Week 5

Why do stereotypes stick with us so much?

A stereotype is, at its core, a way of constructing stories about a given category in order to make the world more coherent and reliant on patterns—which is why our System 1 likes to rely on them

When spouses are asked to estimate the percentage of the housework that they do, their responses usually add up to more than 100%. Why is this?

Availability heuristic: People rely too much on their own experiences and ignore the fact that they are often not privy to the work that their spouse may have contributed.

Differentiate between experts and the average person with regard to perspectives on risk

Average people, most of the time, are deeply affected by stories and are highly subjective. The experts, on the other hand, represent the confidence in statistical objectivity.

Bayesian statistics

Bayes's rule specifies that prior beliefs (base rates) should be combined with representativeness or the diagnosticity of the evidence (likelihood of the hypothesis). For example, if 3% of graduate students are enrolled in computer science (the base rate) and you also believe that Tom W is 4 times more likely to be a computer scientist than a student in another field, the probability that Tom W is a computer scientist is still only 11%.

The relevant rules for Tom W are provided by _______________ statistics

Bayesian

Why is it hard for us to understand regression to the mean?

Because when we see a change between events, we search for causality between this independency, though regression to the mean explains it. Though regression to the mean is not a cause, it is a statistical explanation. In other words, luck (good or bad) eventually balances out.

Describe the memory process with regard to the availability heuristic

Has to do with fluency and the ease/difficulty in coming up with evidence; if recalling evidence is hard, then it must be because S1 says there is not many examples

Describe the Alar scar and its significance

Provides a similar example in which the small risks of a chemical sprayed on apples became hugely overstated. With regard to small risks, we either ignore them or give them far too much weight: due to an availability cascade.

Success =

talent + luck (good or bad)

Lottery wins are easy to recall. What consequence does this have?

Availability Heuristic: People then overestimate how much they will win the lottery

How many instance does it take to get an impression of ease?

0; If presented with the strings of letters "XUZONLCJM" and "TAPCERHOB" we immediately know, without generating any instances, that far more words can be constructed with the second string of letters.

People are more affected by ease of retrieval than by content if they meet certain conditions. Describe some examples.

1. Engaged in another effortful task simultaneously 2. In a good mood 3.Have a large amount of faith or intuition 4. Made to feel powerful: they have more confidence in their own intuition and judgment than they might otherwise feel.

What is Slovic's perspective on risk and policies?

1. Risk is subjective, and people's emotions about it should be taken into account when creating public policies: favours the public's side in policies (Risk is invented by us so that we can understand and cope with the dangers and uncertainties of life). 2. States that experts measure risks by the number of lifes (life-years) lost, while the public draws finer disctinctions ( e.g. "good deahts" and "bad deaths"

How can we combat the representativeness heuristic?

1. We should anchor probability on a plausible base rate 2. Question how much the evidence presented to us should affect our answer: we should slow down, question our intuitions, and rely on our System 2 processing.

Why are people bad at preparing for a disaster?

After disasters, people are very concerned and buy insurance, but this concern dims over time. Protective actions are usually designed to be adequate for the worst disaster that has been experienced. -> It is difficult for people to prepare for disasters that may be worse: people put more stock into their own experiences, rather than objectively identifying possible risk.

Explain these paradoxical findings: 1. People who ride their bikes more often recall that they ride their bikes less 2. Students who rated a course very well did so when they wrote a lot of bad things about a course

As we come up with more instances, fluency and ease decreases, leading to such paradoxical findings. For 1., when we have difficulty recalling instances of riding out bikes, we will think we ride them less. However, if we experience ease with coming up with those examples, we will think we ride our bikes more often than we do.

Describe the Love Canal Affair and its significance.

Buried toxic wast exposed during a rainy season in 1979, causing contamination of the water well beyond standard limits. But scientists believed that the risks were overstated, and the expense incurred by cleaning up the waste could have saved many more lives if directed to other priorities.

