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Pattern recognition


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Pattern recognition

  1. 1. Pattern RecognitionI. What is pattern recognition?II. Template ModelsIII. Feature ModelsIV. Top-Down & Bottom-Up processingV. Neural Network ModelsVI. Prototype ModelsVII. Facial RecognitionVI. ConclusionsI. What is Pattern RecognitionA. Definition: A process of identifying a stimulus. Recognizing a correspondence between a stimulus and information in permanent (LTS) memory.I. What is Pattern RecognitionB. In the context of the Atkinson and Shiffrin ModelInput Sensory Short- Long- Store Term Term Store Store Control Processes rehearsal coding retrieval strategies Response Output Page 1
  2. 2. I. What is Pattern RecognitionC. This process is often accomplished with incomplete or ambiguous information.D. Many variations on a pattern may be recognized as the same object or class of objects. Page 2
  3. 3. Turing test (used by Yahoo,Hotmail, and ebay)F. Pattern recognition that is difficult for machines is easy for yuo cna raed tihs, yuo hvae asgtrane mnid too.I cdnuolt blveiee taht I cluod aulaclty uesdnatnrd waht I was rdanieg. The phaonmneal pweor of the hmuan mind! Aoccdrnig to a rscheearch at Cmabrigde Uinervtisy, it dseno t mtaetr in waht oerdr the ltteres in a wrod are, the olny iproamtnt tihng is taht the frsit and lsat ltteer be in the rghit pclae.The rset can be a taotl mses and you can sitll raed it whotuit a pboerlm.Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe. Azanmig huh? [This demonstration is food for thought. The psychological principles it espouses are only partly correct. See Reicher (1969)]II. Template ModelA. Basic Assumptions1) Memory representation is a holistic unanalyzed entity (a template).2) An input pattern is compared to the stored representation.3) Identity is determined by selection of the template with the greatest amount of overlap. Page 3
  4. 4. II. Template ModelB. Schematic of a Template System Stimulus Brightness A Detector Templates Light Source II. Template Model (cont)C. Template systems in actionTemplate Model (cont)D. Problems with template models 1. Intolerance to deviations 2. Large number of templates required 3. Cannot support similarity-difference judgments Page 4
  5. 5. III. Feature TheoriesA. Basic Assumptions 1. The stored representation is a description of past inputs in terms of list of attributes or features. 2. Inputs are broken down into a small list of constituent features. 3. Identity is determined by selecting the feature list most similar to the input.III. Feature Theories B. Schematic of a Feature Model StimulusIII. Feature Theories (cont)C. Supporting Evidence 1. Hubel & Wiesel (1962): Recorded electrical activity in the visual cortex of the cat. Page 5
  6. 6. Hubel & Wiesel (1962) Results: specific cellsrespond to specific visual features.III. Feature Theories (cont) B. Supporting Evidence (cont) 2. Letter recognition times Gibson, Shapiro, & Yonas (1968) Step 1: Analyze letters in terms of a small set of features. Step 2: Give subjects a reaction test two determine if two letters are the same or different. e.g. G vs.. W RT = 458 msec P vs.. R RT = 571 msec Step 3: Compare the clustering of letters in the reaction time task to the similarities in features.Step 1: Feature Analysis of Letters Page 6
  7. 7. Step 2: Letter Groupings based on RT III. Feature Theories (cont) D. Criticisms of Feature Theories 1. Importance of Context 2. Importance of Arrangement IV. Top-Down vs. Bottom-up Processing comprehensionBottom Up phrase processing Top Down(data driven) word processing (conceptually letter processing driven) feature processing Page 7
  8. 8. IV. Top-Down vs. Bottom-up Processing In Control of Attention (Bushman & Miller, 2007) implanted electrodes in monkeys the monkeys were trained to search for a target in a visual display the researchers measured reaction time and recorded firing rates in parietal cortex (25 electrodes) (visual- sensory information) and the prefrontal cortex (25 electrodes).IV. Top-Down vs. Bottom-up Processing Bushman & Miller (continued) Bottom up: visual pop-out Sensory neurons (parietal) responded first Page 8
