Chapter 8 Lecture: Concepts and Generic Knowledge
Chapter 8 Lecture: Concepts and Generic Knowledge PSYC 3350
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This 19 page Class Notes was uploaded by lambdalambdalambdas on Wednesday June 24, 2015. The Class Notes belongs to PSYC 3350 at University of Houston taught by Victoria Wagner in Spring2015. Since its upload, it has received 246 views. For similar materials see Intro to Cognitive Psychology in Psychlogy at University of Houston.
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Date Created: 06/24/15
1072013 Chapter 8 Lecture Outline Concepts and Generic Knowledge 2010 by W W Norton amp Co Inc Chapter 8 Concepts and Generic Knowledge I Lecture Outline El Definitions II Prototypes and Typicality Effects El Exemplars El Difficulties with Categorizing via Resemblance II Concepts as Theories 39l ll Definitions I Concepts like dogs or chairs El Building blocks El Simple but complex to explain 1072013 Definitions I Dog El Definition I A mammal with four legs that barks and wags its tail El Exceptions I Dog that does not bark or that lost a leg i Definitions I Philosopher Ludwig Wittgenstein 1953 El Simple concepts have no definition El Consider a game I Played by children I Engaged in for fun I Has rules I Involves multiple people I Is competitive I Is played during leisure Definitions Games m Played by children Gambling Engaged in for fun Professional sports Has rules Playing with Legos Involves multiple people Solitaire ls competitive Tea party ls played during leisure Flying simulators 1072013 I Family resemblance Definitions Ideal member Atypical member I Definitions I A dog probably has four legs probably barks and probably wags its tail I A creature without these features is unlikely to be a dog I Definitions I The more characteristic features an object has the more likely we are to believe it is part of the category 1072013 I Prototypes El One that possesses all the characteristic features Prototypes and Typicality Effects Prototypes and Typicality Effects I Prototype El An average of various category members that have been encountered I Differ across individuals I May differ across countries El For example the prototypical house in the United States compared to Japan Prototypes and Typicality Effects I Prototypes II Graded membership I Some members are closer to the prototype El Fuzzy boundaries I No clear dividing line for membership I Which is the best red Prototypes and Typicality Effects 1072013 Evidence Favoring the Network Approach I The sentenceverification task I True or false El Robins are birds El Penguins are birds Prototypes and Typicality Effects I Typicality effects El Name as many fruits as possible El Name as many birds as possible Prototypes and Typicality Effects Does this picture show you a bird Insert typical bird Faster Insert a penguin Slower 1072013 Prototypes and Typicality Effects irun Rating Bima alrnq I The more Apple 525 Robin 539 Peach 53 Blut blld 5A2 Pear 525 Seanull 626 m em bers are Grane 535 Swallow 516 is Strawberry 500 Fa39iuom 52 Lemon 486 Hocksngbim 54 Elauehenr39y 156 St arlimg 526 WEIlQr39MtHOn 4 GE Owl 500 Rania1n 55 Vu ture 484 Fig 558 San DIDEJ 44 I39 Coconut 305 Chicken 395 Pomwgaurute 2 3930 F ommgc 35 avocado 238 ibauuss 332 Pumpkin MI Penguin 25 3 Olive 225 Baa 153 Prototypes and Typicality Effects Birds in a tree Imagine this Not this Prototypes and Typicality Effects I Typicality also influences judgments about attractiveness Which fish is the most attractive 1072013 Prototypes and Typicality Effects I Just as certain category members seem to be privileged so are certain types of category I For example what is this object Prototypes and Typicality Effects Example Detail Furniture Too general Chlair Just right Upholstered Too specific armchair Prototypes and Typicality Effects I Basiclevel categories El Single word El The default for basic level I Easytoexplain commonalities 1072013 a Prototypes and Typicality Effects I Basic categories are learned first I Used by children to describe most objects Exem plars I Exemplar El What is this Exem plars 1072013 Prototype symbolic Exemplar frequency Typicality Average of a category Encountered more often Graded membership Less similar to average How often it is encountered Illustration Ideal fruit apple Apples often vs less ideal fig vs figs not as often I Exem plars I Prototypes El Economical but less flexible I Exemplars El More flexible but less economical I Chinese versus American Birds I A gift for a 4 year old who recently broke her wrist I Exemplars Both prototype and exemplar provide information I Kermit the Frog El Prototypical features I Is green eats flies El Exemplar I Sings loves a pig 1072013 Il E39 Exem plars I Every concept is a mix of exemplar and prototype El Early learning involves exemplars El Experience involves averaging exemplars to get