Download
1 / 50
500 likes | 553 Views
Chapter 8:. Language and Thought. [Packet] Language Development. Let’s preview the questions you will have to answer after this activity! (see next slide) Listen to segments from each song. Write down any words, word parts, or word meanings that you can derive from the lyrics. Enya
E N D
Chapter 8: Language and Thought
[Packet]Language Development • Let’s preview the questions you will have to answer after this activity! (see next slide) • Listen to segments from each song. Write down any words, word parts, or word meanings that you can derive from the lyrics. • Enya • “Dragonstea” • From Los Angeles Master Chorale • “Vivo per Lei” • “Passe de Magica” • “Un CuentoSobre el Agua” • “I’m Into Something Good”
Language Development (ctd.) • Describe the process of trying to understand a sound or word you don’t know. What happens in your mind first? What happens next? Etc.? • Why were you able to pick out some words or parts of words? Once you were able to identify these words/word parts, what did your brain do with them? • How much of what you were able to pick out had to do with previous experience? Why? • Why do you think babies start out babbling sounds? Why don’t they just listen and wait to speak until they know entire words?
[Packet]Early Language Phases • Key Question: How do people mentally structure and develop language? • Use pgs. 304-310 Answer the key question using ALL of these key concepts. • Properties of Language (4) • Structure of Language (4) • Babbling • First Word • Phrases • Sentence
Background: The Cognitive Revolution • 19th Century focus on the mind • Introspection • Behaviorist focus on overt responses • arguments regarding incomplete picture of human functioning • Empirical study of cognition – 1956 conference • Simon and Newell – problem solving • Chomsky – new model of language • Miller – memory (7 + or – 2) • Cognition Today • interdisciplinary field • language, problem solving, decision-making, and reasoning
Language: Turning Thoughts into Words • What is language? • Language is defined as consisting of symbols that convey meaning, plus rules for combining those symbols, that can be used to generate an infinite variety of messages. • Properties of Language • Symbolic: people use spoken sounds and written words to represent objects, actions, events, and ideas • Semantic: language is meaningful • Generative: a limited number of symbols can be combined in an infinite number of ways to generate novel messages. • Structured: there are rules that govern arrangement of words into phrases and sentences.
The Hierarchical Structure of Language • Phonemes = smallest speech/sound units • 100 possible, English – about 40 • Morphemes = smallest unit of meaning • 50,000 in English, root words, prefixes, suffixes • Semantics = meaning of words and word combinations • Objects and actions to which words refer • Syntax = a system of rules for arranging words into sentences • Different rules for different languages
Acquiring Language 4 Stages
4 Rules in Acquiring Language • Babbling • -starts at 6 months. Imitates surrounding language. At 6 months babies can discriminate between sounds in their native language. Ba & pa. (even though they can’t say them) • -At 9 months, babbling starts to incorporate vowels and consonants. • “Stoo” “Raaa” “Theep”
2. 1 year – first word • -similar cross-culturally, like words for parents and other very conspicuous concepts in their lives “mama” “no” “dog” • -receptive vs. expressive language • Parentese (motherese) is a way of speaking to young children in which adult speak in a slower and higher than normal voice, emphasizing and stretching out words into simple sentences.
Vocab. and phrases • 18-24 months – vocabulary spurt • -fast mapping: the process by which children map a word onto an underlying concept after only one exposure • -over and underextensions • Ex. “Elmo” for anything red • EX. “doll” for only the child’s favorite doll 3 • -Two-Word Combinations: stringing together strings of 2 words that express actions or relationships. • EX: “ Daddy shirt” = belongs to Daddy • “See Boy” = look at object • - Telegraphic speech : the child omits articles (the), prepositions (in, out), and parts of verbs. • EX: Adult: “I am going to the store.” • Child: “I go to store.” • ***Responsive parents who show more warmth and responsiveness during this stage, their children showed larger range of vocabulary (Tamis-LeMonda 2001) • ***By age 2, children have vocabulary of more than 50 words.
