Cog Psych Study Guide Exam 3
Cog Psych Study Guide Exam 3 PSY 0422
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Cognitive Psych Exam 3 41715 1026 AM 316 Lecture 16 Language Challenges of speech Semantic start off with meaning Pragmatic how to say it adjust to listener 0 Making inferences about what the listener knows 0 Speak differently to a 15 month old than an adult Syntactic what grammar construction 0 Many different syntactically correct ways to convey meaning and we need to choose Lexical which of roughly 50000 words to use 0 Synonymous with words mental dictionary Phonological how do words sound 0 Element of sound 14000 syllables Articulatory how to physically say it Series of very fast memory retrievals Common problems in speech Disfluencies half of talking time pausing retracing hesitating o Re ects thinking and word retrieval problems Deadends tipofthetongue problems 0 Come to a dead stop and can t access a particular word that we know Speech errors many Influences on language production Memory need somewhere to start more available more likely to be used earlier Structure Memory in uences on production Just in time imperative use what s available when it s available 0 Use activated information to help us get over the challenge of what to select when Experiment suppose people listen to words and see a picture church in a lightening storm 0 Say what you see primed with associated word earlier in utterance 0 Thunder lightening hitting the church 0 Worship church being hit by lightening Strong evidence that whatever is activated in memory just before has an impact on what words we decide to use and when Use it or lose it Structure in language production Speakers generate language in syntactic chunks clauses noun amp verb phrases Evidence from pauses and hesitations 0 Mean pause length at clause boundary 1 sec 0 Shorter pause lengths within clause More evidence when speakers repeat or correct themselves they tend to repeat or correct a whole phrase repetition of units Example Turn off the stove the heater switch NOT turn off the stove off the heater switch we repeat a whole linguistic noun phrase How to study speech production Speech error corpora collections of naturally occurring and transcribed slips of the tongue such as Spoonerisms 0 Example The Lord is a shoving leopard to his ock 9 not intended Various problems with using natural speech 0 Depends on listener judgments 0 Some errors hard to classify 0 Some theoretically important ones occur rarely Speech Errors in the lab Baars Motley amp MacKay 1975 subjects are asked to silently read word pairs Occasionally they are cued to read aloud as quickly as possible 0 Priming trials ball dome beak doll bus door bell dark 0 Critical trial darn bore people say barn door 9 larger error 0 Priming trials big dutch bang doll bill deal bark dog 0 Critical trial dart board 9 lesser error The error bard doard occurs only about 10 of the time compared with 30 for the barn door set Lexical biasz tendency to create words not nonwords Freud on Speech Errors Freud said speech errors arise from the concurrent action or the mutually opposing action of two different intentions Failure to censure unconscious intention results in speech error that expresses some aspect of the unconscious often sexual oedipal Cognitive Account Speech errors re ect properties of the language system They result from concurrent competing activation of certain linguistic units Failure to achieve correct speech production output allows insight into the structure of the language system and processing e g which units are concurrently active Levels of slips Errors happen at different linguistic levels 0 Sound errors rack pat and pack rat 0 Morpheme errors selfinstruct destruction and selfdestruct instruction 0 Word errors writing a mother to my letter and writing a letter to my mother Type of Errors Sound exchanges night life nife lite beast of burden burst of beaden coin toss toin coss Anticipation Errors take my bike bake my bike b sound takes over Perseveration Errors beef noodle beef needle Blends tab taxicab shruck structurechunk Two Important Speech Errors Lexical biasz tendency in phonological errors to create words rather than nonwords 0 Hold hard cash instead of cold hard cash likely o Weautiful boman instead of beautiful woman less likely Mixed errorsz tendency to produce errors that are semantically and phonologically related to the intended word 0 Apricot instead of apple share same beginning sounds Interpretation Suggests that semantic and phonological retrieval must be interacting Perhaps selection of sounds feeds back to the earlier level and biases selection of words Interactive activation model PDP model with layers of nodes corresponding to semantic features words and phonemes All connections are bidirectional if we activate a word the meaning is activated and then the sound is activated etc Activation spreads through the network Word node that receives the highest activation is selected Phonemes that receive the highest activation are selected Example Semantics of CAT turns on word node corresponding to cat Also gives some activation to words for related concepts dog rat The word cat turns on the corresponding phonological nodes k ae and t 3 Phonological nodes feed back activation to the word layer strengthening cat and turning on rat and mat Cat should be most active followed by rat which gets activation from semantics and phonology