Hockenbury Discovering Psychology 5th txtbk

286 CHAPTER 7 Thinking, Language, and Intelligenceavailability heuristicA strategy in which the likelihood of anevent is estimated on the basis of howreadily available other instances of the eventare in memory.representativeness heuristicA strategy in which the likelihood of anevent is estimated by comparing howsimilar it is to the prototype of the event.languageA system for combining arbitrary symbols toproduce an infinite number of meaningfulstatements.Vivid Images and the Availability HeuristicAfter the terrorist attacks of September 11,2001, vivid scenes of the devastation atthe Pentagon in Washington, D.C., and atthe site of the World Trade Center werehighly publicized. Fears of another terroristhijacking caused sales of airline tickets toplunge, and many Americans turned toautomobiles for long-distance travel. Butas the number of miles driven on interstatehighways surged, so did traffic deaths. Infact, there were 353 more traffic deathsduring the last three months of 2001 thanthere were for the same months during theprevious three years. As German psychologistGerd Gigerenzer (2004) points out,“The number of Americans who lost theirlives on the road by avoiding the risk of flyingwas higher than the total number ofpassengers—266—killed on the four fatalflights.” How does the availability heuristicexplain the fact that so many people areunwilling to fly on a commercial airlinerafter highly publicized plane crashes?Decisions Involving UncertaintyEstimating the Probability of EventsSome decisions involve a high degree of uncertainty. In these cases, you need tomake a decision, but you are unable to predict with certainty that a given event willoccur. Instead, you have to estimate the probability of an event occurring. But howdo you actually make that estimation?For example, imagine that you’re running late for a very important appointment.You may be faced with this decision: “Should I risk a speeding ticket to get to theappointment on time?” In this case, you would have to estimate the probability ofa particular event occurring—getting pulled over for speeding.In such instances, we often estimate the likelihood that certain events will occur,then gamble. In deciding what the odds are that a particular gamble will go our way,we tend to rely on two rule-of-thumb strategies to help us estimate the likelihoodof events: the availability heuristic and the representativeness heuristic (Tversky &Kahneman, 1982; Kahneman, 2003).The Availability HeuristicWhen we use the availability heuristic,we estimate the likelihood of an event onthe basis of how readily available otherinstances of the event are in our memory.When instances of an event are easily recalled,we tend to consider the event asbeing more likely to occur. So, we’re lesslikely to exceed the speed limit if we canreadily recall that a friend recently got aspeeding ticket.However, when a rare event makes avivid impression on us, we may overestimateits likelihood (Tversky & Kahneman,1982). State lottery commissionscapitalize on this cognitive tendency byrunning many TV commercials showingthat lucky person who won the $100million Powerball. A vivid memory iscreated, which leads viewers to an inaccurateestimate of the likelihood that theevent will happen to them.The key point here is that the less accurately our memory of an event reflects theactual frequency of the event, the less accurate our estimate of the event’s likelihoodwill be. That’s why the lottery commercials don’t show the other 50 million peoplestaring dejectedly at their TV screens because they did not win the $100 million.The Representativeness HeuristicThe other heuristic we often use to make estimates is called the representativenessheuristic (Kahneman & Tversky, 1982; Kahneman, 2003). Here, we estimate anevent’s likelihood by comparing how similar its essential features are to our prototypeof the event. Remember, a prototype is the most typical example of an objector an event.To go back to our example of deciding whether to speed, we are more likely torisk speeding if we think that we’re somehow significantly different from the prototypeof the driver who gets a speeding ticket. If our prototype of a speeder is ateenager driving a flashy, high-performance car, and we’re an adult driving a minivanwith a baby seat, then we will probably estimate the likelihood of our getting aspeeding ticket as low.