4. Sentences: • -End of third year – complex ideas, plural, past tense • Overregularization with grammar • EX. “He goed home” • -Around 4 years of age. Sentences range from 3 to 8 words, and indicate a growing knowledge of the rules of grammar. • -by the first grade the average child has a vocabulary of approx. 10,000 words, by the 5th grade, 40,000. Some 2-year-olds learn as many as 20 new words a week.
Bilingualism:Learning More Than One Language • GQM: Does learning two languages simultaneously cause problems? Why or why not? • Yes and No • Research findings on children: • Acquisition for Bilinguals: Smaller vocabularies in each language, but combined vocabularies average • Cognitive Abilities for Bilinguals: • Higher scores for middle-class bilingual subjects on cognitive flexibility, analytical reasoning, selective attention, and metalinguistic awareness • Slight disadvantage in terms of language processing speed • Age and Learning a Second Language: • 2nd languages more easily acquired early in life • Greater acculturation when young facilitates rapid acquisition of a second language. • Bilingual kids “catch up” when they hit late-elementary school age
Prinderella and the Cince • 1. Read “Prinderella and the Cince” aloud with someone. • 2. Summarize the story. • 3. Does your brain process the language in this story based on phonemes, morphemes, semantics, syntax, or some combination of these? In a paragraph explain exactly how and why. • 4. Create at least a line of text that would cause a reader to process language in a way similar to the way in which we processed “Prinderella and the Cince.”
Engage: Quote Reflection “The human mind suppresses uncertainty. We’re not only convinced that we know more about our politics, our businesses, and our spouses than we really do, but also that what we don’t know must be unimportant.” -Daniel Kahnman Reflect on this quote.
(Spiral) Decision Making and Judgment Strategies • Add to the following story by projecting how you would apply to it the decision making or judgment strategy that you receive: • The year is 2020. You are the Secretary of Homeland Security. You are given a list of 20 terrorists from the CIA who are an immediate threat to the United States. You know that attacks have already taken place in Los Angeles, Chicago, and Miami, and that you and your allies must take out the terrorists in the right order, ring leaders first, to minimize U.S. causalities. You have been talking with diplomats from countries that have experienced recent terrorist attacks: Brittan, Spain, Italy, France, Brazil, Mexico, and Canada. They offer you advice, but the bottom line is that much of the situation is still a guessing game. The fact remains, however, that you must act and act now! What will you do? • Trial and Error (pg. 317) • Algorithms (pg. 317) • Theory of Bounded Rationality (pg. 323) • Additive Strategy (pg. 323) • Elimination by Aspects (pg. 323) • Expected value and subjective utility (pg. 325) • The availability heuristic (pg. 325) • The representativeness heuristic (pg. 326)
Famous Cases Wherein Politicians May have Guessed Right or Wrong • Look up as many of these websites as you can during the allotted time. How do the decision making strategies and tendencies we just saw apply to these famous decisions? • 1. U.S. History: Precolumbian to the New Millennium: Dropping the atomic bomb. http://www.ushistory.org/us/51g.asp • 2. From John F. Kennedy Presidential Library and Museum: On the Bay of Pigs. http://www.jfklibrary.org/JFK/JFK-in-History/The-Bay-of-Pigs.aspx • 3. From U.S. History: Precolumbian to the New Millennium: On Vietnam. http://www.ushistory.org/us/55.asp • 4. Google “why bush thought there were weapons of mass destruction.” You’ll find varying opinions. Stay away from wikis and blogs! • 5. From New York Times: On the Economic Stimulus. http://www.nytimes.com/2010/02/18/us/politics/18obama.html?_r=0
Goal: Fill in the chart as a class using the strategies from the last activity.
Background Information for Decision Making and Judgment Strategies
Decision Making:Evaluating Alternatives and Making Choices • Simon (1957) • - theory of bounded rationality: human decision making strategies are simplistic and often yield irrational results. • Making Choices • Additive strategies: add up qualities you like • When to use: • decisions involve relatively few options that need to be evaluated on only a few attributes • Elimination by aspects: subtract qualities you don’t like • When to use: • More options and factors are added to a decision making task
Decision Making:Evaluating Alternatives and Making Choices • Risky decision making • Expected value: what you stand to gain or lose. • EX. Playing dice. For each roll, I have a 1/6 chance in winning and a 5/6 change in loosing. When people engage in activities that violate expected value: • Subjective utility: represents what an outcome is personally worth to an individual • EX. insurance and sense of security. • Subjective probability: involves personal estimates of probabilities…often quite inaccurate. • EX. “I’m going to win the lottery!”
