1 Perceptual Processes ïƒ Introduction Pattern Recognition Pattern Recognition Top-down Processing & Pattern Recognition Top-down Processing & Pattern Recognition

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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 Slide 2 2 Perception Process that uses our previous knowledge to gather and interpret the stimuli that our senses register Slide 3 3 Pattern Recognition The identification of a complex arrangement of sensory stimuli Slide 4 4 Patterns Slide 5 5 Glory may be fleeting Slide 6 6 The Letter Z Slide 7 7 Theories of Pattern Recognition Template Matching Theory Prototype Models Distinctive Features Model Recognition by Components Model Slide 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 Slide 9 9 Examples of Template Matching Attempts Slide 10 10 Used in machine recognition Slide 11 11 Problems for Template Matching Inefficient - large # of stored patterns required Extremely inflexible Works only for isolated letters and simple objects Slide 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 Slide 13 13 Forming Prototypes Faces-- Faces Animated Version Examine the faces below, which belong to two different categories. Slide 14 14 Forming Prototypes of Faces Slide 15 15 Prototypes Family resemblances (e.g. birds, faces, etc.) Evidence supporting prototypes Problems - Vague; not a well-specified theory of pattern recognition Slide 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 Slide 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 Slide 18 18 Visual Cortex Cell Response Slide 19 19 Gibson--Distinctive Features Slide 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 Slide 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? Slide 22 Letter Detection Task 22 Decide whether the pair of letters are the same or different: Yes or No Slide 23 Letter Pairs L TT K MG NS TG 23 Slide 24 24 T Z A How a Distinctive Features Model Might Work: Slide 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? Slide 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 Slide 27 27 Examples of Geons Slide 28 28 Status of Recognition by Components Theory Distinctive features theory for 3-D object recognition Some research consistent with the model; some not Slide 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? Slide 30 30 Support for Biederman Slide 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 Slide 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 Slide 33 33 Thought Experiment Assume each letter 5 feature detections involved Page of text approximately 250-300 words of 5 letters per word on average Each page: 5 x 5 x 250-300 = 6250 - 7500 feature detections Typical reader 250 words/min reading 6250/60 secs =100 feature detections per second Slide 34 34 Ambiguous Stimulus -The Man Ran Slide 35 35 Ambiguous Stimulus - The Cat in the Hat Slide 36 36 Fido is Drunk Slide 37 Reversible Figure and Ground 37 Slide 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. Slide 39 39 Word Superiority- Non-word Trial Slide 40 40 Word Superiority: Word Trial Slide 41 41 Single Letter K vs K in a word Slide 42 42 Word Superiority: Single Letter Trial Slide 43 43 Word Superiority: Word Trial Slide 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 Slide 45 45 Attention Slide 46 46 Definitions of Attention Concentration of mental resources Allocation of mental resources Slide 47 Multiple Aspect of Attention Divided attention Divided attention Selective attention Selective attention Theories of attention Theories of attention 47 Slide 48 48 Divided Attention Slide 49 49 Reinitz & Colleagues (1974) Divided Attention Condition Subjects count the dots Full Attention Condition No instruction about dots Slide 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.81.48.42 Slide 51 51 Divided Attention & Practice Hirst, et. al. 1980 Spelke, 1976 Slide 52 Demo 52 Slide 53 53 UpsetHotelJudgeEmploymentMapIndulgePencilProblemKeyTerrible Slide 54 Can we always divide our attention with practice? 54 Slide 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 Slide 56 56 Selective Attention Slide 57 57 Selective Attention (Dichotic Listening Task) Shadowing Irrelevant Channel Cocktail Party Effect - Morray (1959) Wood and Cowan (1995) Treisman (1960) Slide 58 58 Dichotic Listening Task T, 5, H LEFT T 5 H RIGHT S 3 G Slide 59 59 Cocktail Effect Slide 60 60 Treismans Shadowing Study Slide 61 61 Stroop Effect Go to Stroop Demonstration Slide 62 62 Filter Models of Attention Slide 63 63 Capacity Model of Attention Slide 64 64 Diagnostic Criteria for Automatic Processes Slide 65 65 Cerebral Cortex & Attention Slide 66 Stroop Effect Demos 66 Slide 67 Experiment 1 67 Slide 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! Slide 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! Slide 70 Experiment 2 70 Slide 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! Slide 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! Slide 73 Stroops 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 Slide 74 74 Go Back