Interactive Activation Model How does this model account for the ndings Lexical bias output of the model is determined by selecting the combination of phonemes with the highest activation level 0 Likely to be one that has topdown activation from a word node Mixed errors activation ows in both directions 0 Word selection is in uenced not only by topdown semantic information but also by backward activation from the phonological level 0 Mixed errors receive activation both topdown from semantic level and bottomup from the phonological level and are thus more likely to be erroneously selected 318 Lecture 17 Language Summary of Speech production Speaking involves coordination of many processes In uenced by memory and structure Speech errors provide cues as to how speech is produced Interactive model does best job 0 Suggests interaction between semantic word and phonological stages Understanding Language Overview Route to understanding language is similar to what we learned earlier about visual input Visual Processing light patterns 9 Visual features 9 Objects 9 Scenes Language Processing sound patterns 9 Phonemes 9 Words 9 Sentences Four levels of analysis Phonology sounds put together to form words production and perception of language sounds Syntax rules for order of words and phrases sentences Semantics meaning word sentence paragraph Pragmatics rules of conversation paragraph longer 0 Meaning is relative to concepts beliefs Phonology Phonemes individual speech sounds smallest unit of speech sounds Phonemes can be combined to form words 4 o Bat and Cat differ by one phoneme b and c Phonological regularities memorization or rules 0 We memorize all of these sounds and access them when necessary 0 Rules for combining sounds implicit knowledge I How to decide to pronounce plural nonwords Examples pg 294 Why is speech perception hard 0 Coarticulation parallel transmission of information multiple sounds continuous stream I We need to divide up those sounds into phonemes and identify them even though it s coming in a continuous stream Segmentation As native language speakers we easily separate the language into words Hearing the information doesn t give us insight on when sound begins and ends Where we segment speech language is critical Ex THEREDONATEAKETTLEOFTENCHIPS meaning you get out of this depends critically on where you separate the words How do we segment Motor theory production and perception linked backwards engineering perception is innate and special to humans Auditory theory speech perception derives from the general properties of the auditory system not species specific Phonology Summary When people speak 0 the sounds of separate words blur together much more than we think 0 we use our past experience with words to separate the words hypothesized to be regulated by implicit rules Syntax and Semantics Both are critical in understanding language Sentences are regulated by grammar and semantics Semantics is meaning of words 0 Example turtle green animal with hard shell Grammar is a set of rules that structure language 0 Example in English the order of a sentence is subject verb object Example of phrase structure draw out S 9 NP VP Semantic Grammar Distinction the brain also makes a distinction between semantics and grammar as parts of complex language Patient Evidence Damage to Broca s area 9 grammar impairment Damage to Wernicke s area 9 semantics impairment Our brains appear to parse syntax grammar separately from semantics meaning Understanding Text Involves building a representation of the whole text You can think of this representation as a propositional representation pattern of symbols that make up a meaningful declarative sentence and often can be thought of as true and false 8 The hippie touched the debutant in the park 9 So 10 she slapped him Evidence for propositional representations Memory load experiment more propositions require more memory and so harder to remember Priming experiment Geese crossed the horizon as wind shuf ed the clouds o Priming is stronger within a proposition geesehorizon than across propositions geeseclouds Language and Ambiguity Types of Ambiguity Lexical ambiguity a word has more than one meaning Surface ambiguity alternative meanings have different phrase structures Underlying ambiguity alternative meanings have the same phrase structures Immediacy of processing Leads to slower or more errorprone processing with ambiguity Syntax and Semantics Summary We try to understand the syntactic structure of a sentence on a wordbyword basis building propositional representations in memory Shows how language and memory come together This involves making some guesses Sometimes we are fooled when the best guess or most common choice is wrong 323 Lecture 18 Language bridging to concepts Using eyetracking to study garden path sentences Garden path sentence after the musician played the piano was quickly taken off the stage 0 People slow down when they read the inconsistent information later in the sentence 0 This supports the immediacy of processing idea that we construct representations of text immediately while reading each word of the sentence 0 Surfacelevel ambiguity Nongarden path sentence after the musician bowed the piano was quickly taken off the stage Syntax and Semantics Summary We try to understand the syntactic structure of a sentence on a wordbyword basis building propositional representations in memory Shows how language and memory