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by: Marco Wolf


Marco Wolf
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This 61 page Class Notes was uploaded by Marco Wolf on Monday September 7, 2015. The Class Notes belongs to PSY 355 at University of Texas at Austin taught by Staff in Fall. Since its upload, it has received 5 views. For similar materials see /class/181804/psy-355-university-of-texas-at-austin in Psychlogy at University of Texas at Austin.


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Date Created: 09/07/15
The syntax of language How do we form sentences Processing symtax Language and the brain Productivity again We can combine words into new sentences 7 The brown dog ate some smelly food Ifwe know a word we can use it in a variety of different sentences 7 The smelly dog ate some brown food 7 How are We able to form these new sentences A limited set of basic units words 7 A set ofrules for combining Words grammar SyntaX The grammatical structure of a language Languages have a structure that determines how words are put together to form sentences Types of words 7 Nouns Refer to objects concepts and locations 7 Verbs Refer to actions or states Verbs structure a sentence 7 Modifiers Used to add information to nouns and verbs 7 Structuml Words Prepositions articles These are closed class words Forming sentences Words are combined to form sentences English is a word order language 7 Syntax is based mostly on Word order 7 Most languages are case languages Pre xes andor suf xes are added to want to indicate the role they play in a sentence Grammatical rules Most grammars involve rewrite rules 7 Here are some examples S NP VP NP 7 An Adj N A big dog VP V NP bought a dress VP V sang V V NP PP brought his friend to the pa y Sentence Production follows the grammatic 7 Person must rst have a thought 7 The thought is translated into language When speaking create a sentence that al rules Sometimes different thoughts can lead to the same sentences Mary had a little lamb That evening with her bllnd pulled Mary had mm helplngs 0 corn Mo baked potatoes extra bread and a quotme lamb Another example 0 Sarah saw a man eating shark SarahNP saw a manNP who was eating sharkS VP S 0 Sarah NP saw a man eating shark NP VP S 0 The same sentence can be parsed into two different structures The structure in uences the meaning 0 The productivity of language leads to ambiguity 0 We rarely notice this ambiguity except in jokes HOW 10 W6 pI39OCGSS SGHtGHCGS 0 We do not have a whole sentence in front of us to process We hear one word at a time 0 We hold a few words in working memory We must parse a sentence as it comes in As always we use constraints Our parser makes guesses about sentences The cat sat on the mat Syntactic illusions Constraints can sometimes cause problems Garden Path Sentences The horse raced past the barn fell 0 People do not typically produce sentences like this Language and the brain 0 Many observations of language disruptions following brain damage 0 Language appears to be localized in the left hemisphere Some lefthanded people are lateralized differently Types of language disruptions Aphasia Disruptions of language processing Agnosia Disruption of naming Some important brain areas for language Wernicke s area Msl PRIMARY Sml PREMDYCR MOTOR 3 1 v a Broca S FRONTAL EYE Fl ELD VlSUAL ASSDClATlON 1 7 PRIMARY VISUAL SURFACE FRIMARV AUDITORY Aphasia There are many types of aphasia 7 Each is characterized by particular de cits Two common types of aphasia 7 Broca s aphasia May involve too little activation 39 Grammatical problems in semantic network English comprehension focuses on word order 7 Wernicke s aphasia I Fluent grammatical processing Disruption 39 Speech is word salad inhibition in sem anti c network Agnosia People have trouble naming objects Can be speci c to particular categories 7 Vegetables artifacts Prosopagnosia 7 Speci c to faces Not a perceptual de cit 7 People can describe the objects 7 Can draw them accurately 7 The connection between the perceptual representation and the label is damaged Language and intelligence Williams s3mdrome 7 Preserved language abilities 7 Severely impaired cognitive abilities Suggests that the ability to learn words and form sentences is not just a component of general intelligence Language learning 7 We learn language differently from many other things 7 Language is learned incidentally 7 Language learning is Worse in adults than in children For most cognitive abilities the opposite is true Summary Syntax allows languages to be productive Can characterize symtax as a set of 39 rules Parsing nding the syntactic form of a sentence 7 Must be done online Language and the brain Decision making 0 Making decisions 0 Optimal decisions 0 Violations of rationality What is a decision 0 Person must have a goal 0 There must be many ways to satisfy the goal 0 There is a set of options Consideration set Set of options being evaluated 0 Options are evaluated in some way Eventually one of the options is selected 5 win LI What is a good decision Economists have worried about good decisions Rational decision making What is the optimal choice Decisions should be consistent Law of contradiction Reasoning processes that use the same information should reach the same conclusions 0 Those that do not are irrational Example Transitivity lfyou preferA to B and B to C Then you should prefer A to C A brief foray into economics Much research in the 1970s and 1980s was devoted to comparing human