1. Recapitulation
Causes for peoples’ tendency to detect spurious causes and contingencies:
Subjective theories.
Pattern recognition capabilities.
Belief in the law of small numbers.
Example: Hot hand in basketball.
Regression to the mean.
Incorrect weighting of contingency information.

2. 2.5 Erroneous weighting of information IV
Hamilton & Gifford (1976) Differential weighting of positive and negative information and the perception of minorities (39 descriptions).
Behavior
Group
Positive
Negative 
A (Majority) 18 8 26
B (Minority) 9 4 13

3. 2.5 Erroneous weighting of information IV
Hamilton & Gifford (1976):
Result: Despite the fact that the pro-portion of positive and negative char-acteristics was the same in both groups (18:8, 9:4), the minority group was judged as less positive.
Explanation: Majority group has more positive features (in absolute terms).

4. Attribution theory: Explaining behavior
In the following, biases of causal judg-ment concerning the own behavior as well as that of other people are investi-gated (Attribution theory).
Specifically, we ask whether peoples’ attributions suffer from the same problems as causal judgments in other domains: Overestimation of irrelevant causes and neglect of relevant ones.

5. ANOVA-Model I ANOVA model (Kelley, 1967)
Aim: Explaining peoples’ causal attributions.
Basic conception: People are regarded as lay-scientists that analyze the various sources of variance just like it is done in the Analysis of Variance (ANOVA).
Tools-to-theory heuristic: A well-known method is directly applied to the domain of cognition.

6. ANOVA-Model II ANOVA model (Kelley, 1967)
3 possible causes influencing behavior: person, object, and situation
3 sources of variance are analyzed:
Consensus: Variation due to person.
Distinctness: Variation due to object.
Consistency: Variation due to situation.
Example (Johns dancing performance): Consensus = low, Distinctness = low, Consistence = high  Person

7. ANOVA Model III ANOVA Model: Shortcomings:
Data driven model: Ignoring the impact of personal theories.
Ignoring motivational aspects.
Ignoring saliency or attentional effects.
Various sources of variance are weighted differently or are ignored all together.

8. ANOVA Model IV: Ignoring relevant influences
Example 1: Halo effect:
Concept Halo effect: Overestimation of the consistency of the characteristics of an object.
Examples:
Thorndike (1920).
Estimation of George Bush economic capabilities after 9/11 and in 2005.
Attractiveness and other features.
Institutions and publication success.
Explanation: Striving for consistency.

9. ANOVA Model IV: Ignoring relevant influences
Example 1: Ignoring Halo effect:
Indication of a Halo Effect: Characteristics (appearance, mannerisms, European accent) of »warm« teacher evaluated as attractive. Characteristics of »cold« teacher assessed as vexing.
People judged erroneously that the cha-racteristics influenced their judgment of sympathy (and not the other way round).

10. ANOVA Model IV: Ignoring relevant influences
Example 1: Ignoring Halo effect:
Explanation:
Characteristics of the person are salient. Thus, people regarded them as the basis (causes, reasons) of their evaluation of the person.
The more subtle Halo effect is ignored.

11. ANOVA Model IV: Ignoring relevant influences
Example 2: Ignoring Anchor effects:
The anchor presented as description of a person had an (ambiguous) effect on the prediction of participants’ behavior in ex-periments: Either in the same or in the op-posite direction with respect to the anchor.
People did not recognize the influence of the anchor.
Again: Anchor is not salient enough to be regarded as influential.

12. ANOVA Model IV: Ignoring relevant influences
Example 3: Ignoring Consensus information:
Concept: Concensus information: Information concerning variation due to persons: How strong varies the observed phenomenon with respect to different persons involved.
Consensus high: Different persons reveal a similar behavior.
Consequence: Personal traits are not the causes of observed phenomenon.

13. ANOVA Model IV: Ignoring relevant influences
Example 3: Ignoring Consensus information: Experiment:
People rated the values of 11 traits (like aggressive-ness, warmth) for 2 participants, who provided maximal shocks (about 400V) in den Milgram-Experiment.
Half of the participants were informed that about 65% of Milgram’s subjects applied the maximal shock.
Comment: Prior to the experiment Milgram asked Psychiatrist to rate the percentage of participants that would provide maximal shocks (Result: 1%).