Explore: Scary Movies • 1. Pick a scary movie. Name it. • 2. What makes it scary? • 3. Why are the characteristics you named in #2 “scary” to humans? • 4. Why do we feel apprehension during scary movies even though we are conscious that movies and real life aren’t the same thing? • 5. What do you think this activity could have to do with the subject of judging probabilities?
Rules of Thumb Task: Describe some of the rules of thumb that we use to judge probabilities.
Heuristics in Judging Probabilities • The availability heuristic: basing the estimated probability of an event on the ease with which relevant, personal instances come to mind. • EX. Estimate divorce rate by recalling number of divorces among your friends’ parents. • The representativeness heuristic: basing the estimated probability of an event on how similar it is to the typical prototype of that event. • EX. You flip a coin 6 times. Which of the following sequences is more likely? • T TTTTT • H T T H T H • They’re equally likely! (.5 x .5 x .5 x .5 x .5 x .5)=1/64 • Why do we think that #2 is more likely?
Heuristics in Judging Probabilities • The tendency to ignore base rates • EX. Steve is very shy and withdrawn, invariabley helpful, but with little interest in people or the world of reality. A meek and tidy soul, he has a need for order and structure and a passion for detail. Do you think Steve is a salesperson or a librarian? • Most people guess that Steve is a librarian because he looks like a librarian, even though you know that salespeople greatly outnumber librarians in the population. We ignored base rates! • The conjunction fallacy: occurs when people estimate that the odds of two uncertain events happening together are greater than the odds of either event happening alone. Related to the representativeness heuristic. • EX. You’re going to meet a man who is an articulate, ambitious, power-hungry wheeler-dealer. Is the man a college professor or a college professor who is also a politician? • Most people guess that the college professor is also a politician, because the description fits the prototype of politicians. But think! College Professor + Politician: Narrower Category College Professor: Broader Category
Heuristics in Judging Probabilities • The alternative outcomes effect: occurs when peoples’ belief about whether an outcome will occur changes, depending on how alternative outcomes are distributed, even though the summed probability of the alternative outcomes is held constant. • EX. Read “Even Objective Probabilities Are Subjective” on pgs. 327-328. Constant=probability of a focal outcome. EX. Probability of drawing the cookie you want from a jar. Perceived change=distribution of probabilities for alternative outcomes. EX. 1 out of 4 chances for drawing out the cookie you want vs. 2 out of 8 chances for drawing out the cookie you want. Result: The distribution of alternative outcomes influences the perceived likelihood of the focal outcome
After notes, write on an incident within your observation in which you or other people use/have used each of these heuristics. • EX. Availability Heuristic: I used to believe that everyone’s brothers were mean to them, because mine were. I then met people who liked their brothers. • EX. Representativeness Heuristic: My mom had 4 boys and then me. I remember thinking that it was a very unlikely pattern. I thought the distribution of siblings should be more even (boy, girl, boy, etc.). • EX. Tendency to Ignore Base Rates: I met a guy as a teenager who was very charismatic, and I thought he would probably go into politics. He went into the military, which provided a much larger percentage of the population than politicians. • EX. The Conjunction Fallacy: I remember being afraid as a high school student that neither of my alarm clocks (one was electric and one was battery) would fail on the same day, and I would be late for school. In reality, the probability that both would fail on the same day is very low. • EX. The Alternatives Outcome Effect: I once entered a talent show when I was in college. There were no prizes, but I still felt intimidated about all of the over-achievers I was up against. I had entered talent shows previously, also when there were no prizes, and had felt less intimidated. I was more nervous for the college one, though, even though there were no prizes for either (outcome the same).
(Spiral) Understanding Pitfalls • The following slides present situations that ask for your consideration and decision. Please copy the title of each slide and write a response.