performance to the expectations of economic models Economic models of choice 7 Expected value theory 7 Expected utility theory Expected value theory 7 People calculate the potential value of each option 7 Pick the option with the highest expected value Raf e with 10 chance to win 35 00 EV 103500050 Expected value cont Simple example Which gamble would you rather play A 20 chance of Winning 5 00 B 30 chance of winning 4 50 EVA7 20 5 00 7 1 00 EVB 7 30 4 50 7 1 35 Expected value suggests you should choose B This seems reasonable Problem With expected value Not every dollar has the same subjective value 7 Gmduate student 3 100 would allow student to eat better food or to buy new clothes 7 Lawyer 100 would not need to be spent on necessi ies Example Lotteries 7 People o en play the lottery 7 Pay 100 for a 152000000 chance to win 10000000 Expected value of this gamble is less than 1 00 Expected utility 0 What can an option be used for 7 That is the expected utility of an option 0 Consider the lottery 7 The expected utility of 100 may be low I There is not much you can do With 1 00 7 The expected utility of the prize may be high IYou could do a lot With that kind ofmoney IThe loW probability of Winning does not completely outweigh the high utility of the e IThere is also the pleasure in dreaming about Winning Formal setup for EU model 0 The Expected Utility model EU E Weight utility 0 Expected Utility is a rational model 7 Obeys the law of contradiction All choices are transitive 7 Everything is evaluated relative to a global scale Problems with Expected Utility 0 The Allais Paradox A A 100 chance to Win 1000 B An 89 chance to Win 1000 A 10 chance to Win 5000 A 1 chance to Win 0 C An 11 chance to Win 1000 An 89 chance to Win 0 D A 10 chance to Win 5000 A 90 chance to Win 0 The rst second set of options is derived from the rst by removing an 89 chance to Win 1000 Certainty Bias The Allais paradox is an example of a certainty bias People often prefer the certain 1000 Also true in nonmonetary situations Imagine that the Us is preparing for the outlxeak of an unusual Asian disease which is expected to kill 500 pqule Two a1temative programs to combat the disease have been proposed Assume that the exact scienti c estimates of the consequences ofthe program are as 5 People tend to Program A 200 people will be saved pick Program A Program B A 13 chance 500 people will be saved and a 23 chance that no people will be saved Galns and losses The previous example suggests people are risk averse for gains 7 They do not Want to risk losing a possible gain 7 What happens for losses Imagine that the Us is preparing for the outlxeak of an unusual Asian disease which is expected to kill 500 pqule Two a1temative programs to combat the disease have been proposed Assume that the exact scienti c estimates of the consequences of the program are as 11 People tend to Program A 400 people will die pick Program B Program B A 13 chance no people will die and a 23 chance that 500 people will die forlosses Framing effects Kahneman and Tversky People treat gains and losses differently 7 Losses loom larger than gains The same situation feels worse when framed in terms oflosses than when framed in terms of gains 7 May not be true in all cultures Practical application 7 When making a decision try to fmme the options both in terms oflosses and gains 7 See Whether your opinions about the options ch es Context effects 0 Expected utility predicts that each option is evaluated individually 7 Adding more members to the consideration set should not influence people s preferences 0 The attraction effect 0 Brand A Imagine you are initially indifferent between Brand A and Brand B Quality You like them equally Brand B well What happens if a new Price brand is added The Attraction Effect 0 Brand A Brand C is asymmetrically dominated It has a higher price and lower quality than B Brand B It has a lower price and lower quality than A The number of people who choose B increases Quality Brand C Price Somehow C attracw choices to B Was the introduction of New Coke in the 1980s a case ofareal life attraction effect Preference Reversals 0 Different measures of preference may sometimes lead to different outcomes A 1112 chance to win 12 chips 112 chance to lose 24 chips B 212 chance to win 79 chips 1012 chance to lose 5 chips 0 Slovic amp Lichtenstein 0 Some people asked to choose a bet 7 Tended to choose A 0 Some people asked how much they would pay 7 Gave a higher price for B Logical Reasoning What is reasoning What is logic 0 Can people reason logically What is reasoning The world does not give us complete information Might want to assume that there is a party Would not want to assume that this is lunch Reasoning is the set of processes that enables us to go beyond the information given What types of reasoning are there Deductive reasoning 7 Allows us to draw conclusions that must hold given a set of facts premises Inductive reasoning 7 Allows us to expand on conclusions 7 Conclusions need not be true given premises 7 Categorybased induction 7 Analogical reasoning 7 Mental models Deduction An example You have tickets to a game You agree to meet Bill and Mary at the comer of 21st and Speedway or at the seats 7 Ifyou see Mary on the corner of21st and Speedway you expect to see Bill as Well 7 If you do not see either of them at the corner you expect to see them at the seats When you get to the stadium This seems simple 7 How do you generate this expectation The logic of the situation The agreement has