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Introductory Psychology

by: Danial Kreiger

Introductory Psychology PSYCH 100

Danial Kreiger
GPA 3.75


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Class Notes
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This 29 page Class Notes was uploaded by Danial Kreiger on Friday October 30, 2015. The Class Notes belongs to PSYCH 100 at University of Massachusetts taught by Staff in Fall. Since its upload, it has received 18 views. For similar materials see /class/232305/psych-100-university-of-massachusetts in Psychlogy at University of Massachusetts.


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Date Created: 10/30/15
Concept formation Categories and concepts PCategory set of entities things actions properties In the world 1 Concept our mental representation of a category 7quot Are categories arbitrary Or based on What is in the world Borge s Taxonomy of Fauna Taken from Lakoff Women F ire andDangeraus Things The taxonomy of the animal kingdom from The Celestial Emporium ofBenevolenZKnowledge On those remote pages it is written that animals are divided into a those that belong to the Emperor b embalmed ones c those that are trained d suckling pigs e mermaids f fabulous ones g stray dogs h those that are included in this classi cation i those that tremble as if they were mad j innumerable ones k those drawn with a very fine camel s hair brush 1 others m those that have just broken a ower vase 11 those that resemble ies from a distance J L Borges 1966 Otherlnquisitions What are the functions of concepts P Permit inferences gt Allow reasoning about new things P Support communication gt Add new information about concepts r Permit economy of communication Classical approach to concepts P Concepts are arbitrary set by culture PThey have necessary and sufficient features defining features PHypothesis testing to learn defining features gt Example color cate ones gt Demonstrations classical concept foimation experiments JL I J iJfl J iJfl J iJfl J iJfl J iJfl a Figure 83 p 269 Different abject aH Chairs Eleanor Rosch s position The Roschian revolution Observation 0 Wittgenstein you usually can t nd de ning features for categories Observation 1 an entity usually belongs to several different categories gt But one is BASIC Observation 2 categories have better and worse members 9 Categories don t have xed boundaries Basic Level Terms And superordinate and subordinate terms PTaxonomies of categories gt Animalmammaldogcollie v Artifactfurniturechaireasychair P Rosch s observations gt Basic level the preferred name gt Fastest to verify nameobject match gt Learned rst gt Shortest gt ASL single sign mm A x cm max 561 l mm mung Klkhzu Dunn smx Dnuhla mm mm lay Figure 810 p 279 Supamrdvmu Mm l am my Subamumle rm Gum mun van in idea that the bash level is psychumgmauy mugged Rm w Basic level principles PBasic level the highest most inclusive level at which all instances share a large number of features PFeature listing Superordinate nniture few features gt Basic level chair many features gt Subordinate easy chair only a few more features PHemenway members of basic level categories have parts in common not so for superordinates Underlying principle P Jointly maximize informativeness and discriminability of categories gt Informativeness what do you know given that you know that X is in category Y gt Discriminability how hard is it to tell that X is in category Y rather than category Y P A tradeoff r thing easy to discriminate but not very informative gt son of Citation very informative but not easy to discriminate gt horse basic level jointly maximizes both iiiiiiiiiiiiiiiil ii iiii iiiiii ii Last points about basic level P Expertise matters gt Dog experts as fast on subordinate term as basic term Their basic level may really be our subordinate term Distinctiveness and inforrnativeness increased for them Li Li H 51 Li L U 51 Li Li Li in Li Li i U 51 Li L U L1 Typicality and category membership W i thincategory structure PRelations between categories Basic level analysis PWithincategory relations Typicality and graded category membership Rate the typicality l 3 Fruits quot Blrds Apple Robin 625 689 Fig quot Bat 338 153 Olive 9 Flamingo 225 337 Grape 39 seaguu 513 626 Typicality effects P Time to identify name as category member 9 Time to identify picture as category member L fEEase of learning category membership PName members of category frequency of naming 9 Similarity ratings asymmetry less typical members are more similar to more typical members than VV Prototype models Alternative ideas ofprototype F Idealized abstract form 3 Some kinds of instances PMental representation of ideal form or instances Similarity