1. Exercise 2-7: Regression artefact: Lord’s paradox

2. Recapitulation I: Memory Judgments
Judging of stability and change:
Assessment of former attitudes is influenced by actual attitudes.
Biased assessments of previous states (capabilities, pain) in order to explain «effect» of treatments.
Hindsight bias: Biased retrospective evaluation of previous knowledge: «I knew it all along!»

3. Recapitulation II: Memory Judgments
Basic Mechanism: Anchoring and adjustment: Actual state serves as an anchor that is adjusted due to subjective theories.
Open-mindedness as an important aspect of critical thinking: Taking different perspectives.

4. Recapitulation III: Retrospective evaluation of episodes
Central Result: A negative event of greater duration is preferred to a shorter negative event that is part of the longer event.
Experiences and memory of experiences can differ.
Snapshot model.

5. Recapitulation IV: Heuristics and Biases Program
Availability Heuristic: The frequency of events is judged due to the easiness of how particluar instances can be generated (or come to mind).
Examples:
Memory and availability: Famous people
Death rates.
Influence of imagination.

6. Availability heuristic: Personal experience and exampels
Ex. 4-6: Influence of personal experiences and examples.
Central lession to be learned: Beware of arguments based on examples.

7. Probability Judgments: Representativeness Heuristic I
Functioning: Assessment of the frequency of events according to similarity.
Example: Evaluation of the probability of random sequences

8. Probability Judgments: Representativeness Heuristic II
Example: Linda-Problem:
Linda is 31 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 anti-nuclear demonstrations.
Ranking
Statement
(5.2)
Linda is a teacher in elementary school.
(3.3)
Linda works in a bookstore and takes Yoga clas­ses.
(2.1)
Linda is active in the feminist movement. (F)
(3.1)
Linda is a psychiatric social worker.
(5.4)
Linda is a member of the League of Women Vo­ters.
(6.2)
Linda is a bank teller. (B)
(6.4)
Linda is an insurance salesperson.
(4.1)
Linda is a bank teller and is active in the feminist movement. (BF)

9. Probability Judgments: Representativeness Heuristic III
Example: Political predictions:
Rank
Statement
(1.5)
Reagan will cut federal support to local govern­ment. (B)
(3.3)
Reagan will provide support for unwed mothers (A)
(2.7)
Reagan will increase the defense budget by less than 5%.
(2.9)
Reagan will provide federal support for unwed mo­thers and cut federal support for local government. (AB)

10. Probability Judgments: Representativeness Heuristic IV
Conclusion (Basic lession):
Beware of detailed internally coherent and plausible scenarios (those concerning the future as well as those concerning the past).
More detailed scenarios appear as more plausible. However more detailed scenarios are less probable since each added de­tail reduces the probability of the scenario.

11. Probability Judgments: Probability Matching
Basic phenomenon:
Peoples’ answers reflect probabilities
70%
30%

12. Probability Judgments: Probability Matching
Non-optimality of PM:
With PM 58% correct, with optimal strategy: 70% correct.
Outcome
Participant’s prediction 
»Red light«
»Green light«
Red light
0.49
0.21
0.70
Green light
0.09
0.30

13. Probability Judgments: Probability Matching (PM)
Humans and animals:
Rats and students
Animals: birds
Animals: ducks
Individual differences:
Intelligence and PM
Gender differences

14. Probability Judgments: Probability Matching
Explanation of PM: Greed as a possible explanation: Trying to get reward from both sources.
Rationality and PM.

15. Probability Judgments: Conditional probabilities (CP)
Conception
CP and stochastic independence of A and B:

16. Probability Judgments: Conditional probabilities (CP)
Asymmetry of CPs :

17. Probability Judgments: Conditional probabilities (CP)
CP and Causal Reasoning:
Preference for causal to diagnostic reasoning contradicts the principle:
Example 4-14:

18. Probability Judgments: Conditional probabilities (CP)
Non-monotonic CP:
New facts can completely reorder probabilities:
Yet
and

19. Probability Judgments: Conditional probabilities (CP)
Non-monotonic CP:
Example Simpson paradox:
yet,
and

20. Probability Judgments: Base rate neglect
Ignoring base rates
What is base rate information?
Example: Base rate neglect
Causal base rates