Norbert Schwarz studied how people's impressions of frequency are affected by requirements to come up with specific examples of that category. Describe the study and it's significance (hint: assertiveness)

Description: Asked people to list examples of assertiveness IV: 6 examples / 12 examples DV: Self-report of assertiveness Results: people who had listed 12 instances rated themselves as less assertive than people who listed 6 (listing 6 is easier->ease) Significance: 1. Difficulty (no ease) in coming up with 12 examples translates to feeling less assertive (disfluent experience) 2. New participants who see the behaviours listed from previous participants will tell you that those who list more examples are more assertive (they don't have a subjective answer in this case)

Describe the heart attack survey that illustrates how we can reduce the conjunction fallacy

Description: In a study people are told that a health survey was conducted among 100 adult men. IV: ->"How many of the 100 participants have had one or more heart attacks?" ->"How many of the 100 participants both are over 55 years old and have had one or more heart attacks?" DV: The number of people Result: People will commit the conjunction fallacy far less with numbers than if they are asked about percentages Significance: We are affected less because we think about concrete individuals. This example shows how our brains are very ill-equipped to deal with pure statistics and probability even if we understand the underlying calculations. By using numbers, you think about individual people, it's easier to visualize, you are able to think about the venn diagram of these questions

Tom W puzzle

Description: Kahneman asks us to rank the likelihood of Tom studying in nine different fields (e.g., business, medicine, humanities, etc.). IV: Given only this fact or give a description of Tom (likes sci-fi, is intelligent but not really creative, not very sympathetic) DV: Ranking of fields Results: ->Without description of time: people rank based on base rates -> With description of Tom, people will prioritize fields like computer science and engineering, even though statistically these groups are much smaller, because he is more "representative" of those categories. Significance: Base rates become largely irrelevant (neglect) to people in the face of new information about Tom's personality. People instead prioritize the similarity of Tom W to the stereotype of a computer scientist

Describe the Mark counterexample

Description: Participants presented with "Which alternative is more probable: that Mark has hair, or that Mark has blond hair." Significance: The Mark counterexample further demonstrates the power of coherence in the Linda example: this example does not tell a story, and therefore we do not make the same mistakes in evaluating probability. Logic prevails!

Describe the Linda Study and its significance

Description: Participants presented with a "single, outspoken, and very bright" woman named Linda who majored in philosophy and was concerned about social justice. DV: Asked which occupation is more probable for Linda Result: Most people will say that Linda is more likely to be a bank teller who is active in the feminist movement than merely a bank teller Significance: 1. Demonstrates how our intuitive processing can even overrule principles of logic, demonstrating the depth to which we have a preference for coherent stories over statistical principles. 2. The most coherent story is not necessarily the most probable, but perhaps the most plausible. Adding detail to scenarios might make them more persuasive, but still less likely. 3. The target question is substituted because the descriptions seems more coherent and consistent with feminist x bank teller (priming and activation of accessible schemas)

Describe Dinner plates experiment and its significance

Description: People were presented with sets of dinnerware that were almost identical, and most dishes were in good condition. IV: Present either both or one of the sets DV: Amount that you'd pay for Results: -> When people are shown both sets of dinnerware, they will on average pay a little more for Set A than for Set B ($32 vs. $30). -> But when people are shown only one set, the results reverse: people would pay on average $23 for Set A and $33 for Set B, even though Set A contains all of the dishes in Set B, because no one wants to pay for broken dinnerware Significance: Here people do commit the conjunction fallacy because they have nothing that they can anchor the value of the set to. This also introduces a concept of prospect theory, which is that our decisions about money and goods are governed less by intrinsic value and more by comparisons. When you take out the other, we can't add the probability of something not happening. Thus, our subjective probability increases

Schwarz and his colleagues discovered that people who are personally involved in the judgment are more likely to consider the number of instances they retrieve and less likely to go by fluency. Explain the heart disease study that supports this

Description: Recruited either students with IV1: No family history of heart disease / family history of heart disease IV2: Recall 3 or 8 behaviours that could affect their health Results: ->For those with no family history, they felt safer if asked to retrieve many risky behaviours (which they found hard to do) ->For those with family history, they felt greater danger when they retrieved many instances of risky behaviour Significance: When people are personally involved in the judgment, they are more mentally invested in the answers that they provide, and thus they think more with System 2 (deliberation and logic) and less with System 1 (which, as in the previous example, relies purely on ease and intuition).