Feature overlap features produced by students Bird Features Robin Swallow Vulture Flamingo ies sings lays eggs is small nests in trees eats insects SIM TO BIRD 6 6 2 6 An application of typicality A Psych 100 trick P Or is it really thought transmission Thought transmission P I ll pick number between 10 and 50 Pit has to be odd Peach of its digits has to be odd lPits two digits have to be different gt 11 is not good D 115 is OK P I ll write it down and think about it P Then YOU write down what I m thinking 37 Exemplar theories of concepts These theories contrast with prototype theories PPrototype theory claim You lean some kind of abstraction some single mental representation of a category PExemplar theory claim You remember individual instances exemplars of concepts and compare new things to these remembered instances to categorize these new things Exemplar theories of concepts Their virtues P Explain typicality effects very well Permit mathematical models Supports quantitative predictions of choice learning 1 Accounts for feature variation effects 7 pizza vs quarter if Accounts for feature covariation effects quot little singing birds vs big nonsinging birds Medin et al 1982 Medical diagnosis P EMWeight gain puffy eyes stiff muscles splotchy skin gt Terrigitis P RL Weight gain sunken eyes stiff muscles skin rash gt Mdosis P AM Weight loss sunken eyes stiff muscles skin rash I Midosis P L F Weight gain sunken eyes muscle spasms skin rash i Terrigitis P J J Weight loss sunken eyes muscle spasms splotchy skin I Midosis P S T Weight loss puffy eyes stiff muscles splotchy skin I Terrigitis P plus 2 more Medin et al cont d Test phase P Weight gain sunken eyes stiff muscles splotchy skin v Terrigitis 67 P Weight loss puffy eyes muscle spasms skin rash Igt Terrigitis 75 P Weight gain sunken eyes muscle spasms slotchy skin b Midosis 72 P THE POINT Terrigitis is characterized by both stiff muscles and splotchy skin or by both muscle spasms and skin rash Midosis is characterized by both stiff muscles and skin rash or by muscle spasms and splotchy skin People pick this up pretty well But what they learn is COVARIATION between features not just features of a prototype Strengths and weaknesses of exemplar models P Strengths gt Accounts for typicality effects PLUS variability and covariability effects gt Permits development of explicit accurate mathematical models P Weaknesses gt Supporting data come from odd experiments that encourage instance memory People CAN form abstractions Model doesn t address main points of why we have concepts economical prediction Exemplar models depend on how you compute similarity ll HH il lii HH V V V Concepts as theories Murphy amp Medin P Theory as glue that holds concept together P Theory as whatever principles tell you which properties should be important which unimportant gt And WHY gt That is they tell you how to compute similarity Evidence for concepts as theories PFacilitation in learning concepts eg thing that can be used as a hammer prey vs predator PAbility to form ad hoc categories PBelief in essences essentialism PKeil transformation studies children s concepts More evidence for concepts as theories P Illusory correlation Chapman and Chapman PClinicans diagnoses of mental disorders Kim and Ahn gt DSM lV checklists of symptoms presumably theory free gt But diagnosis is categorization gt Individual clinician s diagnosis affected by hisher theory of the disorder Associative Theories of LTM FENetwork models 57 Connectionist models Collms and Qu llan expt Reaction Time to Verify P Propertyquesuons 39 An oak has acorns 7 distance 0 gt A spruce hasbmnches 7 dxstance gt A birchhas seeds 7 distance 2 Category questions gt Amapwe gmuwu won Hanqu w uwmmu nmmmm Wu game 29 muumsm memm mum w w wwvwm WWW lvualawq mm mm g m u rm 3 H Merver mm H W mu m m C mm J u 7 ma Amy u 7 3 mm 39 i A my mm is 3 4 mm 12cm 3 a Hun mm if my mm a tvivevsb Spreading activation PA metaphor a concept can be active to a greater or lesser degree PIS activation spreads to related concepts P Priming PDistance effects P Fan experimenm Fan experiments John Anderson Smith Adams amp Schorr 1978 P Learning materials gt Marty broke the bottle Marty did not delay the trip gt Herb produced sour notes Herb realized the seam was split gt six more pairs P Test materials gt TF Marty broke the bottle Herb did not delay the trip MEASURE RT accuracy Fan 2 links Broke the bottle Did not delay the trip I Marty Fan Experiments phase 2 PAugmented learning materials gt Marty broke the bottle Marty did not delay the trip Marty was chosen to address the crowd gt Herb produced sour notes Herb realized the seam was split Herb painted an old barn r six more pairs P Test phase gt Same as before