9/12 Probability Estimation

Question 1

Probabilty it will rain given that it is cloudly or probabilty enjoy a movie given my friend saw it is what?

Answer 1

Conditional probability

Question 2

History of probability estimation research

Answer 2

Thought we sucked at it

But! Heuristics proposed.

Question 3

Tversky & Kahneman’s Heuristics:

The Availabilty Heuristic

Ease of retrieval is indicative of…

Answer 3

frequency

(words with r as first letter, vs. 3rd letter) estimate more begin with r

Question 4

Availability Heuristic

Gabrielcik & Fazio (1984) Experiment

What did they manipulate?

Answer 4

Manipulate Accessbility

Subliminal priming words containing letter t (tattoo, tentative) for milliseconds led participants to judge t to occur more frequently

Do more words in English begin with blank or t? Judge the t.

Question 5

Availability Heuristic:

Schwarz et al. 1991: Directly Manipulated the subjective experience

Answer 5

Ease of retrieval paradigm, easier to list 6 instances of past assertiveness than 12, greater ease resulted in higher ratings of one’s assertiveness

You might have listed more experiences of assertiveness, but because the retrieval tasks is difficult, at some point you start viewing yourself as less assertive due to the difficulty

Question 6

Know the difference between thse terms:

Available vs. Availability Heuristic

Answer 6

Available: Does the knowledge exist in memory?

Availablility Heuristic: Notion that we infer frequency from ease of retrieval

Question 7

What do we mean when we say something is accessible?

Answer 7

How readily can available construct be accessed?

Question 8

Salience: What does this mean?

Answer 8

Some situational factor FORCES attention to an entity in the environment or a construct in memory

Not memory, it’s the situational factor forcing attention

Question 9

Tversky & Kaheman’s Heuristics:

Anchoring and Adjustment Heuristic

Answer 9

Is X higher or lower than randomly determined number? Insufficient adjustment from anchor.

Example: UN number wheel. People fail to adjust sufficiently from anchor

Question 10

Tversky & Kaheman’s Heuristics:

Selective Accesibility Model

(Mussweiler & Strack, 1999)

Answer 10

Anchor provides intital hypothesis

Tested in hypothesis-consistent manner (search our memory to see if hypothesis is good, but we tend to look for evidence consistent with hypothesis: Knowledge consistent with hypothesis is rendered accessible and forms basis for judgement)

Question 11

Tversky & Kaheman’s Heuristics:

Incidental Anchors (Critcher & Gilovich, 2008)

Answer 11

Anchoring bias does not require an initial, overt comparison

Estimates of how much you’d spend at restaurant named “studio 17” vs. “Studio 97”

Question 12

Tversky & Kaheman’s Heuristics:

Representativeness Heuristic

Answer 12

Similarity = likelihood

Can result in insensitivity to base rates

How we misinterpret questions: decide if X is engineer, how similar are they to an engineer stereotype.

Question 13

Lawyer-Engineer Problem
Ginossar & Trope (1987)

30% Engineer
70% lawyer

What was the experiment? What was the twist at the end.

Answer 13

Higher estimate say engineer than expected if given engineer sounding paragraph.

However, new experiment: Ps given 2 nondiagnostic and 2 diagnositic

nondiagnostic descriptions either with no or mixed info. Particpants do well on these.

Diagnosistic descriptions clearly lawyer or engineer.

Results: Nondiagnostic do well, but for diagnostic info, forget about the base rates. (more 40/60, less 30/70).

Twist: Ask about a new person.
Results: No prior problem and diagnostic prior problem were fine, back to using base rates, BUT now non-diagnostic base rate of engineers when up. Based on new information.

THIS IS REPRESENTATIVE HEURISTIC

Question 14

Krosnick, Li, and Lehman (1990)

Previous research: base-rate info before individuating.

Why is this a problem? (hint: Gricean rules of communication)

Answer 14

This could all be the recency effect

Gricean rules: assume speaker intends to communicate relevant and important info last)

Question 15

Krosnick, Li, and Lehman (1990)

To combat possible recency effect, what did they manipulate in their base-rate info before individuating experiment?

Answer 15

Order of base-rate vs. individuating info when giving info AND whether order could reflect coversation, by telling participants order was random.

Divorce experiment

Results:

Standard: Base rate first is highest divorce probabilty, lower for base rate second

Randomly: Base rate first still higher, but less different.

Question 16

Conditional Probabilities via Bayes Theorem

Answer 16

Given symptom, do you have dieases? Given disease, do you have the symptom?

P (D | S) = prob disease given symptom

= P(S | D) x P(D) / P(S)

Do we do this in daily life? No.

Question 17

Gavanski & Hui (1992)

The shape experiment

how do they use Bayes Theorem?

Answer 17

20 shapes. 2 black circles, 6 line circles, 4 line squares, 8 black squares.

Prob black given round.

You form smaple spaces that only include specific objects. “Black given round” is a whole equation BUt you can notice: 8 found objects, 2/8. Much easier with sample space.

Directly assess prob. from revised sample space.

Question 18

Gavanski & Hui (1992)

Remember what this experiment is.

Answer 18

Cute little alien fella.

Divide critters into groups: 81% by head shape not nose. Round and square head more natural categories than noses. Easier sample space to form.

36 faces. 4 stimluus configurations that vary base rates.

Easier if up nose given Plam, because I was thinking about head shape.

But Plam given up turned nose…well I wasn’t thinking about that.

Participants varied in how many of each kind they saw. High base rate: Lots of Plams with up noses, low base rates not a lot of plams with up noses.

Results: Who failed?

P(Category | Feature) at the high base rate 80%). At the low base rate (50%), more accurate because you guess around 50%.

P (Feature | Category) were good on both. GIVEN category I was try to remember, do fine.

Question 19

Sherman, McMullen, and Gavanski (1992)

Compared probabilities conditioned upon a natural categoy (Gender) to those conditioned upon an ad hoc category (Blue and Green)

Answer 19

Natural: 100 random women, prefer blue to green, reasonably accurate

Ad hoc: 100 random people who like blue > green, how many are women. Inaccurate.

natural sample space = more accurate estimation.

Question 20

McMullen, Fazio, & Gavanski (1997)

What did they do to the cute little critters in 1997 and why did they do it?

Which conditions were easier for what group.

Answer 20

**Hair and teeth instead of the nose. To test MOTIVATION. **

Critters take or give points.

Attend to Hair (side part +1000, center -1000) Teeth (one tooth +20, two teeth -20 points)

Attend to Teeth, the teeth are worth more points.

Categorization follows attention. From similarity judgements, understand how they were categorized.

Similarity index (2 pairs w/common hair - 2 pairs with common teeth). similarity inputs in multidimensional scaling program.

Did the same base rate thing: low was 50%, high was 80%

Results:

Judge Hair GIVEN tooth easier for teeth

**Judge Tooth GIVEN Hair easier for hair group **