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 astimulus. Recognizing a correspondencebetween a stimulus and information inpermanent (LTS) memory.I. What is Pattern RecognitionB. In the context of the Atkinson and Shiffrin ModelInputSensory Short- Long- Store Term Term StoreStore Control Processes rehearsal coding retrieval strategies Response Output Page 1

2. I. What is Pattern RecognitionC. This process is often accomplished withincomplete or ambiguous information.D. Many variations on a pattern may berecognized as the same object or class ofobjects.Page 2 3. Turing test (used by Yahoo,Hotmail, and ebay)F. Pattern recognition that is difficult formachines is easy for people.fi 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 holisticunanalyzed entity (a template).2) An input pattern is compared to the storedrepresentation.3) Identity is determined by selection of thetemplate with the greatest amount of overlap. Page 3 4. II. Template ModelB. Schematic of a Template System Stimulus BrightnessA DetectorTemplatesLight Source II. Template Model (cont)C. Template systems in actionTemplate Model (cont)D. Problems with template models1. Intolerance to deviations2. Large number of templates required3. Cannot support similarity-differencejudgmentsPage 4 5. III. Feature TheoriesA. Basic Assumptions1. The stored representation is a descriptionof past inputs in terms of list of attributes orfeatures.2. Inputs are broken down into a small list ofconstituent features.3. Identity is determined by selecting thefeature list most similar to the input.III. Feature Theories B. Schematic of a Feature ModelStimulusIII. Feature Theories (cont)C. Supporting Evidence 1. Hubel & Wiesel (1962): Recorded electrical activity in the visual cortex of the cat. Page 5 6. Hubel & Wiesel (1962) Results: specific cellsrespond to specific visual features.III. Feature Theories (cont) B. Supporting Evidence (cont)2. Letter recognition timesGibson, 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 thesame 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 thesimilarities in features.Step 1: Feature Analysis of LettersPage 6 7. Step 2: Letter Groupings based on RT III. Feature Theories (cont)D. Criticisms of Feature Theories1. Importance of Context 2. Importance of Arrangement IV. Top-Down vs. Bottom-up ProcessingcomprehensionBottom Up phrase processingTop Down(data driven) word processing(conceptuallyletter processingdriven)feature processing Page 7 8. IV. Top-Down vs. Bottom-up ProcessingIn Control of Attention (Bushman & Miller, 2007) implanted electrodes in monkeysthe monkeys were trained to search for a target in avisual displaythe researchers measured reaction time and recordedfiring rates in parietal cortex (25 electrodes) (visual-sensory information) and the prefrontal cortex (25electrodes).IV. Top-Down vs. Bottom-up ProcessingBushman & Miller (continued)Bottom up: visual pop-outSensory neurons (parietal) responded first Page 8 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 thesensory cortex. Top down processing signals begin in the frontal cortex. V. Neural Network Model of Word Pattern Recognition AnalysisA. Interactive Activation Model (McClelland & Rumelhart, 1981) Letter AnalysisIncorporates top-down processing from theword level to the letter level.Excitatory connections: FeatureInhibitory connections: AnalysisVisual Input Page 9 10. Simplied view of the Network of Connections Excitatory connections: Inhibitory connections: Word LevelCAT CHAIR THE Letter LevelA C H T E Feature Level InputMore Complete view of theNetwork of Connections:B. Supporting Evidence:The word/letter effectReicher (1969)Stimulus Example Test Percent Correctletterh h/t 78series csah csah/csat 76word cash cash/cast 89Page 10 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 TheorySolso & McCarthy (1981) 50%face recognition 25% Prototype 100% 0% VI. Prototype Theory (cont.) Solso & McCarthy (1981): results5 Old Items4New ItemsOld 32Confidence10 -1 -2 -3New -4 -5 1007550 25Percent Overlap with Prototype Page 11 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 facescomposite faces containing 2 - 32 faces. Examples of composite faces:Number in composite481632 Page 12 13. Rated attractivenessNumber of facesaverage rating12.5122.8742.8483.0316 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 minoritygroups later than majority groups, so theylearn to focus attention on features thatdistinguish 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: Caucasians (New Zealanders) 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. 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 timesminority 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 BsA 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. VII. Facial Recognition:A special problem for theories of patternrecognition:A. Different set of rules? (Example: object vs.facial recognition).Yin (1970), and Rock (1974) demonstrated thatfacial recognition is more easily impaired byinversion than is object recognition. Who is this?Page 15 16. A BPage 16 17. AB 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