• Shuffle
Toggle On
Toggle Off
• Alphabetize
Toggle On
Toggle Off
• Front First
Toggle On
Toggle Off
• Both Sides
Toggle On
Toggle Off
Toggle On
Toggle Off
Front

### How to study your flashcards.

Right/Left arrow keys: Navigate between flashcards.right arrow keyleft arrow key

Up/Down arrow keys: Flip the card between the front and back.down keyup key

H key: Show hint (3rd side).h key

A key: Read text to speech.a key

Play button

Play button

Progress

1/13

Click to flip

### 13 Cards in this Set

• Front
• Back
 Inductive reasoning Probabilistic. Conclusions that are likely, but not necessarily true. Discussed in “Judgment and Decision Making” lecture Hit rate example If the disease is present, the test will be positive 80% of the time. False alarm rate example If the disease is absent, the test will be positive only 10% of the time. Base rate example BUT -- If only 1% (10 out of 1000) people actually have the disease: optimal normative theories Current evidence Hit Rate False Alarm Rate Base rate Bottom line on Bayes Theorem Takes 3 things into account: The BASE RATE - P(H) The HIT rate - P(E/H) The FALSE ALARM rate - P(E/not H) Bayes’ theorem simply describes how to do the computation on these 3 pieces of information A problem is base rate neglect: people often ignore the base rate and pay too much attention to the evidence. More on base rate neglect People often rely too much on the current evidence and ignore the base rate. People put more emphasis on similarity than on base rate 2 blue and 1 red is more similar to 20 blue and 10 red (bag B) Why do people show base rate neglect Theory: People rely on heuristics in judgments under uncertainty rules of thumb are quick, require fewer cognitive resources, are “good enough” for everyday reasoning. Lab studies exploit the weaknesses of the heuristics Representativeness of heuristics Judge whether A comes from class B by relying on the similarity of A to B. e.g., judge whether your patient is schizophrenic by relying on how similar he or she is to schizophrenics. Problem: Focuses too much on current evidence - ignores base rates Engineer with description People ignore base rate because of biases and expectations Base rate ignored even with _______ neutral description One more part of lawyer vs. engineer experiment When given no other information, subjects can use base rate information to judge probability Part 1 When other information is available (even when it is uninformative!), base rate information is ignored and people use representativeness (similarity) as the basis for probability judgment Why this happens Normative reasoning is cognitively demanding (WM!) Heuristics are usually “good enough”