The gambler’s fallacy Laura is in a casino watching people play roulette. The 38 slots in the roulette wheel include 18 black numbers, 18 red numbers, and 2 green numbers. Hence, on any one spin, the probability of red or black is slightly less than 50-50 (.474 to be exact). Although Laura hasn’t been betting, she has been following the pattern of results in the game very carefully. The ball has landed in red seven times in a row. Black hasn’t won for awhile. Which color might Laura bet on if she joins the game?
The Law of Small Numbers Envision a small urn filled with a mixture of red and green beads. You know that two-thirds of the beads are one color and one-third are the other color. However, you don’t know whether red or green predominates. A blindfolded person reaches into the urn and comes up with 3 red beads and 1 green bead. These beads are put back in the urn and a second person scoops up 14 red beads and 10 green beads. Both samplings suggest that red beads outnumber green beads in the urn. But which sample provides better evidence?
Overestimating the improbable Various causes of death are paired up below. In each pairing, which is the more likely cause of death? - Asthma or tornadoes? - Accidental falls or gun accidents? - Tuberculosis or floods? - Suicide or murder?
Confirmation bias and belief perseverance Imagine a young physician examining a sick patient. The patient is complaining of a high fever and a sore throat. The physician must decide on a diagnosis from among a myriad possible diseases. The physician thinks that it may be the flu. She asks the patient if he feels “achey all over.” The answer is “yes.” The physician concludes that the patient has the flu. What, if anything, is wrong with the doctor’s questioning?
The overconfidence effect Make high and low estimates of the total U.S. Defense Department budget in the year 2000. Choose estimates far enough apart to be 98% confident that the actual figure lies between them. In other words, you should feel that there is only a 2% chance that the correct figure is lower than your low estimate or higher than your high estimate. Write your estimates in the spaces provided, before reading further (Between 50 and 950 billion $). High estimate: ______________________ Low estimate: ______________________
Framing Imagine that the U.S. is preparing for the outbreak of a dangerous disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Which would you choose between A and B? -Program A: 200 people will be saved. -Program B: There is a one-third probability that all 600 people will be saved and a two-thirds probability that no people will be saved. Which would you choose between C and D? - Program C: 400 people will die. - Program D: There is a one-third probability that nobody will die and a two –thirds probability that all 600 people will die.
“Understanding Pitfalls in Reasoning About Decisions” • Read the article on pgs. 332-335. As you read, pay attention to these key points: • The gambler’s fallacy • Law of Small Numbers • Overestimating the improbable • Confirmation bias and belief perseverance • The overconfidence effect • Framing • Did you act as the article predicted you would? Indicate next to each pitfall.
Additional Information on “Understanding Pitfalls” Slides 40-49
Group Mini Presentations • The gambler’s fallacy: Group 1 • Law of Small Numbers: Group 2 • Overestimating the improbable: Group 3 • Confirmation bias and belief perseverance: Group 4 • The overconfidence effect: Group 5 • Framing: Group 6
UNRAVELING The Gambler’s Fallacy Laura is in a casino watching people play roulette. The 38 slots in the roulette wheel include 18 black numbers, 18 red numbers, and 2 green numbers. Hence, on any one spin, the probability of red or black is slightly less than 50-50 (.474 to be exact). Although Laura hasn’t been betting, she has been following the pattern of results in the game very carefully. The ball has landed in red seven times in a row. Black hasn’t won for awhile. Which color might Laura bet on if she joins the game? Most people would bet on black, because our prototype of a game of chance indicates that the red-black distribution should be MIXED. It’s “black’s turn.”
UNRAVELING The Law of Small Numbers Envision a small urn filled with a mixture of red and green beads. You know that two-thirds of the beads are one color and one-third are the other color. However, you don’t know whether red or green predominates. A blindfolded person reaches into the urn and comes up with 3 red beads and 1 green bead. These beads are put back in the urn and a second person scoops up 14 red beads and 10 green beads. Both samplings suggest that red beads outnumber green beads in the urn. But which sample provides better evidence? Most people think the first sample (3 to 1) is more convincing, because it shows greater predominance of red over green. However, the larger sample should be mathematically convincing, because the greater the sample size, the less of a chance the results of the draw are a fluke. The likelihood of misleading results are therefore much greater in smaller samples.