come together This involves making some guesses Sometimes we are fooled when the best guess or most common choice is wrong Pragmatics 4th level phonology syntax semantics pragmatics Making sure that people understand what is said and what is meant People follow rules in conversation to help the listener know what is meant Conversational Maxims People are assumed to cooperate in a conversation and therefore follow rules 0 Relevance be relevant Quantity be informative Quality say what s true Manner and tone be clear OOOO Relations with partner infer and respond to partner s knowledge and beliefs 0 Rule violations signal intentional violations Indirect Speech Acts Example Parent Where did you go Teenager Out Parent What did you do Teenager Nothing Review Sounds of separate words blur together much more than we think they do and we use our past experience with words to separate them e g implicit rules Our brains appear to parse semantics meaning separately from syntax grammar Meaning is the memory units we activate and grammar is the way we connect the meanings together in working memory Understanding a text involves building propositional representations and language ambiguities give us insight into how we process text Pragmatics complex way we use the situation rules of conversation to get at real intended meaning Language and Thought SapirWhorf Hypothesis Strong version language determines thought 0 Can t think things that you don t have words for 0 Too strong language is exible I Kiriwina has word mokita which means the truth that everyone knows but no one talks about I Ojibwe has word inaabate which means the smoke that curls there 0 Can express in English though no speci c word Weak version language favors some thought processes over others 0 Will tend to think in ways your language suggests Testing the hypothesis color English has many color terms Other languages have as few as two 0 Dani of Indonesia mili dark mola light If the SapirWhorf hypothesis is correct then expect that perception of memory for color should be different for Dani speakers and English speakers How Test two groups using color chips o Naming colors different e g amount of agreement but memory for colors very similar 0 Make same kind of errors 0 Do best for the same prototypical colors Results do NOT support the strong version of the SapirWhorf hypothesis 0 But color may be a bad choice strong biological component Weaker hypothesis Two related points Processing costs some things easier to say in some languages Thinking for saying if language requires some distinctions to be made then people speaking that language need to pay attention to those aspects of the world Summary Language and Thought Language and thought are often closely related 0 Our thought processes get re ected in our language 8 0 We use linguistic codes to help us think But language is exible enough not to constrain thought as strong versions of the SapirWhorf hypothesis would suggest However language can make information more or less accessible and having verbal labels can help in some tasks 325 Lecture 19 Concepts Ch 10 Importance Functions of concepts Concept mental representation of a class Category examples picked out by that concept Classification decision that an item is in the class Reasoning and cognitive economy don t need information for every instance Prediction go beyond given information Understanding explanation Building blocks for complex concepts Communication Conceptual representations and classification How do we classz39jjz Theories Classical view Probabilistic view Exemplar view Classical View All instances of a concept share common properties that are singly necessary and jointly suff1cient If an instance has all these properties it is a member of the category If not it is not a member Unitary Description true of all members of the category single representation of a category in our mind that s true of every member within it Classical View Benefits eff1cient storage of concepts clear category boundaries decisions Problems failure to specify def1ning features fails to capture typicality effects many unclear cases Probabilistic Prototype View Unitary representation that includes features usually true of instances of that concept Example bird feathers beak wings can y two legged lays eggs Prototype summary representation including all the typical properties 0 Used to represent the concept classify new instances and reason 0 The prototype is the central tendency of the category Prototype and family resemblance Similar to an extended family Structured by family resemblance Prototype consists of features that occur with many of the instances 0 Best example or most ideal member of the category 0 Do you think of the most ideal version of a category Similarities of classical and prototype unitary representations consisting of a list of features classify based on those features neither can account for context frequency variability or correlation of features Differences of classical and prototype allornone for classical typical features for prototype prototype can account for typicality family resemblance effects and classical can t clear category boundaries for classical Family resemblance and typicality In artificial categories family resemblance predicts 0 Ease of learning how easy we learn the category or new object o Categorization RT after learning 0 Typicality ratings 0 Output order e g furniture usually generated