Paul Slovic asked participants in a survey to consider pairs of causes of death (e.g., diabetes and asthma). Describe the study. Participants indicated the more frequent cause and estimated the ratio of the two frequencies. What was the result?

Estimates of causes of death were warped by media coverage, which is biased toward novelty and poignancy (for example, death by accidents were judged to be more than 300 times more likely than death by diabetes, but diabetes is actually four times more likely). Significance: 1. Causes of death that yielded frightening and visceral images were particularly overestimated (ease with recalling these) 2. This thought process also seems linked from the emotional aspect of System 1, as frightening images become even more available and overestimated in our minds.

Ajzen showed participants descriptions of students He told one group that 75% of the students passed an exam Told another group that 25% of the students had passed. When participants were asked which group was most likely to pass, what was the result?

Every student was judged more likely to pass the high-success condition than in the high-failure rate, because participants assumed that the test had been brutally difficult. Significance: Like the green cab experiment, constructing a stereotype allowed the participants to make correct inferences about the students—that generally, it is safe to assume that the students in the 75% passing group were more likely to have done better than the students in the 25% passing group.

Richard Nisbett and Eugene Borgida told their students about a "helping experiment" that had been conducted a few years earlier. In this video, participants were separated into individual booths made to think that someone in another booth was having a seizure and choking ->Only four out of fifteen them responded to the person's call for help When students were shown videos of brief interviews with two of the participants (didn't tell them the results), they were told that these two individuals did not help the choking person. Nesbitt and Borgia then asked them to guess the global results, and the students' guesses. How accurate were these? Why is the result significant?

Extremely accurate Significance: Again, the stories (particularly about individuals) take precedence over the statistics. When confronted with surprising individual cases, we are more likely to make accurate inferences about the general population than if they are shown a surprising statistic and unsurprising individual cases.

Regression to the mean

If the first measurement is extreme, second measurement will be closer to the mean

Describe the change to the cab story that shows causes trump statistics

If the first sentence had said that green cabs are involved in 85% of accidents, people give more weight to that information because they construct a story assuming that the green cabs are more reckless. Significance: This slight change in the way the information is presented also supports the idea that stories take precedence over statistics, as this fact provides people with the ability to construct a coherent story about the green cabs: stereotype about green cabs is created

Describe the Massachusetts Department of Education blunder

Implemented statewide mandatory testing (math, english; like EQAO) 1. Ranked schools by performance year 1 ->Schools in top 10% scores expected to maintain good performance ->Schools in bottom 10% scores expected to improve scores 2. Then ranked schools by performance year 2 ->Schools in top 10% scores went down ->Schools in bottom 10% scores went up Significance: Good performance at t1 is followed by poor performance at t2 and bad performance at t1 followed by good performance at t2. Superintendent trying to find patterns between the two instances but it was simply just regression to the mean

How does substitution relate to representativeness?

Instead of answering the question about probability, people answer a question about similarity; basing judgments on the similarity/typicality of a stimulus to category exemplars

Describe how the availability heuristic is applied to group projects

It is easy for you to think of your contributions to a group project yet we neglect what others contribute because we don't see what they do: then we feel we do more than everybody else ->Leads to bitterness and resentment

From Max Bazerman's Judgment in Managerial Decision Making. The given circumstances are as follows: you are a sales forecaster for a department store chain. All stores are similar in size and merchandise, but their sales differ due to location, competition, and random factors. Overall sales are expected to increase by 10% across the board. It then asks the reader to complete a table, predicting how each store will do in the coming year. What are the results?