Small Number Example Think about it! Would you be more convinced if a new medication helped 4 out 5 people, or if out of 1,000 people the medication helped 750 people? Even though the ratio of people helped is greater in the smaller sample, the larger sample indicates that the new medication really does help most people, and the population is large (1,000 people) we know the results aren’t a fluke.
UNRAVELING Overestimating the Improbable Various causes of death are paired up below. In each pairing, which is the more likely cause of death? - Asthma (2, 000) or tornadoes (25)? - Accidental falls (6, 021) or gun accidents (320)? - Tuberculosis (400) or floods (44)? • Suicide (11, 300) or murder (6, 800)? • Because of the human interest in the dramatic and grotesque and our media’s interest in exploiting that interest, we often think that these noticeable events occur more often than they really do.
UNRAVELING Confirmation Bias and Belief Perseverance Imagine a young physician examining a sick patient. The patient is complaining of a high fever and a sore throat. The physician must decide on a diagnosis from among a myriad possible diseases. The physician thinks that it may be the flu. She asks the patient if he feels “achey all over.” The answer is “yes.” The physician concludes that the patient has the flu. What is wrong with the doctor’s questioning? The doctor asks leading questions, and therefore plants suggestions in mind of the patient that he has flu-like symptoms. The patient simply confirms the suggestions that have been planted. The doctor fails to ask questions, or seek evidence, outside of her postulation that the patient has the flu. She should ask multiple general questions like, “How do you feel?” and follow up questions when the patient responds. This kind of questioning would leave the full gamut of symptom options open to the patient and the patient himself would narrow them down without bias.
UNRAVELING The Overconfidence Effect Make high and low estimates of the total U.S. Defense Department budget in the year 2000. Choose estimates far enough apart to be 98% confident that the actual figure lies between them. In other words, you should feel that there is only a 2% chance that the correct figure is lower than your low estimate or higher than your high estimate. Write your estimates in the spaces provided, before reading further. (The real answer is $281 billion). High estimate: ______________________ Low estimate: ______________________ We are too confident about the estimates we make. Most of the time, the people answering this question made their range too narrow to encompass 98% certainty. A 98% confidence interval means that the correct answer would fall within the guessed range 98% of the time. In fact, the correct answer only fell within the guessed range 60% of the time! In everyday life, people predict that they’re going to be more successful in their personal goals than they actually end up being.
UNRAVELING Framing Imagine that the U.S. is preparing for the outbreak of a dangerous disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. -Program A: 200 people will be saved. (Preferred by 72%: the sure thing) -Program B: There is a one-third probability that all 600 people will be saved and a two-thirds probability that no people will be saved. (Preferred by 28%: the risky gamble) Conclusion: People go with the surest option when framed as “lives saved” - Program C: 400 people will die. (Preferred by 22%: the sure thing, but shaped pessimistically in terms of “death”) • Program D: There is a one-third probability that nobody will die and a two –thirds probability that all 600 people will die. (Preferred by 78%: the risky gamble) Conclusion: People are willing to take risks when the information is framed in terms of lives lost. They must “cut their losses.”
UNRAVELING Framing • A and C are the same, but A uses language that we like (“saved”) and C doesn’t (“people will die”). • B and D are the same, but B uses no negative language (even says “no people will be saved”) and D uses the word “die” twice. • Subjects chose the sure thing when the decision was framed in terms of lives saved, but they went with the risky gamble when the decision was framed in terms of lives lost. • When seeking to obtain gains, people tend to avoid risky options. However, when seeking to cut their losses, people are much more likely to take risks.
(Spiral) Decision Time Instructions: Structure the answers to the following questions into a paragraph. • Think of an important decision that you may have to make within the next five years. • What specifically should you consider before making this decision? Name 3 considerations. • Will you use the algorithms, heuristics, additive strategies, elimination by aspects, or some other strategy? Why? • Which of the decision making pitfalls might you encounter during this particular decision? Choose two. • How will you avoid these two decision making pitfalls?