Marty broke the bottle etc Fan three links Broke the bottle I Did not delay the trip I Was chosen to address the crowd I Marty Fan experiments P The time to verify the original facts was longer when the third fact about each individual was added PInterpretation The activation spreading from the Marty node was divided among more other nodes 3 not 2 Therefore these other nodes were activated more slowly The paradox of the expert Pthe start of the paradox learning some material slows you down in remembering other material and even causes you to forget the other material retroactive inhibition gt So who should have the hardest time remembering material I well the person who has learned the most other material an expert gt but experts seem to have the BEST memory for a topic not the worst The Paradox of the Expert cont Pthe start of the solution schema theory gt experts don t have random facts they have integrated schemas about their topic gt perhaps random facts interfere with one another but an integrated schema helps integrate and hold new facts Fan Experiments Phase 2 version 2 PLearning materials augmented with thematicfacts gt Marty broke the bottle Marty did not delay the trip Marty was chosen to Christen the ship gt Herb produced sour notes Herb realized the seam was split Herb played a damaged bagpipe P Adding this thematically third fact did not slow and may even have speeded veri cation RT Connectionist neural net models Simple models complex results P Cognitive tradition symbolic models gt Symbolic representations gt Information processing rules for operating on representations PNonsymbolic models gt Simplify theoretical assumptions gt Still get complex results Connectionist models Components of a connectionist model PNodes P Connections gt Strength of connections P Activation Axow NEURON CELL sour AXONS FROM A UTHER NEURONS r DENDRITES igum 11 A chemati neumn A node 1 o A connection with a weight quotn Localist Connectionist Models PA node represents an entity stimulus response individual idea concept PActivation is transmitted between nodes gt One node can either excite or inhibit another node P System provides a computational foundation for associative networks Input nodei Output node j 12 3 12 3 W33 Connection weights wij Delta rule perceptron convergence procedure etc Rule for adjusting weights based on instruction feedback Previous examples of localist models PWord recognition models v Hierarchy of feature letter bigraIrL word detectors McClelland and Rum elhart connectionist model 7 With bottomup excitation from input opdown excitaxion from Words and lateral inhibition 311 A mm mm mm mm mm m mu sum mwv w l mm weather bigmm mimm l m m M m m wk my Themequot om n in mm pmmwi a pm umnmm nmv u rImv mused w Mimi Wm Mlt Llnnd and Kumdharl ml nifth p1 r deleclvn hlr lens vmblxmuans lmmn leuv ileum lemmnnnnnn mlvdd nn no a awn name lama nnnlnn amnnmnmnamam r n r m anquot 5 rpm M m an man a V a lawn mam mrnrranm walnnn Mama mmnrr rm Learning in a Localist Model PHebbian leami gt Learn covan39ation in node activiation gt Rule strengthen connection between nodes that are simultaneously active Weaken other connections gt Result activation ofone node predicts activation of othe nodes P Guided instructed learning gt Example set Weights in semantic net gt Delta rule strengthen Weights that result in activation oftarg et output node weaken other Weights 7 Backpmpagation algorithm allow strengtlleningweakening to apply to earlier connections in net Distributed Models PParallel Distributed Processing PDP models v Represented an obj ect event 7 by pattern of ac 39Vation across nodes 7 Not by anod s as in alocalislmodel Menioiyi set of ctivation Weights of connections betweennod e Penuits aparticular panein ofaclivarion to develop given an mgu v Underlying metaphor brain structure 7 Node corresponds to nenmn links con espond to synapses Cognmv Adivuy in Arti ml Neunl Network 203 OUTPUT UN TTS HIDDEN UNITS mm ommm xnm cu39HP INPUT UNITS n5 1 A mph mwmk Connectionist models cont PA hybrid model the minerock detector P Hidden layers Increase computational power PLeaming models gt Guided instructed learning 7 Delta mle r Backpropagation i GHQ Ex mwsn rnznuzncv Hgm 15 peneotual kmgmhon wuh i hrge nulwnlk Advantages Disadvantages P Attractions Neural plausibility gt Parallel processing gt Graceful degradation under damage gt Emphasize leaming Dis vantages Overlooks the stmeture that the hiain DOES have Don l give insight into rules the mind follows gt Don do as mucl abstraction as We seem to P Resolution Man s analysis day 1 ofthe course symbolic model the algon39thmie level eonneetionist model the implementational level


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