a logical form Bill AND Mary will be located at comer OR Bill AND Mary will be located at seats AND and OR are logical operators 7 They have truth tables AN DAE 0RAE AlsFALSE AlsTRUE Al FALSE AlsTRUE E ls FALSE FALSE FALSE E l FALSE FALSE TRU E ls TRUE FALSE TRUE E ls TRUE TRUE TRUE Simple logical arguments Ifyou see Mary Bill AND Mary 4 Premrses Mary Bill 4 Conclusion You expect to see Bill ANDAE Al FALSE AlsTRUE 1 E l FALSE FALSE FALSE E lsTRUE FALSE TRUE Another logical argument 0 If Mary and Bill are not on the corner Bill AND Mary will be located at corner OR Bill AND Mary will be located at seats NOT Bill AND Mary located at corner Bill AND Mary located at seats 0 You expect to see them at the seats 0RAB Ais FALSE AisTRUE l Bis FALSE FALSE TRUE BisTRUE TRUE TRUE Limits of logical reasoning 0 We are good at this kind of reasoning 7 We do it all the time 7 We can do it in novel situations 0 Are we good at all kinds of logical reasoning What are our limitations An example Each card has a letter on one side and a number on the other Which Cards must you turn oVer to test the rule If there is a Vowel on one side of the card then there is an odd number on the other side HIE What about this case Who do you have to check If you have abeer then you must be 21 or older IE These cases are logically the same Valid Arguments If premises are true conclusion must be true Affirming the Antecedent Denying the Consequent PgtQ PgtQ P m Q Modus Ponens NOT P Modus Tollens Invalid Arguments Conclusion neednot be true even ifpremises are true Affirming the Consequent Denying the Antecedent PgtQ PgtQ J m P NOT Q Logic and content 0 Pure logic says that we should be able to reason about any content 7 The Ps and Q5 in the argument could be anything 0 Earlier we saw content effects 7 Wason selection task With neutral content it is hard With familiar content it is easy 0 Social schemas are easy to reason about 7 Cheng amp Holyoak Tooby amp Cosmides 7 Permission Some precondition must be lled in order to carry out some action Other content effects We are more likely to accept an argument when the conclusionis true in the real world All professors are educators Some educators are smart Some professors are smart This conclusion may be true 7 The argument is not valid 7 It is possible that the smart educators are not profes sors So Where does this leave us We are good with simple logical operators 7 AND OR NOT More complex argument forms can be dif cult in unfamiliar contexts Why do we see these content effects 7 Valid deductive arguments ensure that a conclusion is true ifthe premises are true 7 Truth cannot be determined With certainty 7 Thus We must generally reason about content We will look at how people reason about content Decision making behavior Why do people make the choices they do Reasonbased choice Regret theory Effortaccuracy Choice and judgment heuristics Where are we so far Economics predicts rational choices 7 Obey the law of contmdictiort People s choices are not always optimal That does not mean choices are bad 7 Psychologists have set up particular circumstances in which people make poor choices 7 Helps to illustmte processes people use We will examine models of choice behavior 7 Many different processes are used to make choices Prospect Theory A model that is like economic models P 7 2 r1 u P is subjective probability u is the utility of each option Utility is evaluated relative to a reference point U Subiective Frabablllty Actual Prubablllty Losses loom larger than galrls Prospect Theory Big difference between certainty and uncertainty Utility inction explains framing effects euhiemve Probability Actual Probability Losses iuum larger than gains Reasonbased choice People want to be able to justify their choices 7 May make decisions that are easiest to justify 7 Sha I Simonson amp Tversky Imagine you just finished a particularly dif cult nal exam and you ate Acapulco The Offer expires that aftemoon but you can pay 5 00 to the 5 00 fee A majority ofpeople given this scenario pay the 5 00 Two other groups are run One goup to1oi they passed the exam Most choose to go One goup to1oi they failed the exam Most choose to go information that 39 not affect their choice Reasonbased choice cont People want a reason to go on the trip 7 If they get a passing gmde Celebration 7 If they get a failing gmde Consolation Other reasonbased effects 7 The attmction effect discussed last class 7 Effect is stronger ifpeople have to justify their choice Justi cation is not always good 7 People tend to use less information and to rely on single dimensions when forced to justify a choice 7 It is easier to come up With these simpler justi cations Regret Theory People may make choices to avoidregret Statis quo bias 7 People Would prefer not to make a change 7 If a change is made and it goes badly there is regret Imagine you come to class and are given a ticket for a drawing to win a prize You are given the option to trade your ticket with your neighbor before the drawing takes place will you trade This is an even trade but most people elect to keep their ticket They would experience regret if the ticket they traded away eventually won the drawing May also explain certainty effects Regret if a 124 chance occurs Effort Accuracy framework People attempt to make accurate choices 7 People Want to minimize effort Some methods for making choices are higllly accurate 7 They involve considering a lot of information 7 Calculating expected utility is a high efforthigh accuracy Way