Learning from experience in nonlinear environments: Evidence from a competition scenario.

We test people's ability to learn to estimate a criterion (probability of success in a competition scenario) that requires aggregating information in a nonlinear manner. The learning environments faced by experimental participants are kind in that they are characterized by immediate, accurate feedback involving either naturalistic outcomes (information on winning and/or ranking) or the normatively correct probabilities. We find no evidence of learning from the former and modest learning from the latter, except that a group of participants endowed with a memory aid performed substantially better.

Automatic processes, emotions, and the causal field.

Newell & Shanks (N&S) provide a welcome examination of many claims about unconscious influences on decision making. I emphasize two issues that they do not consider fully: the roles of automatic processes and emotions. I further raise an important conceptual problem in assigning causes to potential unconscious influences.

Assessing the chances of success: naïve statistics versus kind experience.

Additive integration of information is ubiquitous in judgment and has been shown to be effective even when multiplicative rules of probability theory are prescribed. We explore the generality of these findings in the context of estimating probabilities of success in contests. We first define a normative model of these probabilities that takes account of relative skill levels in contests where only a limited number of entrants can win.

Sequentially simulated outcomes: kind experience versus nontransparent description.

Recently, researchers have investigated differences in decision making based on description and experience. We address the issue of when experience-based judgments of probability are more accurate than are those based on description. If description is well understood ("transparent") and experience is misleading ("wicked"), it is preferable to experience.

Determinants of linear judgment: a meta-analysis of lens model studies.

The mathematical representation of E. Brunswik's (1952) lens model has been used extensively to study human judgment and provides a unique opportunity to conduct a meta-analysis of studies that covers roughly 5 decades. Specifically, the authors analyzed statistics of the "lens model equation" (L.

What risks do people perceive in everyday life? A perspective gained from the experience sampling method (ESM).

The experience sampling method (ESM) was used to collect data from 74 part-time students who described and assessed the risks involved in their current activities when interrupted at random moments by text messages. The major categories of perceived risk were short term in nature and involved "loss of time or materials" related to work and "physical damage" (e.g.

Heuristic and linear models of judgment: matching rules and environments.

Much research has highlighted incoherent implications of judgmental heuristics, yet other findings have demonstrated high correspondence between predictions and outcomes. At the same time, judgment has been well modeled in the form of as if linear models. Accepting the probabilistic nature of the environment, the authors use statistical tools to model how the performance of heuristic rules varies as a function of environmental characteristics.