Module 28 Thinking Can you get out?

Module 28 Thinking Can you get out?

Thinking • Cognition • mental activities associated with thinking, knowing, remembering, and communicating • Cognitive Psychologists • study these mental activities • concept formation • problem solving • decision making • judgment formation

Thinking • Concept • mental grouping of similar objects, events, ideas, or people • set of ideas and properties which can be used to group things together (abstract – justice, concrete – furniture). • Prototype • mental image or best example of a category • matching new items to the prototype provides a quick and easy method for including items in a category (as when comparing feathered creatures to a prototypical bird, such as a robin)

Problem Solving Nine Dot Tower of Hanoi Cognition Demos

Problem Solving Petals around a Rose Game Riddles

Problem Solving Petals around a Rose Game – • How did you solve the problem? • What information did you attend to in attempting to solve the problem? Why? • Do obstacles in problem solving reflect problems in isolating relevant information or are they also the result of frustration or performance anxiety?

Problem Solving Levine’s Theory of Hypothesis Testing – • We begin a concept-formation task with a “pool” of hypotheses. • From this “pool” we select a “working hypothesis” that determines our initial responses. • As long as feedback is consistent with our working hypothesis we retain it. • If feedback contradicts our hypothesis, we shift and choose a new working hypothesis that is consistent with the current feedback, and also consistent with as much of the feedback as we can remember.

Write out the specific steps you would take to solve the following problem – 2876948 ÷ 4 Answer - 719237 Thinking

Thinking • Algorithm • methodical, logical rule or procedure that guarantees solving a particular problem • contrasts with the usually speedier–but also more error-prone--use of heuristics

Thinking • Heuristic • simple thinking strategy that often allows us to make judgments and solve problems efficiently • usually speedier than algorithms • more error-prone than algorithms

Unscramble S P L O Y O C H Y G Algorithm all 907,208 combinations Heuristic throw out all YY combinations other heuristics? Thinking

Thinking • Insight • sudden and often novel realization of the solution to a problem • contrasts with strategy-based solutions • Confirmation Bias • tendency to search for information that confirms one’s preconceptions • Fixation • inability to see a problem from a new perspective • impediment to problem solving

The Matchstick Problem • How would you arrange six matches to form four equilateral triangles?

The Candle-Mounting Problem • Using these materials, how would you mount the candle on a bulletin board?

Thinking • Mental Set • tendency to approach a problem in a particular way • especially a way that has been successful in the past but may or may not be helpful in solving a new problem • Train Example

Thinking • Functional Fixedness • tendency to think of things only in terms of their usual functions • impediment to problem solving

The Matchstick Problem • Solution to the matchstick problem

The Candle-Mounting Problem • Solving this problem requires recognizing that a box need not always serve as a container

Heuristics • Representativeness Heuristic • judging the likelihood of things in terms of how well they seem to represent, or match, particular prototypes • may lead one to ignore other relevant information

REPRESENTATIVENESS: Base predictions on similarity to other events or situations (but we may ignore other relevant information such as the actual frequency of events) Assume that all families with exactly six children are surveyed in a city. In 100 of these families the exact order of births of boys (B) and girls (G) was G-B-G-B-B-G. What is your guess as to the number of families in which the exact order of birth was each of the following? Estimate a number for each of the following (adapted from Kahneman & Tversky, 1973): 1. G-G-B-G-B-B For each of these 2. B-B-B-B-B-B possibilities, the 3. G-B-B-G-B-G expected number 4. B-B-B-G-G-G of families is 100. Statistically, all four alternatives are equally likely (50% B, 50% G) Sex of previous births doesn’t affect sex of next birth. REPRESENTATIVENESS: Which birth orders “look” random? Most people misunderstand how randomness works. They expect things to “even out” in the short run. • Assume that all families with exactly six children are surveyed in a city. In 100 of these families the exact order of births of boys (B) and girls (G) was G-B-G-B-B-G. What is your guess as to the number of families in which the exact order of birth was each of the following? Estimate a number for each of the following: • G-G-B-G-B-B • B-B-B-B-B-B • G-B-B-G-B-G • B-B-B-G-G-G

REPRESENTATIVENESS: Base predictions on similarity to other events or situations (but we may ignore other relevant information such as the actual frequency of events) • Imagine that you just met a man named Steve. Steve is very shy and withdrawn, invariably helpful, but with little interest in people or in the world of reality. A meek and tidy soul, he has a need for order and structure and a passion for detail. Which statement about Steve is more likely: • Steve is a retail salesperson • Steve is a librarian • Both “a” and “b” are equally likely (within 5% of each other) • Imagine that you just met a man named Steve. Steve is very shy and withdrawn, invariably helpful, but with little interest in people or in the world of reality. A meek and tidy soul, he has a need for order and structure and a passion for detail. Which statement about Steve is more likely (adapted from Kahneman & Tversky, 1973): • Steve is a retail salesperson (3,964,680 in the United States) • Steve is a librarian (139,460 in the United States) • Both “a” and “b” are equally likely (within 5% of each other) • Approximately 28.4 retail salespersons for every librarian. • Steve is much more likely to be a retail salesperson. • But Steve’s description fits our stereotype of librarians. • Data from the Bureau of Labor Statistics (2000) survey

At a party there are 30% engineers and 70% lawyers. You meet a person at this party and find out he is a 45 year old man. He is married and has 4 children. He is generally conservative, careful and ambitious. He shows no interest in politics and social issues and spends most of his free time on hobbies, that include home carpentry, sailing and mathematical puzzles. How likely is it that this person is an engineer?