rst Context Sensitivity Example At Thanksgiving time someone says The bird is in the oven what s the bird Turkey more typical of an answer Probabilistic View accounts for typicality unitary representation Problems 0 Lack of sensitivity 0 Not sensitive to many factors that people are sensitive to I Frequency of instances Variability Correlation of feature values Exemplar View Representation consists of separate disjunctive descriptions of some of its exemplars No unitary description Explicitly disjunctive can use different information for different classifications DOG collies or spaniels or Fido Exemplar View Different ways of making concrete 0 Most similar exemplars 0 Set of most similar exemplars o All exemplars weighted by similarity Can predict many prototypelike effects Can predict in uences of frequency variability correlation of features 10 Realworld evidence physicians res think about the speci c re they ve seen in the past and determine what to do in a new situation of a re based on that past situation Exemplar view explains a lot but People have abstractions germ positive feedback loop Do people have abstractions in disjunctions If so how Why do these exemplars go together Beyond similarity Theories Similarity not enough Need for explanatory knowledge DEMO mutilated items Item I Raccoon paint it black paint a white stripe on its back 0 Operate put super smelly yucky stuff in its tail Item 2 Coffee pot punch holes in it pull off spout hang from tree ll with bird seed Why going beyond the similarity of observable features 330 Concepts are organized by theories Theory View To decide if something is a member 0 Not match of features rather ask if it ts Role of similarity o A good heuristic guess 0 Deep and surface are correlated 0 However if similarity and knowledge con ict people tend to go with knowledge Demo reexamining classical view What is a bachelor 5year old boy Pope No Concept of eligibility we have a theory of what bachelors should be Summary of Conceptual Representations Classical Probabilistic Exemplar But similaritybased may not be enough need to take into account knowledge Betweencategory structure Hierarchy 0 Levels with setinclusion o 1 Tree maple tree sugar maple tree 0 2 Vehicle Car Sedan Property inheritance properties of higher levels inherited at lower levels 0 Properties of tree true of maple tree 11 Taxonomic organization draw diagram example 9 Basic level Most people name objects in same way Basic level of representation common to people 0 Superordinate above Basic larger category furniture 0 Subordinate below Basic rocking chair Rosch s Explanations two opposing principles Informativeness 0 Want as much useful information as possible better for prediction explanation about that object 0 Get more information the further down you go 0 SO 9 lower is better Laziness 0 Want to use as little effort as possible 0 SO 9 higher is better Basic level is the best compromise Evidence for the basic level Superordinate levels are pretty much useless where as basic and subordinate levels are more useful subordinate is most useful Overlap of attributes feature listing Motor movements Shape similarity silhouettes Identified first as this level Acquired first Basic level can change with Context If you re in a chair shop you have to ask for a type of chair not just chair Expertise Dog trainers are as fast on subordinates as on basics What makes a basic level Rosch compromise between maximizing information and minimizing effort information and laziness Highest level at which entities share parts Differentiation Differentiation Hypothesis two principles 1 Specificity maximize information want to get as specific as possible 2 Distinctiveness do not want it confused with likely contrast categories categories at SAME level 12 Superordinate not informative but distinctive Basic informative and distinctive Subordinate informative but not distinctive Basic level usually has the most distinctiveness but if items are more distinctive at the subordinate level expect that level to be faster than basic Superordinate example fruit vs vegetable most distinct Basic example apple vs orange vs very distinctive Subordinate example Macintosh apple vs Jonathan apple vs hard to differentiate at this low level Atypical subordinate example informative and distinctive have features that make them distinct from other subordinate categories Murphy and Brownwell 1985 Exp 3 Implications Basicness is not a property of entire level 0 Though there may be a level used for most typical items Why might distinctiveness be important 0 More accessible useful for prediction 0 Communication Complications Knowledge Basicness depends on expertise Basicness depends on measure 0 Can identify at one level e g bird 0 Reasoning implies basic at lower level robin 0 Why Types of conceptual organizations Taxonomic different levels of abstraction 0 At each level an example belongs to one set of mutually exclusive categories Thematic script items share a common association 0 Script based function of human activity Ad hoc category members that serve some goal or ideal Summary Conceptual representations classical probabilistic exemplar Theory view goes beyond similarity Between category structure 0 Hierarchies and basic levels 0 Explanation differentiation 13 Type of conceptual representation 41 Problem Solving Ch 12 What is a problem o Givens initial conditions objects information 0 Goal desired outcome 0 