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1 Perceptual Processes  Introduction Pattern Recognition Pattern Recognition Top-down Processing & Pattern Recognition Top-down Processing & Pattern Recognition.

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Presentation on theme: "1 Perceptual Processes  Introduction Pattern Recognition Pattern Recognition Top-down Processing & Pattern Recognition Top-down Processing & Pattern Recognition."— Presentation transcript:

1 1 Perceptual Processes  Introduction Pattern Recognition Pattern Recognition Top-down Processing & Pattern Recognition Top-down Processing & Pattern Recognition Face Perception Face Perception  Attention Divided attention Divided attention Selective attention Selective attention Theories of attention Theories of attention

2 2 Perception Process that uses our previous knowledge to gather and interpret the stimuli that our senses register

3 3 Pattern Recognition The identification of a complex arrangement of sensory stimuli

4 4 Patterns

5 5 Glory may be fleeting…

6 6 The Letter Z

7 7 Theories of Pattern Recognition  Template Matching Theory  Prototype Models  Distinctive Features Model  Recognition by Components Model

8 8 Template Matching Theory  Compare a new stimulus (e.g. ‘T’ or ‘5’) to a set of specific patterns stored in memory  Stored pattern most closely matching stimulus identifies it.  To work – must be single match  Used in machine recognition

9 9 Examples of Template Matching Attempts

10 10 Used in machine recognition

11 11 Problems for Template Matching  Inefficient - large # of stored patterns required  Extremely inflexible  Works only for isolated letters and simple objects

12 12 Prototype Theories  Store abstract, idealized patterns (or prototypes) in memory  Summary - some aspects of stimulus stored but not others  Matches need not be exact

13 13 Forming Prototypes Faces-- Faces Animated Version Examine the faces below, which belong to two different categories.

14 14 Forming Prototypes of Faces

15 15 Prototypes  Family resemblances (e.g. birds, faces, etc.)  Evidence supporting prototypes  Problems - Vague; not a well-specified theory of pattern recognition

16 16 Distinctive Features Models  Comparison of stimulus features to a stored list of features  Distinctive features differentiate one pattern from another  Can discriminate stimuli on the basis of a small # of characteristics – features  Assumption: feature identification possible

17 17 Distinctive Features Models: Evidence  Consistent with physiological research  Psychological Evidence Gibson 1969 Gibson 1969 Neisser 1964 Neisser 1964 Waltz 1975 Waltz 1975 Pritchard 1961 Pritchard 1961

18 18 Visual Cortex Cell Response

19 19 Gibson--Distinctive Features

20 Can we empirically test the distinctive features theory? In other words, can we show that we must be processing features when we identify and distinguish one pattern from another – e.g. letters? There are many ways we can test a feature- based theory. For example: 20

21 21 Scan for the letter ‘Z’ in the first column of letter strings. Scan for the letter ‘Z’ in the second column of letter strings. Where did you find the ‘Z’ faster: in column 1 or 2? What does this show?

22 Letter Detection Task 22 Decide whether the pair of letters are the same or different: Yes or No

23 Letter Pairs L TT K MG NS TG 23

24 24 T Z A How a Distinctive Features Model Might Work:

25 25 Distinctive Features  Theory must specify how the features are combined/joined  These models deal most easily with fairly simple stimuli -- e.g. letters  Shapes in nature more complex -- e.g. dog, human, car, telephone, etc  What would the features here be?

26 26 Recognition by Components Model  Irving Biederman (1987, 1990)  Given view of object can be represented as arrangement of basic 3-D shapes (geons)  Geons = derived features or higher level features  In general 3 geons usually sufficient to identify an object

27 27 Examples of Geons

28 28 Status of Recognition by Components Theory  Distinctive features theory for 3-D object recognition  Some research consistent with the model; some not

29 Recognition by Components Pro – Biederman found that obscuring vertices impairs object recognition while obscuring other parts of objects has a lesser effect. 29 Which is easiest to recognize as a cup? The left or right? Con – Biederman – Not all natural objects can be decomposed into geons. What about a shoe? Con – Biederman – Not all natural objects can be decomposed into geons. What about a shoe?