It is tempting simply to add 10% to each store's sales, but one must also adjust for regression and add slightly more to the underperforming stores, and slightly less for the overperforming stores. Significance: Understanding the effects of regression can allow people to more accurately make predictions about the future, as is the case with this scenario in which the reader can attempt to make projections about the performance of different department stores.

representativeness heuristic

Judging the likelihood of things in terms of how well they seem to represent, or match, particular prototypes; may lead us to ignore other relevant information (e.g. base rates)

How to get a better grade participation assignment?

Leveraging difficult to recruit examples. Ask professor to give you 10 reasons for why she shouldn't give you an A. Then, the difficulty for coming up with these reasons leads the teacher to thinking that the student should get a better grade

When surveying people about the benefits and risks of various technologies, people who liked a technology (liking obtained when reading a paper in favour of them) exaggerated its benefits and underplayed its risks; when they disliked a technology, the opposite happened. Describe how this supports the affect heuristic.

Like the halo effect, if we are exposed to one side of the story, it then becomes coherent and consistent. Because we like this, we will believe in the story more (E.g. a favour story will lead us to exaggerate the benefits).

Describe the hit and run cab study and its significance

Scenario: a cab was involved in a hit and run accident at night. 85% of the cabs in the city are green and 15% are blue. A witness identified the cab as blue; witnesses under these circumstances correctly identify cab colours 80% of the time. Result: Usually, people ignore the base rates of the number of cabs, and instead favour the witness's accuracy, guessing about 80% percent. However, according to Bayes's rule, the correct answer is 41%. Significance: People vastly prefer stories over statistics. Instead of relying on the numbers provided to them (the fact that there are far more green cabs than blue cabs), people prefer to rely on the story provided by the witness.

When Schwarz provided an explanation for the difficulty (i.e., by telling participants that the background music (present vs absent) would affect performance in the memory task), how did participants rate themselves when coming up with the examples of assertiveness and why was this the result?

Self-report of assertiveness = for 6 vs 12 examples. Significance: 1. S2 is telling you that the strain is then from the background music which would affect your performance. In other words, any surprise from steep drops in fluency that S1 would typically recognize is now reset by S2 with a cause present. In other words, S2 rejects difficulty in recall: you pay more attention to why it's difficult to come up with examples. Availability heuristic is eliminated because S2 monitors the diagnosticity of the information

How is substitution applied with the availability heuristic?

Substitutes (S1) the question "how frequent or how sizeable is this category?" with "how easily can I think of examples of this category?". Moreover, events that attract attention (like celebrity divorces), dramatic events in the news (like plane crashes), and personal experiences, pictures, and vivid examples will all alter our sense of how frequent they are.

Describe the basic process of the availability heuristic

Target Q: How frequent is this event? ->If it's difficult to retrieve, we think it's not frequent/familiar/etc Heuristic Q: ease of retrieval? ->S2 will endorse the answer because ease=fluency=familiarity=frequency In auditioning priming is involved since highly accessible concepts are easier to retrieve (e.g. when thinking about sharks, you will think about your odd shark schemas, and will think the risk is higher than it actually is).

Describe the process of the representativeness heuristic

Target Q: Likelihood that X belongs to category A? ->Likelihood requires you to consider probability and statistics (base rates) Heuristic Q: similarity of X to exemplars of category A? Plausibility that X could be in category A? ->Similarity does not equal likelihood

Describe the process of the conjunction fallacy

Target Q: Which is more probable? Heuristic Q: Which is more plausible/coherent?

Richard Nisbett and Eugene Borgida told their students about a "helping experiment" that had been conducted a few years earlier. In this video, participants were separated into individual booths made to think that someone in another booth was having a seizure and choking ->Only four out of fifteen them responded to the person's call for help When students were shown videos of brief interviews with two of the participants, who appear to be nice, normal decent people, the students believed that both individuals would rush to the choking person's aid—despite the fact that they knew there was only a 27% chance of this being the case. Why is this significant?