of making a choice Some methods are simpler 7 They involve considering less information 7 We Will now look at some simpler decision heuristics Satis cing Choose the rst option that is satisfactory 7 Will find an option that satis es the goal 7 Does not guamntee nding the best option Imagine you are a manager at a supermarket 7 You need someone to bag groceries 7 You get 100 applications 7 The cost ofhjring a suboptimal person is loW 7 Take the rst person Who looks like they can do the job Elimination By Aspects Start with the most important attribute 7 Eliminate all options that are not satisfactory with respect to that attribute Then go to the next most important attribute 7 Repeat this process until there is one option le Lexicographic Semiorder Like Elimination By Aspects Look at the most important attribute 7 Select the option that has the best value on that attribute Mental accountlng Utility theory is a common currency theory All options are evaluated With respect to utility But all gains and losses are not viewed as the same 7 People seem to have avariety of mental accounts in 1 and n ma themboth at the same department store The calculator costs 25 and thejacket costs 120 You are told that a store across town has both items but the calculatoris s15 cheaper atthat store Do you buy the items atthat store or do you go across town Mostpeople say yes Ifthe Jacket 15 15 cheaper mostpeople say he Tnaha Mental Accounting The idea is that people are creating separate mental accounts for different goals 7 Money for necessities 7 Money for entertainment 7 Spending money from one account does not affect others Imagine you have gone to the movies to see a show You got to the from L r 39 intwin doyoustill 39 Most people say yes Imagine you have gone to the movies to see a show The ticket costs 10 Yo b the ticket early in the day When you get to the theater you realize you lost the ticket Do you buy another one no The House Money effects Another example of Mental Accountng You go to a casino and put a quarter in a slot machine You win 100 How is your gambling behavior affected People are often more willing to gamble in this situation Not any windfall increase in money works n L 39 39 r You own 100 shares ofa stock and nd out that it went up 31 00 that day How is your gambling behavior affected Most people s gambling behavior is unaffected by this news 4 npnnlp 99 39 39 39 money In the second case it feels like their own money Summary Prospect theory Reasonbased choice Regret theory Heuristics and biases Mental accounting Problem solving 0 What is problem solving 0 Weak and strong methods 0 Weak methods of problem solving There are problems all around us 0 Much of our life is spent solving problems Getting into the cookie jar without your mother noticing Doing a crossword puzzle What makes these problems Four aspects to a problem Goal 0 What is to be accomplished Givens 0 What is known from the start of the problem Means of transformation 0 How can the initial state be modi ed Obstacles 0 Something that stands between the initial state and the goal What would happen if one of these aspects were missing Types of problems Welldefined problems All four aspects of the problem are specified Towers of Hanoi Mazes Illdefined problems One or more of the aspects of the problem are not well specified Stopping a war Getting cookies without your mother knowing Move one coin so that there are two straight lines of six coins which cross each other at the center point of each line C 0 D C What kind of problem is this Illdefined Wellde ned How do we solve problems Sometimes a problem is novel Then we use general problem solving strategies These are called weak methods Sometimes a problem is more familiar Then we can use our background knowledge These are called strong methods We will focus on weak methods today Strong methods will be discussed next class SO THE PROBLEM I BREAK DOWN Problem solving as search 0 Consider a wellde ned problem The givens are known The means of transformation are known The goal is known 0 The obstacle is generally that there are so many possible solutions it is hard to nd the right one We must search for the right solution The problem space 0 3 disc Towers of Hanoi problem Initial State Goal State Search for a path from I the initial state to the LIL end state For this problem the L ALL A whole space can be r39 x enumerated lei Lg I11 LL ALL LU e LL What if the search space is too large It is not possible to enumerate the entire search space for all wellde ned problems 7 Chess A er a few moves there are too many possible moves and counter moves to consider all of them We must use constraints 7 O en called heuristics 7 A heuristic is a general guideline It is likely to leadto a good solution Not guaranteed to work Hill climbing Find some measure of the distance between your present state and the end state 7 Take a step in the direction that most reduces that distance J A potential local minimum problem An example Stacking blocks Create a stack with A on top the B then 3 I3 nmetime m Meansend analysis Try to reduce the largest difference between the initial state and the goal state first How should you get from UT to the Empire State Building 7 Fly from Austin to New York That takes care of the biggest difference 7 That creates new subproblems Getting from UT to the airport Getting from a New York airport to the Empire State Building 7 Each of these new subproblems needs to be solved Working backward Sometimes it is hard to solve a problem