frequency, you can induce a the intense luminance perception of a bright sun.

33. Spatial Frequencies Work Together • Low S.F. give you outlines, High give you details. • Broad spectrum give you Local and Global features

34. Bottom-Up Processing • Perception comes from the stimuli in the environment • Parts are identified, put together, and then recognition occurs • Context does not matter

35. Gibson’s Direct Perception (Bottom-Up) All the information needed to form a perception is available in the environment Perception is immediate and spontaneous Affordances and attunements Perception and action cannot be separated Action defines the meaningful parameters of perception and provides new ways of perceiving

36. 0 Top-down Processing • Perception is not automatic from raw stimuli • Context is needed to build perception • Meaning is constructed by making inferences, guessing from experience, and basing one perception on another

37. 0 Template Theory:Perception as a Cookie Cutter • Basics of template theory • Multiple templates are held in memory • Compare stimuli to templates in memory for one with greatest overlap until a match is found Search memory for a match See stimuli

38. 0 Template Theory • Weakness of theory • Problem of imperfect matches • Cannot account for the flexibility of pattern recognition system • More problems… Search for match in memory See stimuli No perfect match in memory

39. 0 Template Theory • More Weaknesses of theory • Comparison requires identical orientation, size, position of template to stimuli • Does not explain how two patterns differ • e.g., there’s something wrong with it this, but I can’t put my finger on it – AHA! I see!

40. 0 Feature Theories • Recognize objects on the basis of a small number of characteristics (features) • Detect specific elements and assemble them into more complex forms • Brain cells that respond to specific features, such as lines and angles are referred to as “feature detectors”

41. Two Feature Theories of Object Recognition • Recognition By Components (Biederman; Marr) vs. • View-Based Recognition (Tarr; Bülthoff)

42. Superquadratics (Pentland, 1986) Geons (Biederman, 1987) Generalized Cylinders (Binford, 1971; Marr, 1982)

43. Recognition By Components (Biederman) • Basic set of geometrical shape • Geons (“geometric” + “ions”) • Distinguishable from almost any viewing angle • Recognizable even with occlusion • “Grammatical” relationship b/w parts • Part-whole hierarchies

44. 0 Evidence of Geons • Beiderman (1987) • Can you identify these objects? These objects have been rendered unidentifiable because their geons are nonrecoverable

45. 0 Evidence of Geons • Beiderman (1987) • Can you identify these objects? These objects have had the same amount of the object taken out but because the geons can still be recreated, one can recover the objects

46. Testing Biederman • Objects are decomposed • Omitting Vertices • Retaining Vertices • In accordance with theory, easier to identify object with vertices

47. Object Recognition • Pros • Explains why it can be hard to recognize familiar objects from highly unusual perspectives • Cons • Absence of physiological evidence • Does not explain expert discriminations or quirks of facial recognition

48. Marr’s Computational Approach • Primal Sketch: 2-D description includes changes in light intensity, edges, contours, blobs • 2 ½ -D Sketch: Includes information about depth, motion, shading. Representation is observer-centered • 3-D Representation: A representation of objects and their relationships, observer-independent.

49. View-Based Recognition • Tarr; Bulthoff • Multiple stored views of objects • Viewer-centered frame of reference • Specific views correspond to specific patterns of neural activation (possibly involves “place neurons”) • Match b/w current and stored pattern of activation • Interpolating (“educated guessing” or impletion) b/w seen and stored views