Considers that intuitive predictions follow a judgmental heuristic-representativeness. By this heuristic, people predict the outcome that appears most representative of the evidence. Consequently, intuitive predictions are insensitive to the reliability of the evidence or to the prior probability of the outcome, in violation of the logic of statistical prediction. The hypothesis that people predict by representativeness was supported in a series of studies with both naive and sophisticated university students. The ranking of outcomes by likelihood coincided with the ranking by (...) representativeness, and Ss erroneously predicted rare events and extreme values if these happened to be representative. The experience of unjustified confidence in predictions and the prevalence of fallacious intuitions concerning statistical regression are traced to the representativeness heuristic. (shrink)

Human reasoning is often biased by stereotypical intuitions. The nature of such bias is not clear. Some authors claim that people are mere heuristic thinkers and are not aware that cued stereotypes might be inappropriate. Other authors claim that people always detect the conflict between their stereotypical thinking and normative reasoning, but simply fail to inhibit stereotypical thinking. Hence, it is unclear whether heuristic bias should be attributed to a lack of conflict detection or a failure of inhibition. We introduce (...) a neuroscientific approach that bears on this issue. Participants answered a classic decision-making problem while the activation of brain regions believed to be involved in conflict detection and response inhibition was monitored. Results showed that although the inhibition area was specifically activated when stereotypical responses were avoided, the conflict-detection area was activated even when people reasoned stereotypically. The findings suggest that people detect their bias when they give intuitive responses. (shrink)

Dual process theories conceive human thinking as an interplay between heuristic processes that operate automatically and analytic processes that demand cognitive effort. The interaction between these two types of processes is poorly understood. De Neys and Glumicic (2008) recently found that most of the time heuristic processes are successfully monitored. This monitoring, however, would not demand as many cognitive resources as the analytic thinking that is needed to solve reasoning problems. In the present study we tested the crucial assumption about (...) the effortless nature of the monitoring process directly. Participants solved base-rate neglect problems in which heuristic and analytic processes cued a conflicting response or not. Half of the participants reasoned under a secondary task load. A surprise recall task was used as an implicit measure of whether the participants detected the conflict in the problems. Results showed that, even under load, base-rate recall performance was better for conflict problems than for no-conflict problems. Although participants made more reasoning errors under load, recall of the conflict problems was not affected by the working memory load. These findings support the claim about the successful and undemanding nature of the conflict detection process during thinking. (shrink)

The phenomenon of base-rate neglect has elicited much debate. One arena of debate concerns how people make judgments under conditions of uncertainty. Another more controversial arena concerns human rationality. In this target article, we attempt to unpack the perspectives in the literature on both kinds of issues and evaluate their ability to explain existing data and their conceptual coherence. From this evaluation we conclude that the best account of the data should be framed in terms of a dual-process model of (...) judgment, which attributes base-rate neglect to associative judgment strategies that fail to adequately represent the set structure of the problem. Base-rate neglect is reduced when problems are presented in a format that affords accurate representation in terms of nested sets of individuals. (shrink)