by starting at the initial state 7 Many puzzles are intentionally designed to be hard to solve from the givens It can be useful to start at the end state and work backward Problem Representation What is the largest number of knights you can put on a chessboard where no two knights can attack each other Between category structure 0 Hierarchical structure 0 The basic level 0 Goalderived categories Multiple categorization 0 What is this 7 Poodle dog animal thing pet as a dog Hierarchical categories 0 Categories are organized hierarchically 7 Different levels of abstraction 0 Why do we have different levels 0 How do we know which one to use Chances are you rst identi ed this Why different levels Communication 7 Different levels communicate different things 7 Allows us to refer to groups of different sizes Inference 7 Hierarchies allow inheritance web 70 f 157 Cells Does a dog have a tail Dog Is a dog made of cells wjmrt Tail is7 Does a poodle have a tail Poodle How do we use these levels Objects are named at a middle level of abstraction The middle level 7 Often called the basic level Animal Dog The superordinate level Cat The basic level Poodle Siamese I The subordinate level What makes the basic level basic 7 Studied by Rosch and colleagues Why is the basic level basic Many factors converge at the basic level The most abstract level at which 7 Objects from a category tend to have the same shape 7 The same motor movements are used With the objects 7 Objects Within the category tend to share parts 7 Contmsting categories can be easily compared A high level of within category similarity 7 Members of a category tend to be similar A low level of between category similarity 7 Members of contmsting categories tend to be dissimilar Changes With expertise Noviceexpert differences American students differ from Itzaj Mayans 7 Bird is the basic level for American students 7 Level is more speci c for Itzaj Mayans 7 This difference is only seen for naming 7 American students and Ma ans similar When tested using other functions of categories We ll talk more about that next class Similar results have been observed within this culture for experts and novices Is this only true for objects There may be a basic level for events too 7 Basic level events contain common subevents 7 Superordinate events may have many possible subevents M aklng a purchase Fund Shupplng Shupplng at a Shupplng at a supermarket vegetable stand Goalderived categories Not all categories name groups of objects 0 Some categories are organized around goals Pets Goalderived categories 0 Are goalderived categories at all like taxonomic categories They have a graded typicality structure There are good and bad examples What is a good example of a pet What is a bad example of a pet Typicality is assessed relative to ideals What is the ideal pet How is this related to good and bad examples of pets Ad hoc categories Goalderived categories may also be formed on line Things to take on a picnic Things to take out of the house in the event of a re Categories are easy for people to form These novel categories also have a graded typicality structure Like other goalderived categories typicality is assessed relative to an ideal Reasoning and Mental Models 0 Mental models 0 Naive physics Scienti c reasoning Reasoning about devices SORT 0 UKE THE SUV WHO LNES U N OUR GANGE EXACTH HELL LET39S SAY YOU AND REFUND WE MKCHlNE WANT 25 DOLLARS YOU THERE S A GUY mm A PUNCH IN THE AMOUNT PRlNTlNG PRESS WHO MAKE THE 1 39 Mental Models Mental models allow us to reason about devices Kind of like scripts and schemas discussed earlier People often have causal information about the way things work Used to allow us to get through the world Information may be flawed Three types of mental models Logical mental models Analogical mental models Causal models Logical mental models Used to solve logic problems 7 JohnsonLaird 7 Contain empty symbols that are manipulated All Archers are Bankers A B A B No Bankers are Chemists A B A B 7 A B A B B B B B All Archers are Bankers N0 Bankers are Chemists c No Archers are Chemists Useful primarily for logic puzzles I Analogical mental models Sometimes we understand one device by analogy to another 7 Electricity and water ow Voltage lt gt Water pressure Current lt gt Flow rate Resistance lt gt Width of pipe What happens to the current in a circuit when the resistance is increased Might visualize a pipe being constricted It would be harder for water to get through the narrower pipe and so the current would decrease Causal Models Causal models 7 Vosniadou amp Brewer 7 Contain causal V AR information 7 Go beyond what we learn 7 May have aws Na39139vc physics What would happen to a ball shot through this pipe People o en respond by assuming curvilinear momentum 7 McCloskey and Prof tt Even happens if they carry out an action What would happen to a bomb dropped from this plane Why do we err Our naive physics matches our observations 7 The World has friction and so there are unseen forces that act in opposition to seen forces 7 Our naive physics is o en accurate for things We can do With our bodies Only when we create larger machines do the differences become important Should not be a surprise 7 Newtonian physics is only a few hundred years old 7 Aristotelian mechanics is closer to our daily experience How deep are our models Shallowness of explanation 7 Keil and colleagues People believe they understand more than they do 7 Asked college students about devices Toilet Car ignition Bicycle