The “false consensus” error

1. The “false consensus” error • Tendency to see one’s own attitudes as more common than they really are • Why? Example: Legalization of pot (marijuana): • Estimate of others’ attitudes: What percentage of students in this class are “pro” vs. “anti”? • Self: Is your own attitude “pro” or “anti”?

2. Actual distribution of attitudes in Spring 2004 class 50% 57% anti Favor of legalizing pot? 43% pro Perceived distribution of attitudes 60% pro 50% 56% anti 44% pro 40% anti Perceptions by pro participants Perceptions by anti participants

3. Probability estimates • Again, judgments are biased by what comes to mind first • Flying vs. driving • Lotteries • Wildly inflated perceived risk of vivid events (e.g. getting struck by lightning) • Guess which is more likely—earth getting hit by a “globally catastrophic” asteroid in the next hundred years, or you winning a typical national lottery? • The asteroid is much more likely—about 20 thousand times more likely • http://newton.dm.unipi.it/ • AIDS

4. Approximate Statistics Participants’ estimates Risk that woman will contract AIDS with one heterosexual contact with HIV positive male, no condom .2% (1/500) 50% BUT: Why important to wear a condom: Helps prevent: unwanted pregnancy, spread of other STDs (e.g. herpes; 20-30% U.S. population, no cure at current time) 8% of AIDS cases in the United States have been attributed to heterosexual contact. .02% (1/5000) 5% w/condom

5. Controlled processing • Interplay between automatic and controlled processing: popular view • Automatic processes first, then control as potential “correction” • Similar to “Stroop” effect • ORANGE • BLUE • Diallo case • Key points: awareness, motivation and ability

6. Stimulus Automatic process triggered If no… Automatic processing likely to dominate Perceiver Aware of automatic process? If yes… If no… Motivated to “correct”? If yes… If no… Ability to “correct”? If yes… Potential for controlled processing

7. Automatic believing (acceptance) and controlled unbelieving Read assertion Washington University professor found guilty of drinking beer during class Understand assertion Largely automatic Colleagues of Dr. Lambert are “shocked, just shocked” at the news. Initial acceptance of assertion as true “Unaccept” if motivation and ability high controlled

8. Why this is important • Similar social psychological phenomena • Social perception and attribution • Self and social comparison • Applied issues • False/misleading advertising • Negative and misleading political campaigns • Legal issues • Exposure to pre-trial press/rumor • Juror instructions to disregard

9. Mental Correction and the self • Self-relevant information varies in quality/meaningfulness • Just because we receive it doesn’t mean we want to, or should, accept it • Motivated (enemies, bigots, subordinates) • Relevance (social comparisons) • Extenuating circumstances

10. Recent study by Hetts et al. on sarcasm • All participants perform poorly • 2 IVs • Type of feedback after performance • Feedback only • Feedback + additional positive sarcastic remark • Load vs. no load during feedback • DV: performance SE (after feedback)

11. Models of interplay • Correction/regulation – C can override A • Multi-tasking – A & C may operate in parallel • Delegation – C may initiate A • Orienting – A may initiate C • Automatization – C can be transformed into A • Disruption - A can be transformed into C • Intrusion – A may interfere with C

12. Mental Undoing:Counterfactual thinking • Generating alternative accounts of reality • If X hadn’t happened, then….. • If X had happened, then…. • Implications for emotional reactions • Near-misses elicit more extreme reactions • E.g. grades • Medvec, Madey, and Gilovich (1995)