The 27% statistic is surprising, and it conflicts with our idea of people (and of ourselves) as generally decent and helpful. And so, when individuals appear to be decent and helpful, they confirm our previously held beliefs and this information takes precedence over the statistic.

Analysis of the Olympic ski jump, in which athletes jump twice. If athletes have a good first jump, commentators say they will have a worse second jump because they will feel pressure; if athletes have a bad first jump, commentators say that they have nothing to lose and will have a better second jump What is right and wrong here?

The analyst has detected a principle of luck and chance but has assigned a causal story to it Significance: Commentator's analysis also demonstrates some of the ways in which we will create explanations to provide a sense that there is a causal explanation for the athletes' performances.

How can we debiase the conjunction fallacy?

The error incurred in the conjunction fallacy is greatly reduced, however, if people are asked about numbers rather than percentages.

typicality effect

The finding that the more typical members of a category are classified more quickly than the less typical category members

An instructor wouldn't praise flight subordinates. Instead, he would punish them because they would do better in performance versus praising them. Why is this wrong?

The instructor was inappropriately attaching causality between his actions and the cadets' performances, ignoring the fact that a particularly good execution of a certain maneuver will likely be followed by a less well-executed maneuver, and vice-versa with a particularly bad execution Significance: We prefer to think that all events have causal explanations, despite the fact that some things simply occur due to randomness.

What was the significance of Richard Nisbett's and Eugene Borgida's studies?

The results demonstrate that when students were surprised by a statistical fact, the students did not change their assumptions. But when surprised by individual cases, they immediately made the generalization and inferred that helping is more difficult than they thought. And, being surprised by one's own behavior is more powerful than being surprised by people's behavior more generally. Thus, when we are surprised by individual cases (including ourselves), we are more likely to learn the general lessons that he offers.

What is Sunstein's perspective on Risk and policies?

The system of regulation in the United States reflects public pressure and sets poor priorities. He believes that risk can be calculated by lives and dollars cost. Sides more with the experts.

affect heuristic

The tendency to consult one's emotions instead of estimating probabilities objectively. People substitute the question "What do I think about it?" with "How do I feel about it?"

Why is the likelihood part in Bayesian statistics important?

There is some truth to these stereotypes, so we should consider some likelihood in these predictions Though, in the real world, you should question likelihood because we tend to exaggerate it (e.g. coherence from specific information -> stereotype)

What is the difference in people's answers when presented with 1. highly intelligent women tend to marry men who are less intelligent than they are. 2. the correlation between the intelligence scores of spouses is less than perfect

Though they both mean the same thing, people will readily explain the first statement in terms of causality. Significance: People have a hard time understanding probability that does not have an explanation, and so they try to invent explanations as to why intelligent women might intentionally marry less intelligent men.

Exemplar Theory of Categorization

We compare instances to examples/members of a category ->Similar enough? Yes, it belongs ->Not similar enough? No, it doesn't belong

What do we emphasize more? Talent or luck?

We often place more emphasis on talent than on luck in determining what makes someone successful. On any day, if a person does particularly well one can assume that their success was due at least in part to luck—but we have a very difficult time understanding this in practice and like to believe that their talent is the true cause of good performance.

availability heuristic

estimating the likelihood of events based on their availability in memory; if instances come readily to mind (perhaps because of their vividness; ease associated), we presume such events are common. Thus, people overestimate and over-rely on their own experience when estimating a category

Availability Cascade

when (i) the media's focus on a topic and (ii) the emotional reaction of the public to that topic, feed on each other in a cycle of escalating intensity. It relies on a human fault: that we tend to become particularly fearful or particularly affected by gruesome and unique events. Media stories then often focus on these kinds of events because we have such strong reactions to them, and as a result we become even more fearful.

conjunction fallacy

when people think that two events are more likely to occur together than either individual event. This fallacy violates logical possibilities