derailleur 7 Said they under stood devices 7 Could not actually explain them 3 Why does this happen Knowledge is packed 7 When we know how to use an object and it is familiar we believe we know how it works Scienti c reasoning Scientific reasoning 7 Combination of abilities Hypothesis testing 7 Generate an explanation for some phenomenon 7 Develop an experiment to test the hypothesis 7 Seek disconfirrning evidence How good are people at this type of reasoning 7 How good are scientists at living up to this ideal Hypothesis testing People tend to have a confirmation bias 7 We seek con rming evidence Wason 246 task 7 You are told to nd a rule that generates correct three number sequences 7 You are told that 2 46 is a correct sequence 7 You search for the rule by testing as many sequences as you want until you are con dent you know the rule Con rmation bias Many people initially assume the rule is Sequences increasing by 2 7 They try sequences like 468 and l3 lSlT 7 Few people try sequences that would disconfirm their hypothesis eg 123 or 321 7 The actual rule is Any increasing sequence Few people find the conect mle Scientists also show a con rmation bias 7 They tend to be more critical of evidence that is inconsistent with their beliefs This may not be a bad thing Koehler People make good tests Despite con rmation bias adults do seem to understand what would make a good study 7 Will genemte tests that vary only one thing at a time 7 Will look for differences based on the factor manipulated Probably has something to do with education Children must learn this skill 7 Still show a con rmation bias Lfresults of study are inconsistent with prior beliefs people may stick with their prior belie Summary Mental models 7 Logical mental models 7 Analogical mental models 7 Causal mental models Naive physics 7 Physical beliefs sometimes diverge from truth 7 Suf cient to get us around the world Scienti c reasoning 7 People generate pretty good tests 7 O en show a con rmation bias Language and communication What is language How do we communicate Pragmatic principles Common ground What is language Language is the most complex form of communication used by any anim Allows the transmission of culture 7 Permits us to teach others 7 The ratchet effect Tomasello Each generation more sophisticated than the last The components of language Speech sounds Written symbols Words Syntax 7 Permits language to be productive Communication strategies All have been a topic of study Communication Why start with communication 7 Understanding Why We would study the rest of the components of language does not make sense Without understanding communication Prototypical type of conversation 7 Two people 7 Facetoface 7 Speaking Many communication situations differ though 7 Phone calls lectures books 7 Communication can go astray in these situations What is a conversation like A Well let s see we have on the bags Who s on first What s on second I Don t Know is on third c That s what I want to nd out A I say Who s on rstWhat s on second IDon t Know is on third c Are you the manager Yes c Are you going to be the coach too A Yes c And you don t know the fellows names A WellIshould c Well then who s on first A Who This appears to follow rules Sacks Schegloff amp Jefferson People take turns speakng When one person speaks everyone waits for the other person to nish The speaker may suggest the next speaker If there is a break someone may jump in 7 The rst person to speak gets to continue 71ftWo people start at the same time one will stop This misses alot 0 These rules are a set of actions a person takes 0 Communication is more of a joint action 7 Dancing and shaking hands are joint actions 0 Both the speaker and listener are active participants 7 Rapid corrections are made 7 Lots of backchannel feedback 7 Conventions are established Speakers and overhearers Schober and Clark 7 Examining communication as a joint action 0 Pair of people play a communication game 7 Must get a set of figures in the same order 0 Tape of interaction then played to another person 7 Must also get the figures into the proper order 7 Overhearer less accurate than participant 0 Why 7 Participant can get immediate corrections 7 Use of backchannel feedback eg uhhuh The danger of overheanng a conversation g hlS emplayeu39 creams 01 Shamans has lost his nurbles r Wagner bums from his of ce for the last lime Principles of communication We often do not speak in complete sentences We rarely say what we mean literally 7 Previous cartoon is an example How do people interpret what is said 7 A set of social conventions 7 Determines how utterances are structured Givennew convention mammmrasml FEEL YEAH LltE TAKING a 1 FELT VANNA WHITE AGAIN 7 LIKE IT To LUNCH YESTERDAY AGAIN Too What is the given information here Conversational Maxims Grice Quantity Be informative Quality Tell the truth Relation Be relevant Manner Be clear A normal utterance satisfies these maxims Violations of the maxims Quantity 7 Do you have a Watch 7 Interpreted as a request for the time 7 Do you accept credit cards 7 Interpreted as arequest for the types of cards taken Quality 7 I m so hungry I could eat a horse 7 Interpreted as an exaggemtion of hunger More Violations Relevance 7 A Do you have aWatch 7B Yes 7 Interpreted as a Weak attempt at humor or perhaps annoyance Manner 7 Use ofjargon in social settings 7 Interpreted as an attempt to exclude Indirect speech acts We use these violations to communicate Why don t wejust say what we mean 7 Direct speech