Operators means of transforming conditions 0 Obstacles no simple direct known way from givens to goal The Tower of Hanoi Problem move stack to far right peg one disk at a time a disc can t be placed on a smaller disk Newell amp Simon approach Information processing system IPS Memory constraints 0 STM limits 0 Time to read out of LTM retrieve 0 Time to write into LTM encode Serial Processor basic thinking operations of problem solving is done on a serial basis comparing 2 items happens in a very serial fashion problem solving is an explicit deliberate conscious act Global program organization function of goals and knowledge are broken into parts Information Processing System IPS 9 Problem Space 6 Task Environment 0 Task Environment objective problem in the real world 0 Problem Space interaction of IPS and task environment Problem Space internal mental representation of the problem States description of problem elements 0 Initial state where we begin what we re given 0 Goal state where we want to get to 0 Intermediate states between initial and goal states Operators legal moves in space Apply operators to current state to try to reach the goal Search in problem solving Algorithm systematic procedure guaranteed to lead to correct solution 0 Examples addition maze 0 Problem computational complexity Heuristic strategy to guide search but not guaranteed to lead to correct solution 0 Examples hillclimbing working backwards 0 Problem no guaranteed solution 14 Heuristics simple ones too simple always moving toward goal but sometimes need to plan Need to break up problem into meaningful parts subgoals and accomplish those Common heuristic Meansends analysis Basic idea look for difference between where you are in problem space initial state and where you want to be goal state try to eliminate the difference getting rid of big differences f1rst select operator to reduce difference Apply operator 9 new current state Hill Climbing Heuristic simple search at each step go in direction that takes you closest to goal Fails to capture full power of human problem solving no heuristic that bases its decision on a single step could solve a problem reasonably Working Backwards Heuristic working from the goal back to the givens In some situations number of possible directions to go from givens is large and dif cult to know which to choose better to start at goal and ask what would need to be true for goal to be true Priortothegoal statements what would need to be true for each of these statements to be true Keep working backward until these statements are satis ed by the givens once that set is true it implies the net one is true and so on Problem Representation Main idea representation is crucial Representation can 0 Guide search for relevant knowledge 0 Determine which properties to focus on Maj or in uence on whether a problem can be solved 46 How can a representation matter Must be through problem space States Exclude relevant parts include irrelevant parts Operators Affect dif culty of applying operators Affect obviousness of some strategies What leads to representations Conventions norms that a given culture use to represent certain types of information Wording order of words 0 Expertise Schema 15 Representation is a function of the input and activated knowledge understanding How to examine representation and search during problem solving Problem solving collect verbal protocols during solution talking aloud tricky Computer program mimic the steps constrained by principles Analogy involves relational and structural similarity similarity in the relationships that hold among the features in an object May underlie much of our learning Important part of problem solving transfer Correspondences Military Problem Tumor Problem Goal use army to capture fortress Goal use rays to destroy tumor Resources large army Resources powerful rays Operators divide army move army attack Operators adjust ray intensity move ray warmy source administer rays Constraints unable to safely send entire Constraints unable to safely use intense army along one road rays from one direction Solution send small groups among multiple Solution roads Outcome tumor destroyed by rays Outcome fortress captured by army Why is performance so low Not paying attention No Do not understand the principle No Did not think to use the General story Yes people didn t think the military story was relevant to the tumor problem So failure to notice Hypothesis contentdependence of knowledge much of what we learn depends on specific content Many different domains in LTM 0 Bicycle experiences 0 Military strategies 0 Classroom jokes 0 Medical procedures Why so hard People often seem driven by surface similarity 0 Not so good transfer bt tumor problem amp military problem structural similarity 16 o Other research shows that much better transfer between tumor problem and problem in which there is both structural and surface similarity What is the role of surface and structural similarity in accessing and using earlier examples How to test A major dif culty when making an analogy across domains is accessing the prior example and content dependence of knowledge Vary surface and structural similarity of study item lightbulb to tumor problem Vary surface similarity vary instrument 0 Laser vs ultrasound Vary structural similarity vary constraint 0 Fragile glass vs insuff1cient intensity Surface Cue Structural cues Laser Ultrasound F ragileglass High surface