This article described three heuristics that are employed in making judgements under uncertainty: representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value (...) is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty. (shrink)

The ArgumentIn this article I argue that brain images constructed with computerized tomography (CT) and positron emission tomography (PET) are part of a category of “expert images” and are both visually persuasive and also particularly difficult to interpret and understand by non-experts. Following the innovative judicial analogy of “demonstrative evidence” traced by Jennifer Mnookin (1998), I show how brain images are more than mere illustrations when they enter popular culture and courtrooms. Attending to the role of experts in producing data (...) in the form of images, in selecting extreme images for publication, and in testifying as to their relevance, I argue that there is an undue risk in courtrooms that brain images will not be seen as prejudiced, stylized representations of correlation, but rather as straightforward, objective photographs of, for example, madness. (shrink)

The thirty-five chapters in this book describe various judgmental heuristics and the biases they produce, not only in laboratory experiments but in important...