Linda is thirty-one years old, single, outspoken and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in antinuclear demonstrations.” • Linda is a teacher in an elementary school. • Linda works in a bookstore and takes yoga classes. • Linda is active in the feminist movement. • Linda is a psychiatric social worker. • Linda is a member of the League of Women Voters. • Linda is a bank teller. • Linda is an insurance salesperson. • Linda is a bank teller and is active in the feminist movement.

Representativeness Heuristic: • We make judgment of category in a simple way: how representative is this person of the prototypical member of the category? • Linda problem • Lawyer problem • Misconceptions of chance • H H H H H H H H • T H T H H T T H • H H H H T T T T

Heuristics • Availability Heuristic • estimating the likelihood of events based on their availability in memory • if instances come readily to mind (perhaps because of their vividness), we presume such events are common • Example: airplane crash

Availability Heuristic • How we judge the frequency or likelihood of an event. That is to say, we ask how likely is “event X” to occur? • If we can easily think of an example, we think it is more likely to happen. • But, vivid events are remembered easier

AVAILABILITY: Base predictions on information that is easy to think about or recall (but it may not mean it is more likely) • Are there more words in the English language that begin with K or have K as their third letter? • There are more words that begin with K • There are more words that have K as their third letter • Both “a” and “b” are about the same (within 5% of each other). • Are there more words in the English language that begin with K or have K as their third letter? (adapted from Tversky & Kahneman, 1973) • There are more words that begin with K (easier to think of examples) • There are more words that have K as their third letter • Both “a” and “b” are about the same (within 5% of each other). In Slovic, Fischhoff, & Lichtenstein’s (1976) study, few participants guessed correctly on these pairs (see the % correct at left) The more common cause of death is identified in green along with its ratio compared to the less common cause of death. 20% A. Stroke (1.85 to 1) B. All accidents 23% A. Diabetes (1.25 to 1) B. Breast cancer 25% A. Lung cancer, or B. Stomach cancer (1.25 to 1) 17% A. Appendicitis (2.00 to 1) B. Pregnancy42% A. Tornado, or B. Asthma (20.90 to 1) For each of the following pairs, indicate which cause of death was more frequent in the United States during the 1970’s: 1. A. Stroke, or B. All accidents 2. A. Diabetes, or B. Breast cancer 3. A. Lung cancer, or B. Stomach cancer 4. A. Appendicitis, or B. Pregnancy5. A. Tornado, or B. Asthma

Thinking • Overconfidence • tendency to be more confident than correct • tendency to overestimate the accuracy of one’s beliefs and judgments • Example – Parents asking how you did on a test.

Thinking Are We Scaring Ourselves to Death?

Thinking • Framing • the way an issue is posed • how an issue is framed can significantly affect decisions and judgments • Example: What is the best way to market ground beef--as 25% fat or 75% lean? • General Example

Thinking • People are “risk averse” – the first dollar that one acquires is worth slightly more than the second, the second slightly more than the third, and so on, until those with a lot of money valued each additional dollar very little. • Others would say that people are “loss averse” – losses loom larger than gains. Thus, people avoid fair bets because the prospect of gain isn’t worth the pain of loss.

Thinking • Belief Bias • the tendency for one’s preexisting beliefs to distort logical reasoning • sometimes by making invalid conclusions seem valid or valid conclusions seem invalid • Belief Perseverance • clinging to one’s initial conceptions after the basis on which they were formed has been discredited

Is the conclusion in the following valid? No cars run when they’re out of fuel. My car is out of fuel. Therefore my car does not now run. YES! The conclusion of the statements is valid. Thinking

Is the conclusion in the following valid? Some A are B. Some B are C. Therefore some A are C. At first glance this may appear logical, however it is not. Thinking

Thinking Some women are Democrats. Some Democrats are men. Therefore some women are men.

Some of the beekeepers are artists. None of the chemists are beekeepers. Therefore some of the artists are not chemists. Valid – Yes! Some birds can swim. No fish are birds. Therefore some animals that swim are not fish. Thinking

Artificial Intelligence • Artificial Intelligence • designing and programming computer systems • to do intelligent things • to simulate human thought processes • intuitive reasoning • learning • understanding language

Artificial Intelligence • Computer Neural Networks • computer circuits that mimic the brain’s interconnected neural cells • performing tasks • learning to recognize visual patterns • learning to recognize smells

Module 28 Thinking Can you get out?