High structural Low surface High structural Insu icient intensity High surface Low Structural Low surface Low Structural Procedure Read about one of 4 light bulb story comp write summary given tumor problem to solve Testing N0 Hint Surface Cue Structural Laser Ultrasound Mean F ragileglass 69 3 8 54 Insu icient intensity 33 13 23 Mean 51 26 Testing Hint Surface Cue Structural Laser Ultrasound Mean F ragileglass 75 8 l 78 Insu icient intensity 60 47 54 Mean 68 64 Implications Both surface and structural similarity aid retrieval of appropriate source Surface similarity helps people think of using analogous problem But structural similarity more important for drawing appropriate analogical inferences Contentdependence of knowledge 17 Problem with access Problem with understanding principles at abstract level Need to know not just the knowledge but when the knowledge is relevant Improving access Increased similarity Processed in similar way Abstract away from content e g compare examples Selfexplanations e g understand goal structure Generalization to schema Schema Induction process where implicit features of the analogy are made explicit Convergence Schema Goal use force to overcome a central target Resources sufficiently great force Operators change force intensity move source of force apply force Constraints can t safely apply full force along one path Solution apply weak forces along multiple paths Outcome central target overcome by force Summary Analogy Failure to notice reliant on content How to overcome access difficulty emphasis on explanations Expertise Ch 13 Descriptive perspective better able to answer questions faster Theoretical o Knows more 0 Better organization of knowledge 0 More experience 0 Better able to reason and solve problems in domain Why study expertise Importance of experts Applications Learning easier if know target performance Insight into cognition and individual differences How to study expertise Observe experts and have them talk aloud while solving something Compare to novices what do experts have that novices don t Computer simulations simulates different processes an expert might engage in 18 Chess and Calculators DeGroot did extensive analysis of ability tests for chess players of various levels 0 No difference except better players chose better moves 0 Don t consider more moves just better moves 0 No differences in memory ability except Chase and Simon Procedure Subjects were to reproduce the con guration of pieces on the reproduction board Chase amp Simon Results graph Not much difference bt experts amp novices People have different types of information that they remember they put them in chunks of information Experts and chunking When reconstructing board pause group pause group Chunks like in STM work Meaningful units Thought to have about 50000 patterns Patterns and moves associated with them 000 0 Fewer errors more time for strategy Perceptualmemorial view of expertise experts see things we don t see they see chunks when we just see pieces Expert calculator Aiken What is the square root of 851 Immediately 2917 win 15 secs 2917190429 How does he do it 0 Many numerical facts 0 Many calculating plans What is 1851 9 Factors 23 and 37 9 137 027027027 so divide by 23 Limitations and overcoming them Experts also limited 0 Cannot consider all moves 0 STM Working memory is limited Experts able to partially overcome 0 Guide search by experience 0 Use larger chunks in working memory 0 Try to make difficult into routine Much of expertise is due to immediate access of relevant knowledge Expertnovice differences in formal domains 19 Initial Analysis 0 Experts take longer to write equations 0 Experts more likely to draw diagrams Representation o Novices surface features objects analogy o Experts deeper features principles Strategies does vary with domain 0 Novices work backward from goal 0 Experts work forward from givens often with a plan Problem schemas structured knowledge for identifying problems of given type and associated procedures to solve them Allow for 0 Rapid categorization of problem type 0 Inference or elaboration o Topdown and working forward 0 Hierarchically organized knowledge Need both pattern matching and understanding of principles New and complex situations by de nition can t be learned in advance You have to be able to reason through what to do in such situations in chess against new opponents need new strategies Expertise relies upon understanding to adapt to new situations What makes an expert Better performance Immediate access of relevant knowledge 0 More of it declarative amp procedural organized chunks o Impacts initial analysis representation and strategies Need pattern matching and understanding of principles 413 Lecture 23 Expertise 2 Talent versus Practice Talent inherited predisposition to do well in some domain though clearly need practice and experiment Practice no inherited disposition matters all that matters is practice Deliberate Practice very speci c activity designed for a given individual often by a skilled teacher to improve performance Requires careful monitoring of details of performance Amount of deliberate practice often a good predictor of performance 20 Exceptional performers those with large amounts of deliberate practice Problems with the talent view Child prodigies are they the ones with the most talent o Often don t turn out to be exceptional many exceptional aren t child prodigies Are some