14. ANOVA Model IV: Ignoring relevant influences
Example 3: Ignoring Consensus information: Experiment:
Results: The information about the per-centage of people giving maximal shocks (=consensus information) had no influence on the (quite negative) trait ratings.
Comment: Due to the fact that the high rate of people providing maximal shocks is quite surprising, one might expect that consensus information should exert an impact on participants judgments.

15. ANOVA Model V: Overestimating irrelevant influences
On the other hand, people overestimate irrelevant (but plausible) influences:
Example: Assessment of the effect of reassurance.
Participants should predict the strength of e-shocks they would subsequently accept.
Half of participants were give a reassurance that the shock would have no negative effects on health.
Result: The reassurance had no effect on participants’ prediction. However, participants thought that this was the case.

16. ANOVA Model VI: Conclusions
The ANOVA model overestimates peoples’ capability to identify the causes of their behavior.
Specifically, it does not take into account the biased evaluation of information due to saliency, subjective theories, etc.
The results also indicate that questionnaires should avoid questions concerning possible explanations of behavior.

17. The fundamental attribution error
The fundamental attribution error consists in the erroneous explanation of behavior by means of personal traits.
Participants received essays about a topic (Evaluation of Castro’s regime). Despite the fact that they were informed that authors were forced to take the position, revealed by the essays, they ascribed it to the personal view of the authors.
Comment: See also trait assessment of Milgram’s subjects.

18. Attributional asymmetry
Attributional asymmetry consists in the fact that actors tend to explain their behavior (mainly) by means of situational factors whereas observers refer (predominately) to personal traits.
Participants of Group A posed difficult questions to those of Group B (Who killed Phocas? What does the acronym LASER stand for?).
Result: Independent observers rated participants of group B as less well educated than those of A. Participants of A themselves did not assess their knowledge as superior.

19. Saliency needs itself explanation.
Critical Comments
Saliency needs itself explanation.
Cultural aspects of attentional and saliency effects: Chinese refer more to situational factors than US Americans.
Evolutionary roots of attentional and salience effects.

20. 2.6 Methodological Issues: Overview
Causal reasoning: methodology
Simpson’s paradox.
Ecological fallacy.
Regression to the mean and Lord’s paradox.

21. 2.6.1 Causal reasoning
Three important activities (in science and everyday life):
Diagnosis.
Prediction.
Causal explanation.
Problem of inferring causal relations:
Correlations and spurious effects
Confounding

22. 2.6.1 Causal reasoning
Common causes vs. Confounding:

23. 2.6.1 Causal reasoning Example of a hidden common cause:
Formation of families Building houses
Number of storks
Number of births

24. 2.6.1 Causal reasoning
Example of confounding: Full moon and car accidents:

25. 2.6.1 Causal reasoning
Common causes: Factor analytic model:

26. Causal reasoning: Method
Experimental method and causal reasoning: Claim:
Causal relations can be established only by by means of experiments (and not via observational studies).
This is due to the following characteristics of experiments:
Random assignment of units to experimental conditions.
Balancing / Parallelization.

27. Causal reasoning: Method
Example: placebo-controlled, double blind studies with random assign-ment of units to treatments.
Possible confounders: e.g. personal characte­ristics of the client
Treatment
Disease
Possible common causes:
e.g. expectations of the physician

28. Causal reasoning: Method
Question: Is it possible to assess the causal influence of a target cause on an outcome variable without doubt (e.g. by using sophisticated experimental methods)?
Answer: No!
Argument: It is not possible to exclude counfounders without doubt.

29. Causal reasoning: Method
Benedetti (2014): Studies investigating the effects of hidden applications of drugs (e.g. the benzodiacepine Valium) revealed that the »gold standard« is not sufficient to rule out the possibility that the treatment is ineffective since the treatment may enhance the placebo effect without exerting a direct positive effect on the disease.

30. Exercises:
Exercise 2-2