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 Perception Process that uses our previous knowledge to gather and interpret the stimuli that our senses register
3 Pattern Recognition The identification of a complex arrangement of sensory stimuli
5 Glory may be fleeting…
6 The Letter Z
7 Theories of Pattern Recognition Template Matching Theory Prototype Models Distinctive Features Model Recognition by Components Model
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 Examples of Template Matching Attempts
10 Used in machine recognition
11 Problems for Template Matching Inefficient - large # of stored patterns required Extremely inflexible Works only for isolated letters and simple objects
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 Forming Prototypes Faces-- Faces Animated Version Examine the faces below, which belong to two different categories.
14 Forming Prototypes of Faces
15 Prototypes Family resemblances (e.g. birds, faces, etc.) Evidence supporting prototypes Problems - Vague; not a well-specified theory of pattern recognition
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 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 Visual Cortex Cell Response
19 Gibson--Distinctive Features
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 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?
Letter Detection Task 22 Decide whether the pair of letters are the same or different: Yes or No
Letter Pairs L TT K MG NS TG 23
24 T Z A How a Distinctive Features Model Might Work:
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 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 Examples of Geons
28 Status of Recognition by Components Theory Distinctive features theory for 3-D object recognition Some research consistent with the model; some not
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 Support for Biederman
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 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 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 Ambiguous Stimulus -The Man Ran
35 Ambiguous Stimulus - The Cat in the Hat
36 Fido is Drunk
Reversible Figure and Ground 37
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 Word Superiority- Non-word Trial
40 Word Superiority: Word Trial
41 Single Letter ‘K’ vs ‘K’ in a word
42 Word Superiority: Single Letter Trial
43 Word Superiority: Word Trial
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
46 Definitions of Attention Concentration of mental resources Allocation of mental resources
Multiple Aspect of Attention Divided attention Divided attention Selective attention Selective attention Theories of attention Theories of attention 47
48 Divided Attention
49 Reinitz & Colleagues (1974) Divided Attention Condition Subjects count the dots Full Attention Condition No instruction about dots
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 Divided Attention & Practice Hirst, et. al Spelke, 1976
Can we always divide our attention with practice? 54
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
58 Dichotic Listening Task T, 5, H LEFT T 5 H RIGHT S 3 G
59 Cocktail Effect
60 Treisman’s Shadowing Study
61 Stroop Effect Go to Stroop Demonstration
62 Filter Models of Attention
63 Capacity Model of Attention
64 Diagnostic Criteria for Automatic Processes
65 Cerebral Cortex & Attention
Stroop Effect Demos 66
Experiment 1 67
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!
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!
Experiment 2 70
Name the Color of the Ink 71 xxxxx xxx xxxxxx xxxxx xxxxxx xxx xxxx xxxxx xxx xxxx xxxxxx xxxxx xxxx xxx xxxxxx xxxxx Stop!
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!
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