prototypes ElWith more experience we can use both Difficulties with Categorizing via Resemblance I Category membership and typicality II Prototypes that are based on averaged exemplars IIA process of triggering memories Difficulties with Categorizing via Resemblance I Moby Dick was a whale but not a typical one 1O Difficulties with Categorizing via Resemblance EVEN NUMBER ODD NUMBER Stimulus Typica lity rating Stimulug Typicality rating 4 59 3 54 8 55 7 51 10 53 23 46 3918 44 57 44 34 35 501 35 106 31 44739 33 The category is clear and yet typicality goes down 1072013 Difficulties with Categorizing via Resemblance I Atypical features do not exclude category members El For example a lemon that is painted with red and white stripes injected with sugar to make it sweet and then run over with a truck is still a lemon Difficulties with Categorizing via Resemblance I All the typical features but not category members III For example a perfect counterfeit bill 11 1072013 Difficulties with Categorizing via Resemblance I Similar examples come from studies with children IIA skunk cannot be turned into a raccoon I It has a raccoon mommy and daddy IIA toaster can be turned into a coffeepot I Just need to poke some holes in it Difficulties with Categorizing via Resemblance I Essential properties IIThose that define a category ElWhiCh are those Concepts as Theories I Resemblance II Prototypes and exemplars work El Not enough I Perfect counterfeit bill resembles a bill but is not 12 1072013 5 Concepts as Theories I Heuristic IIA reasonably efficient strategy that works most of the time I Prototypes and exemplars 5 Concepts as Theories I When heuristics fail may need a more complete view I Conceptastheory Concepts as Theories Real airplanes resemble whipped cream airplanes 13 Concept as Theories 2010 by w w Norton amp 40 Co Inc 1072013 Concepts as Theories I Concepts are like schemas EIThey allow people to form generalizations II Related to typicality I Generalizations more likely from typical cases I Robins are more likely to be like all birds I Penguins are less likely Concepts as Theories I Theories also explain cause and effect Lion Gazelle 14 1072013 Concepts as Theories I Natural kinds and artifacts are reasoned about differently El Natural kinds eg the skunk and raccoon have essential properties IIThese principles do not apply to artifacts eg toaster and coffeepot Concepts as Theories Categories represented in different brain areas 44 I Knowledge Network Knowledge is represented via a vast network of connections and associations between all of the information you know 15 Knowledge Network I Other evidence for the knowledge representation in a network comes from the sentenceverification task I Participants must quickly decide whether sentences like the following are true El Robins are birds El Robins are animals I Cats have hearts I Cats are birds 1072013 Knowledge Network I Cats have hearts requires two links I Cats have clawsquot requires one link EllH EATHE HM39E H EARS5 EA39IT FOOD mm FEW E SKIN AT DOGS 3mm 7 can FLY C S i HAVE cmws Y CHASEGA39ITS LAYIEGG39S Dunn EWHK ROBIN CANARY CHESHIRE ALLEY CAT COLLIIE TERRIER Y CAN SlNG is YELLOW tam Knowledge Network Reaction time goes up for longer associative paths 1 EIUCI Mean response time Ms 9m H Fromm LI 39 quot calmer I I I U l 2 Levels to be Lrauurgad 16 Knowledge Network I Nodes can represent concepts I Links such as hasa or isa can associate each concept 1072013 Knowledge Network CHEW Relation Relation Proposition smallest unit that can be true or false Four propositions about dogs Knowledge Network LAST SPRING JACOB MGEONS Relation Flelation FEEDS Loca on TRAFALGAH SQUARE Abstract knowledge represented via time and location nodes 17 1072013 5 Knowledge Network I Propositional networks I Localist representations each node is equivalent to one concept I Connectionist networks I Distributed processing information involves a pattern of activation El Parallel processing of information occurs at the same time 5 Knowledge Network I How does learning take place in a con nectionist or parallel distributed processing PDP network EIChanges in the connection weights or strength of connections 5 Knowledge Network I Learning algorithms how weights are changed I Both nodes firing together strengthen their connection El Error signals cause a node to decrease its connections to input nodes that led to the error back propagation 18 5 I In sum concepts are central to human reasoning but are complex I We often reason about concepts using prototypes and exemplars particularly in cases where fast judgments are required I However for more sophisticated judgments we also employ theories represented by networks of interrelated conceptual knowledge I Finally various computational networks have attempted to capture this complexity Concepts 1072013 19
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