30 30 Support for Biederman

31 31 Summary  Distinctive Features approach currently strongest theory  Perhaps all 3 approaches (distinctive features, prototypes, recognition by components) are correct  Regardless, pattern recognition is too rapid and efficient to be completely explained by these models

32 32 Two types of Processing  Bottom-up or data-driven processing  Top-down or conceptually driven processing  Theme 5 -- most tasks involve bottom-up and top-down processing

33 33 Thought Experiment  Assume each letter 5 feature detections involved  Page of text approximately words of 5 letters per word on average  Each page: 5 x 5 x = feature detections  Typical reader 250 words/min reading  6250/60 secs =100 feature detections per second

34 34 Ambiguous Stimulus -The Man Ran

35 35 Ambiguous Stimulus - The Cat in the Hat

36 36 Fido is Drunk

37 Reversible Figure and Ground 37

38 38 Word Superiority Effect We can identify a single letter more rapidly and more accurately when it appears in a word than when it appears in a non-word.

39 39 Word Superiority- Non-word Trial

40 40 Word Superiority: Word Trial

41 41 Single Letter ‘K’ vs ‘K’ in a word

42 42 Word Superiority: Single Letter Trial

43 43 Word Superiority: Word Trial

44 44 Altered Sentences in Warren and Warren (1970) Sentence that was presentedWord Heard It was found that the *eel was on the axle It was found that the *eel was on the shoe It was found that the *eel was on the orange It was found that the *eel was on the table wheel heel peel meal *Denotes the replaced sound

45 45 Attention

46 46 Definitions of Attention  Concentration of mental resources  Allocation of mental resources

47 Multiple Aspect of Attention Divided attention Divided attention Selective attention Selective attention Theories of attention Theories of attention 47

48 48 Divided Attention

49 49 Reinitz & Colleagues (1974) Divided Attention Condition Subjects count the dots Full Attention Condition No instruction about dots

50 50 Proportion of Responses that were “old” for Each of Two Study Conditions and Two Test Conditions (Reinitz & Colleagues, 1994). Study Condition Test Condition Full AttentionDivided Attention Old Face Conjunction Faces

51 51 Divided Attention & Practice  Hirst, et. al  Spelke, 1976

52 Demo 52

53 53 UpsetHotelJudgeEmploymentMapIndulgePencilProblemKeyTerrible

54 Can we always divide our attention with practice? 54

55 Cell Phones & Driving – What Does the Research on Divided Attention Show? Is it safe to drive while talking on a cell phone? Some states have passed legislation prohibiting hand-held but not hands-free cell phones. Does this make any sense What about talking to someone in the car while driving versus talking on a cell phone? What are the chances of an accident? Does practice make a difference? Why not? Compare driving under the influence to cell phone driving 55

56 56 Selective Attention

57 57 Selective Attention (Dichotic Listening Task)  Shadowing  Irrelevant Channel  Cocktail Party Effect - Morray (1959)  Wood and Cowan (1995)  Treisman (1960)

58 58 Dichotic Listening Task T, 5, H LEFT T 5 H RIGHT S 3 G

59 59 Cocktail Effect

60 60 Treisman’s Shadowing Study

61 61 Stroop Effect Go to Stroop Demonstration

62 62 Filter Models of Attention

63 63 Capacity Model of Attention

64 64 Diagnostic Criteria for Automatic Processes

65 65 Cerebral Cortex & Attention

66 Stroop Effect Demos 66

67 Experiment 1 67

68 Read the Word. 68 Green Blue Red Purple Green Blue Orange Red Blue Green Red Blue Orange Green Blue Red Purple Orange Green Black Stop!

69 Read the Word. Ignore the color 69 Green Blue Red Purple Green Blue Orange Red Blue Green Red Blue Orange Green Blue Red Purple Orange Green Black Stop!

70 Experiment 2 70

71 Name the Color of the Ink 71 xxxxx xxx xxxxxx xxxxx xxxxxx xxx xxxx xxxxx xxx xxxx xxxxxx xxxxx xxxx xxx xxxxxx xxxxx Stop!

72 Name the Color (e.g. Red say “blue”) 72 Green Blue Red Purple Green Blue Orange Red Blue Green Red Blue Orange Green Blue Red Purple Orange Green Black Stop!

73 Stroop’s 3 Experiments Exp 1 - Selectively attend to the verbal aspect of the stimulus; ignore ink color Exp 2 – Selectively attend to the ink color of the stimulus; ignore verbal aspect Exp 3 – Why does ignoring the verbal aspect of the stimulus interfere strongly with color naming; but not the reverse? 73

74 74 Go Back


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