  9. 9. IV. Top-Down vs. Bottom-up Processing Bushman & Miller (continued) Top down (visual search) prefrontal cortex responded firstIV. Top-Down vs. Bottom-up Processing Conclusion: Button up processing signals arise from the sensory cortex. Top down processing signals begin in the frontal cortex. V. Neural Network Model of Word Pattern Recognition Analysis A. Interactive Activation Model (McClelland & Rumelhart, 1981) Letter Analysis Incorporates top-down processing from the word level to the letter level. Excitatory connections: Feature Inhibitory connections: Analysis Visual Input Page 9
  10. 10. Simplified view of the Network of Connections Excitatory connections: Inhibitory connections: Word Level CAT CHAIR THE Letter Level A C H T E Feature Level Input More Complete view of the Network of Connections: B. Supporting Evidence:The word/letter effectReicher (1969) Stimulus Example Test Percent Correct letter h h/t 78 series csah csah/csat 76 word cash cash/cast 89 Page 10
  11. 11. VI. Prototype Theory A. Basic Assumptions 1. The stored representation is a Prototype: an abstraction of the typical or best example of an object. examples: chairs, cars, and trucks 2. Inputs are broken down into feature lists. 3. Recognition is process of comparing the features of the input to the features of prototypes, and selecting the best fit. VI. Prototype Theory (cont.) 75% B. Evidence for Prototype Theory Solso & McCarthy (1981) 50% face recognition 25% Prototype 100% 0% VI. Prototype Theory (cont.) Solso & McCarthy (1981): results 5 Old Items 4 New ItemsOld 3 2Confidence 1 0 -1 -2 -3New -4 -5 100 75 50 25 Percent Overlap with Prototype Page 11
  12. 12. VI. Prototype Theory (cont.) C. Prototype Theory and attractiveness 1) goodness of category membership can be defined with respect to the prototype. 2) good category members may be seen as more attractive, or desirable, than poor category membership C. Prototype Theory and attractiveness (cont.) Example: attractive faces are average (Langlois & Roggman, 1990) Stimulus set: individual faces composite faces containing 2 - 32 faces. Examples of composite faces:Number in composite481632 Page 12
  13. 13. Rated attractivenessNumber of faces average rating1 2.512 2.874 2.848 3.0316 3.0632 3.25VII. Facial Recognition:Why Barack Obama is Black (Halberstadt et al, 2011)Hypodescent: association of mixed race individuals as belonging to the minority race.Hypothesis: individuals learn to minority groups later than majority groups, so they learn to focus attention on features that distinguish the groups.Increased attention to distinctive (distinguishing) features leads to over- classification in the “new” group.Why Barack Obama is Black (Halberstadt et al, 2011)Evidence: Experiment 1 Participants: ½ Caucasians (New Zealanders) ½ of Chinese decent (raised in China or Asian Pacific regions). Individuals performed a speeded classification of faces that were morphed blends of Chinese and Caucasian faces: Page 13
  14. 14. Why Barack Obama is Black (Halberstadt et al, 2011)Experiment 2 Participants: 75% Caucasian, 25 % other Procedure: participants learned to classify faces into different (arbitrary) groups. “majority faces” classified 9 times “minority faces” classified 3 timesWhy Barack Obama is Black (Halberstadt et al, 2011) ResultsExperiment 1 Percent of ambiguous faces rated as Chinese: Chinese Participants: 44 % Caucasian Participants: 49 %Experiment 2 Percent of ambiguous faces rated as B’s A faces “majority”: 40 % B faces “majority”: 36 %Conclusions: Biracial classifications are based on learning history. Distinctive racial features receive greater attention if they are learned later in life.Why Barack Obama is Black (Halberstadt et al, 2011)Conclusions: Biracial classifications are based on learning history. Distinctive racial features receive greater attention if they are learned later in life. Page 14
  15. 15. VII. Facial Recognition: A special problem for theories of pattern recognition: A. Different set of rules? (Example: object vs. facial recognition). Yin (1970), and Rock (1974) demonstrated that facial recognition is more easily impaired by inversion than is object recognition. Who is this? Page 15
  16. 16. A B Page 16
  17. 17. A B VII. Facial Recognition (cont) B. Different Neurological Structures? Dissociation between loss of object recognition (visual agnosia) and face recognition in stroke victims. (e.g., Msocovithc, Winocur, & Behrman, 1997)VI. Conclusions on Pattern Recognition A. Template and Feature Models are inadequate B. Context and top-down processing are very important C. Neural Networks can explain top down processes. D. Important role of prototypes E. Challenge of explaining facial recognition Page 17