may sometimes be rude Ironic and sarcastic statements 7 John that Was a really intelligent answer 7 Less rude than John that Was stupid Summary Language is used to communicate Communication is a joint action We often communicate nonliterally Next classes 7 What are the basic components of language 7 What enables us to talk about so many topics Categories 0 What are categories 0 The internal structure of categories 0 Rulebased approaches Similaritybased approaches 0 Theorybased approaches What are categories Categorization is a huge topic How are people able to apply prior knowledge By recognizing a new situation as an instance of a previous situation Categorization is the process that allows this to r uni H l Categories have many functions l39J39 Classification Prediction Reasoning Communication Start with classi cation The most highly studied function 0 How to people learn to classify new items 0 Three approaches Rulebased approaches Similaritybased approaches Theorybased approaches Rulebased approaches Classical View of categories 7 A set of necessary and suf cient features Necessary feature to be in a category 7 All instances ofthat category must have it Four sided Suf cient feature set 7 All instances ofthe category have the set of features 7 No instance not in the category has the set of features Four sided closed gure l I Problems With rules It is hard to nd a set ofnecessary and suf cient features for most categories Bachelor 7 Unmarried adult male 7 But What about a Catholic Priest or a widower Maybe the de nition is no good 7 Almost any definition Would have exceptions Rule Exception models Nosofsky and colleagues 7 Find a simple rule that classi es most items 7 Store the exceptions separately Model accounts for laboratory studies 7 Hard to see What else you could do With rules and exceptions 7 How Would you make predictions reason or communicate Similaritybased models A new exemplar is classified based on its similarity to a stored category representation Similarity Degree of feature overlap between items 0 Types of stored category representations Prototype Exemplar Prototype model 0 Prototype Average category member Typicality gradient This model suggests that there are good and bad category members Can be seen with typicality ratings Robin Eagle Emu Bird Prototype Typicality decreases With distance from prototype Classi cation of Prototype If there is a prototype representation Prototype should be easy to classify Even if the prototype is never seen during learning Posner amp Keele o O 0 o i C O C O O o 39 o o o o o O o 0 g o 0 Prototype Small Medium Large Distortion Distortion Distortion Exemplar model Exemplar A category member Perhaps a category representation consists of storage of a number of category members New exemplars are compared to known exemplars Exemplars and prototypes It is hard to distinguish between exemplar models and prototype models Both can predict many of the same patterns of data Graded typicality How many exemplars is new item similar to Prototype classi cation effects Prototype is similar to most category members Current research focuses on other issues Theorybased models Sometimes similarity does not help to classify Daredevil Theories and development The use of theories increases with development Keil Kids told about a cat given an operation Painted black with a white stripe A bag of smelly stuff put in its stomach It can shoot the smelly stuff Young kids call this animal a skunk Older kids call it a cat Re ects a developing theory of biology Psychological Essentialism People act as if categories are governed by rules We believe that there is something that makes an object what it is Even if we do not know what that thing is We use this as a basis for predictions For living kinds DNA For nonliving natural kinds Atomic structure For artifacts Function or intended function Summary 0 Classical models Similaritybased models Theorybased models 0 Human concepts use a combination of these Memory systems cont Interactions among memory systems Episodic and semantic memory Phenomena in implicit memory How many memory systems are there Separate systems We have talked about the systems as separate Are the systems obviously separate There are some interactions among systems 7 Suggests that the distinctions may not be as clearcut as they seem at first door glass pane shade ledge house An example open shutter curtain fram e Vi ew breeze sash screen Recall as many Words from the list as you can What did you recall How many ofyou recalled 7 D 7 312557 7 Shu er 7 7 Wlnduv Here s the list again open shutter curtain frame ledge breeze Sm sash house screen Here s the 11st agaln door open shutter glass curtain pane frame shade View ledge breeze Wherelswlndow Sm sash Many people recall it house screen Semantic and episodic memory The list consists of frequent associates of the target wor 7 Presumably this leads to activation ofthe target concept 7 A property of semantic memory The word is identi ed as being present on list 7 An episodic memory Semantic memory is affecting episodic memory Are these False Memories The recovered memory debate Many reports of recovered memories 7 Tmumatic events recalled a er many years Where they could not be recalled 7 Some people Were convicted of crimes on the basis of recovered memories 7 Argument is that memories Were repressed 7 Are the memories real How could we tell 7 It is generally impossible to verify old memories What sort of evidence would help resolve this Implanting memories