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Lecture

PSY421 - Lecture 2.docx

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Department
Psychology
Course Code
PSY100H1
Professor
Nick Rule

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PSY421: Person Perception Lecture 2: Race January 30, 2012 Depending on how you think about race, there are many ways to think about it (i.e. Princess of Sweden, skin – closer to Elisabetta Canalis, finger print – closer to Japanese lady, malaria resistance – closer to other race, etc.) Definition of Race:  Blumenbach  Invented “Caucasian”  Refers to people living at food of Caucasus mountains  “Most beautiful” = original humans, birthplace of humanity (considered most pure)  Divided into 5 different groups (based on beauty)  Linnaeus 1758, 4-group division 1. Caucasian 2. American 3. Asian 4. Malay (Blumenbach added this 5th group to the original group) 5. African  Latter 4 groups “degenerations” of original Caucasians 1. Result of adaptation to climates (i.e. Asian eyes = snow blindness) 2. Customs/artistic practices (e.g. skull-binding), could become hereditary 3. Changes could be reserved (over generations) by moving back  Not racist: 1. Believed groups to be equal in value 2. Abolish slavery  Black slaves > White masters (morally) 3. Hierarchy based primarily on beauty If race were based on genetics:  94% or more of DNA is identical across racial groups  Greater variation within groups rather than across racial groups  West Africans may be more similar to White people than to East Africans  DNA polymorphisms show 5 groups:  Sub-Saharan Africans  Europeans & Asians West of Himalayas  East Asians  Inhabitants of New Guinea & Melanesia  Native Americans  Phenotype vs. Genotype  Different looking brothers; similar genotypes, different phenotypes Race: Mostly based on appearance  Environmental adaptations (e.g. skin color)  Geographic origins  Racial variation confounded by geographic variation  “Clines” – incremental variations in traits across geographic areas  Similarity in appearance does not equal genetic similarity  Skin color of African/Aboriginal Australians  3 most important appearance traits in selecting a partner?  Personal ads: skin colour, hair colour eye colour Race: A moving target  Racial groups today may not be true  Can “see” differences where they may not exist  37% of babies described as Native American on their birth certificates described as another race on their death certificates  5 most salient racial groups in US in the late 1800s?  Whites, Blacks, Germans, Italians, Irish  Irish  White; Jews  White Race: A social construction  Important social variable  Proxy for SES, effects of racism  Eye of the beholder, eye of the beholden  How we see others and ourselves  How we assign and perpetuate group membership  Multidimensional Space Models (MDS)  Multiple dimensions are used to perceive and categorize faces into groups  Derives from multidimensional scaling  Statistical procedure  Used to distinguish between different wines  Using dimensions on axis  Each dimension has an axis on the model  MDS of faces:  Sex  Dimension 1 – Hair: long/short?  Dimension 2 – Eyes/Brows: close/far?  Dimension 3 – Brows: thin/Thick?  Dimension 4 – Jaw: big/small?  Dimension 5 – Skin: smooth/rough?  Multidimensional face space – Valentine (1991)  Every point represents a face  Every face ever seen is represented somewhere in the field  The dimensional code for basic aspects  Over time, add new dimensions as new faces/types of faces encountered (i.e. move to foreign country)  Typical faces close to original (similar to average or prototype)  Density of faces on each dimension normally distributed around origin  *Dimensions are theoretical  Norm-based encoding  Prototype at origin 1. All faces stored as vectors from prototype 2. Vectors account for distance between prototype and representations  Exemplar-based encoding  Origin empty 1. Cluster around origin – NO PROTOTYPE 2. Similarity between faces is monotonic distance between them (assume to be Euclidian)  Two white faces are closer together and further apart from Black faces  Direct comparison of the two types of encoding Norm-based Exemplar - Assume normal distribution - Assume normal distribution - Use prototypes (theoretical - Uses actual faces ideals) - Each face relative to - Everything relative to individual faces prototype - More parsimonious - First observed data better  Process of MDS  Perceive a face, attempt to map it to a location in the face space 1. Already there = know + recognize 2. Not there = acquire (learn) a new face + add to field  Number of dimensions in your face space is large enough to uniquely classify any 2 faces  Dimensions are always expanding, creating more space, and shifting location as new faces are acquired 1. When someone changes too much, that person‟s face becomes a new face in the field  Greater expertise with familiar faces, more dimensions that distinguish them (outgroup homogeneity effect) 1. Other-race faces often misclassified because limited experience = more dimensions 2. More faces in a location, longer it takes (actual experimental reaction times) to recognize it 3. Unique/distinctive faces recognized quickly but take longer to be recognized as a face  Atypical faces have larger fields 1. Will collect more percepts 2. Broader region of space allocated – look more like things  Typical/atypical 50% face morphs 1. More likely to think that the face is from atypical parent face than typical face  Relationships to race perception 1. Mind seeks efficiency, will only hold dimensions that are needed  Lots of D to tell ingroup apart, few to distinguish between other races  e.g. white people who watch basketball are better at distinguishing black people  Different groups have different face-space dimensions  Euro Whites: hair, hair texture (distinctive among Whites)  African Blacks: eye size, eyebrows, ears (distinctive among Blacks)  Face space easier for ingroup Race: Face-space dimension  Race may be a visual feature above face space (Levin)  Why do we identify some races faster than own-race faces?  Distinctiveness? Exemplar face-space models  Interferences? From configural cues for same-race faces 1. People don‟t code configuration/individuating properties of other- race faces (“all look alike”) 2. Dog breeders disrupted by face-inversion 3. Arcs in faces vs. non-faces more easily recognized 4. If so, inversion stronger for same race  Race is a visual feature? Processed before individuating cues – race/gender schemas more to recall info about people of different race/gender 1. Femaleness deviation from “maleness norm” – male is default assumption 2. Race ≠ Black/White, Race = Black/Not Black  Context/group size matters  Another face-space caveat  Out-group cannot be the majority  [People that don‟t look like me = all look alike; enough exposure = become expert]  Experiment 1-3  Atypical faces categorized slower 1. Famous/non-famous
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