The use of probabilistic information in making predictions

Overconfidence in Projecting Uncertain Spatial Trajectories

“…However, given the repetitive nature of the trials and that feedback was provided on a trial-by-trial basis (in Phase 1), it is possible that all conditions fall under the domain of "good" decision making and the differences between conditions are due to the use of more confident System 1 invoked in direction extrapolations and more cautious System 2 thinking when acceleration is present (and it is no longer possible to rely on the System 1 perceptual extrapolation). Such a difference between the cognitive demands of estimating means versus variance has been argued by Pitz (1980) and has been shown in performance by Obrecht et al (2007).…”

“…This distinction between the two elements of prediction is quite analogous to the distinction or understanding of the mean based on several samples, a task at which people are quite good (Peterson & Beach, 1967;Pitz, 1980, Wickens et al, 2013, versus assessing the variance of those samples, a task at which people are not so proficient and at which people exhibit systematic biases (Mannes & Moore, 2013). In particular, in other contexts it has been observed that people tend to underestimate the variance of multiple samples, as if consistently underestimating the contribution of additional factors to variables in the world (Henrion & Fischhoff, 2002;Kahneman, 2011;Tversky & Kahneman, 1971).…”

Calculate or wait: Is man an eager or a lazy intuitive statistician?

“…Brown and Siegler (1993), for example, emphasised the importance of both metric (e.g., mean, variance and distribution) and mapping (ordinal relations within the domain) properties of a quantity for judgements about the quantity. Whereas research on heuristics (e.g., Gilovich et al, 2002), multiple-cue judgements von Helverson & Rieskamp, 2008) and function learning (DeLosh et al, 1997;Kalish et al, 2004) has been concerned with the influence of mapping knowledge on judgements, much less attention has been given to the influence of metric knowledge (but see, Pitz, Leung, Hamilos, & Terpening, 1976). Despite this, within several research areas it is generally assumed, often implicitly, that people are influenced by knowledge of distributional shape.…”

Induction of category distributions: A framework for classification learning.

“…For example, a binary feature dimension can be described by a Bernoulli process with its single parameter, p. Presumably there are limits on the types of distributions that people can veridically represent parametrically, and on those that may be learned by instance storage. Some research on the acquisition of nonnormal distributions has been done in studies of decision making (Pitz, Leung, Hamilos, & Terpening, 1976) and of category learning (Flannagan, Fried, & Holyoak, 1981;Neumann, 1977), but more work on this issue is clearly called for.…”