Researchers have asked whether memories can be imp ante 7 Ifa false memory can be implanted that Would make us more skeptical about reports of recovered memories Does not prove that recovered memories are false 7 Would provide a counterexplanation to observed recovered memories Roediger and McDerrnott Deese paradigm 7 These memories are not traumatic though More traumatic false memories Loftus and colleagues 7 Convinced people they Were lost in amall When they Were 5 years old Stories were based on details provided by family Event never really occurred 7 Many participants have strong memories of the event Implications for recovered memories 7 Con dence in a memory does not mean that the memory ls re So What about recovered memories There are some cases where remembered traumatic events were corroborated 7 Not clear that the events Were forgotten in the It is easy to implant false memories 7 Some therapists have been sued succ essfully Reports of recovered memories should be treated with caution 7 Should not be used as a basis for conviction Without some other corroborating evidence Semantic and implicit memory ls implicit memory a separate system Seems to be de ned by tasks 7 Stem completion amp Fragment completion 7 Priming in perceptual identification Contrasts with semantic and episodic memory 7 These types of memory are de ned by kind of ati n information and organiz Transfer Appropriate Processing Perhaps implicit memory is not a separate system Encoding speci city 7 As similarity of study to test increases retrieval improves 7 Implicit tasks may be more like study than explicit tasks 7 Explicit tasks may be conceptual 7 Implicit tasks may be perceptual 7 Encoding may be perceptual in many cases Attention What is attention Low level attention Serial and parallel search What is attention How is the word used Examples 7 Something uttering caught my attention 7 I didn t see you Iwas paying attention to the game 7 I struggled to pay attention to the lecture 7 I don t remember even cleaning the table I must not have been paying attention Talking about attention The way we talk about attention affects how we think about it 7 We use the same Word for all ofthese examples Are they really the same 7 Perceptual attention Seeing objects Hearing one conversation but not another 7 High level attention Switching between tasks Attention as a resource 7 We talk about paying attention Attention is probably not one thing All of these de nitions refer to limiting the amount of information that is processed There are probably many ways to do this We will distinguish two types of attention 7 Perceptual attention 7 Highlevel attention May be many subsystems even within these How do we limit information processing Not everything that we perceive initially gets additional processing 7 Change blindness phenomena Span of apprehension 7 People are o en unable to report much from a brie y presented stimulus 7 Are these limits immediate Partial report technique Sperling 7High tone Top Row edmm tone Middle Row ow tone Bottom Row Either 6 or 12 letters presented In Jll report report all letters people are limited to about 45 letters In partial report they get 34 letters from each row 7 That information had to be in there somewhere More information available early than late Search requires effort When is our capacity to process information limited 7 We can t do everything at once Searching for some perceptual items may require effort 7 How do we know what requires effort 7 How could we test this Consider Studying effort How could we tell how much effort was required for a search What if a search required no effort 7 How long would it take to search through a set of 4 items 7 12 items 7 24 items An example Another example Parallel search Search time does not change with number of items in the display ms All items can be searched at once Nut present Present 7 popom effects Response nrne Limited capacity search Search time increases with number of items in the display Nut present RT when target is present increases Present more slowly than when target is absent 7 Why E E a Serial or parallel This last pattemlooks like a serial search 7 Checking one item at a time Serial search is consistent with these data Could be a parallel search with limited capacity 7 What does that mean 7 Think about a spotlight that is tightly focused O 7 Now one that is broadly focused 7 Which is brighter When are there no capacity limits Single dimension search with homogeneous distractors Treisman and colleagues 7 Suggests early visual information is processed by separate brain areas Color Form 7 Simple contrasts pop out Most searches require capacity Feature Integration Theory If information about different properties of objects are processed in different areas then how do we know what properties an object has Feature Integration Theory Treisman 7 Attention is required to bind together propem39es at locations in visual space 7 Objects and the bmin The what system The whm system Feature Integration Theory R I Suggests there could be illusory conjunctions 7 Snyder People see displays with colored letters May report a letter with the color of a neighboring letter Summary Attention refers to processes that constrain the amount of information processe Early perceptual processes admit a lot of information 7 Information may be processed in pamllel early More complex processes introduce capacity limits 7 Binding features together into objects


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