talents inherited 0 Perfect pitch Can be learned Not clear what evidence there is for talent Debate on Talent vs Practice Practice accounts for much Can it account for all Dan Plan 10000 worth of deliberate practice to get to PGA Tour What about motivation 0 Alternative view talent is what keeps people interested and motivated Evidence Debate Box revealed that practice is necessary for gaining expertise 0 Simon estimates that a chess master knows well over 50000 patterns and it takes at least 10 years to become an expert practice helps partly by allowing the problem solver to learn the important conditions and actions of the domain and to make many of these actions automatic Review class evidence In uence of practice Related debate Intelligence Intelligence learned vs inborn Highly controversial social political implications education who should get it how much How much does each side contribute 0 Too coarse grain level question to ask 0 Understand the cognitive mechanisms associated with excellence Cognitive Training to Improve Intelligence General fluid intelligence ability to reason and solve new problems 0 Raven s matrices measures intelligence Study on cognitive training 0 Training on working memory task nback compared to control group 0 Results improves performance on Raven s 0 Implications people performed better after this training Summary of Expertise Limitations need to be overcome Expertise guides search uses bigger chunks uses different representations 21 Problem schemas Expertise involves perceptualmemory and understanding of principles Practice accounts for much of exceptional performance Creativity product solution that s original and relevant Creative solutions original relevant Why is creativity difficult to study 0 Mystical tradition dreams drugs muses insanity o Circularity and subjectivity hard to get a good independent measure 0 Hard to bring into the laboratory Fluency and Flexibility seeing connections What word is connected to all 3 of these Surprise line birthday 0 Remote Associates Test RAT Unusual uses test think of as many uses as you can for a paper clip 0 Score exibility type uency of uses originality how rare Traditional View Four Stages Walla 1926 Evidence anecdotal Poincare thinks of new set of functions when going on vacation 1 Preparation study area formulate problem Need knowledge from which to create need a database to create from Little creative work in less than 10 years expertise Even prodigies have many years of training before producing best 2 Incubation put unsolved problem aside Why might it help Recovery from fatigue Forget inappropriate approach Conscious problem solving during that time Unconscious problem solving set it aside mind is still working to get solution Evidence very little any that exists is mixed some supports some doesn t 3 Illumination achieve insight into problem Insight often rerepresent to make progress in generating a solution Many riddles like this get sudden insight when thinking about them in right way Standard technique for generating ideas analogies relaxing constraints 4 Veri cation making sure the solution works Sometimes brilliant insights are wrong Evaluation Traditional View Fascinating to read about No good evidence for stages as proposed 0 Not always differentiated 22 o Often in different orders interleaved back and fourth 0 Many creative acts not like this 0 Hard to tell how accurate Not clear this is right type of explanation Cognitive views of creativity can creativity be explained in terms of usual cognitive processes Problem Finding the most creative solutions have to do with ambiguous ill structured problems not clear how to get to a solution 0 Posing the right problem may be the key I Artists stilllife stuajz the artists spend a lot of time thinking about how to put those objects together in a way that will be interesting 0 Much creative problem solving appears to be incremental Evidence Boden distinguishes between historical and personal creativity 0 Historical rst proposal of a given solution to difficult problem ex Newton s description of solar system as central force system 0 Personal generation of a novel idea for an individual important re ects operation of processes that lead to new ideas Problem representation matters Creative Cognition Project Structured imagination discussed in text 0 Inventing new products GENEPLORE Generation Exploration divergent by inventing a new product people will generate some prototype in their mind and they explore the viability Divergent thinking being able to consider a solution in many different ways rather than converging on a single answer Typical experiment Set of 15 component parts subset of 3 Invent in a domain furniture vehicles tools weapons Vary different aspects of paradigm o 3 parts given or not have to choose 0 Domain given before invent or after have already been parts together Results more creative if Component parts given restricted Domain to invent given restricted Domain given after form generated Form generated by self not given 23 Important to have constraints but may be better to generate and then t into particular invention not how usually done Summary of Creativity Dif cult to study and think about Recent research seems promising 0 Try to t into cognitive framework 0 Useful even if turns out to be wrong Many noncognitive approaches as well 24 25 41715 1026 AM 26 41715 1026 AM
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