China
Africa
Explore chapters and articles related to this topic
Driver’s Mental Model of Vehicle Automation
Published in Donald L. Fisher, William J. Horrey, John D. Lee, Michael A. Regan, Handbook of Human Factors for Automated, Connected, and Intelligent Vehicles, 2020
Consequently, under uncertainty or under time pressure, mental models are subject to cognitive biases—systematic patterns of deviation from norm or rationality in judgment (Haselton, Nettle, & Andrews, 2005). Although there are a large variety of cognitive biases, examples of important biases affecting mental models of automation include Confirmation bias—the tendency to search for, interpret, focus on, and remember information in a way that confirms one’s preconceptions (Oswald & Grosjean, 2004).Anchoring bias—the tendency to rely too heavily, or “anchor,” on one trait or piece of information when making decisions (Zhang, Lewis, Pellon, & Coleman, 2007).Overconfidence bias—excessive confidence in one’s own answers to questions (Hilbert, 2012).Gambler’s fallacy—the mistaken belief that if something happens more frequently than normal during a given period, it will happen less frequently in the future (or vice versa) (Tversky & Kahneman, 1974).
Solving Problems and Making Decisions
Published in Robert W. Proctor, Van Zandt Trisha, Human Factors in Simple and Complex Systems, 2018
Robert W. Proctor, Van Zandt Trisha
Representativeness is closely related to the gambler’s fallacy, which is the belief that a continuing run of one of two or more possible events is increasingly likely to be followed by an occurrence of the other event. For example, suppose the births in the BGBBBB sequence above were presented sequentially to a person who made a probability judgment after each birth that the next birth would be a girl. The predicted probability that the subsequent birth would be a girl tends to become larger through the run of four boys, even though the probability is always 50%. The gambler’s fallacy occurs because people fail to treat the occurrence of random events in a sequence as independent; that is, that having a boy does not change the future probability of having a girl.
The technical and scientific stuff
Published in Ian Long, Simplicity in Safety Investigations, 2017
“I’ve done this task a thousand times and nothing has ever happened before. I don’t understand why it went so wrong this time” is a common response to an incident. “We have gone 300 days without a recordable injury. Surely we can easily get to a year injury free. Then we can really celebrate.” Both of these statements are classic examples of the Gambler’s Fallacy. The Gambler’s Fallacy is described as the tendency to think that the future likelihood of an outcome is impacted by past events, when in reality it is not. During an investigation, ask those involved about how they assessed the risk of some task going right or going wrong. If they look back in time and say that it has always gone well when we did it that way, this should pique your interest in exploring whether the Gambler’s Fallacy is playing a part in the decision-making process.
Students’ intuitively-based (mis)conceptions in probability and teachers’ awareness of them: the case of heuristics
Published in International Journal of Mathematical Education in Science and Technology, 2022
Ayhan Kursat Erbas, Mehmet Fatih Ocal
Positive and negative recency effects appear when people confuse the relationship between laws of small and large numbers and conclude that the experimental probability for an event has a limit to the theoretical one (Stohl, 2005). For example, people may judge that the subsequent outcome in a coin-tossing experiment would most likely be heads after observing four heads in a row. This is called the positive recency effect. Likewise, people may judge that the following outcome would be tails because they think the experiment has corrective power, and there should be the same number of heads and tails after several tosses. This is called the negative recency effect or the gambler’s fallacy (Tversky & Kahneman, 1971). Another example of the negative recency effect as a representativeness heuristic was reported by Tversky and Kahneman (1971) for the following problem: The mean IQ of the population of eighth graders in a city is known to be 100. You have selected a random sample of 50 children for a study of educational achievement. The first child tested has an IQ of 150. What do you expect the mean IQ to be for the whole sample? (p. 105)Even though the answer to this question is 101, the researchers found that most individuals answered 100 because they probably thought the sample mean should resemble that of the population. Nevertheless, the positive recency effect is the opposite of the gambler’s fallacy. For example, people may think that heads is more likely to come up after a run of heads in a coin-tossing experiment.