Class Notes (839,112)
Canada (511,191)
Psychology (3,528)
PSY100H1 (1,637)
Nick Rule (7)

PSY421 - Lecture 2.docx

10 Pages

Course Code
Nick Rule

This preview shows pages 1,2 and half of page 3. Sign up to view the full 10 pages of the document.
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
More Less
Unlock Document

Only pages 1,2 and half of page 3 are available for preview. Some parts have been intentionally blurred.

Unlock Document
You're Reading a Preview

Unlock to view full version

Unlock Document

Log In


Join OneClass

Access over 10 million pages of study
documents for 1.3 million courses.

Sign up

Join to view


By registering, I agree to the Terms and Privacy Policies
Already have an account?
Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.