Numerous experiments have suggested that extreme outcomesare disproportionately influential when we make decisions in-volving risk, but there is less consensus on what it actuallymeans to be extreme. Existing accounts broadly fall into twocategories: those that suggest that the best and worst outcomesare uniquely influential and those that suggest that outcomesbecome more influential with increasing deviation from thecentre of the distribution. We conducted two experiments thataimed to tease apart these explanations. Although there wassome evidence that the distance from the centre influencesmemory, neither account was able to fully explain the choicesmade by participants. This finding has implications for the vi-ability of these explanations as well as for the generalisabilityof the effect and the interpretation of the method used to assessmemory.

Hierarchical Bayesian methods offer a principled and comprehensive way to relate psychological models to data. Here we use them to model the patterns of information search, stopping and deciding in a simulated binary comparison judgment task. The simulation involves 20 subjects making 100 forced choice comparisons about the relative magnitudes of two objects (which of two German cities has more inhabitants). Two worked-examples show how hierarchical models can be developed to account for and explain the diversity of both search and stopping rules seen across the simulated individuals. We discuss how the results provide insight into current debates in the literature on heuristic decision making and argue that they demonstrate the power and flexibility of hierarchical Bayesian methods in modeling human decision-making.

At the core of every decision-making task are two simple features; outcome values and probabilities. Over the past few decades, many models have developed from von Neumann’ and Morgenstern’s (1945) Expected Utility Theory to provide a thorough account of people’s subjective value and probability weighting functions. In particular, one such model that has been largely successful in both Psychology and Economics is Cumulative Prospect Theory (CPT; Tversky & Kahneman, 1992). While these models do fit people’s choice behavior well, few models have attempted to provide a psychological account for subjective value, probability weighting, and resulting choice behavior. In this paper, we focus on a memory confusion process as described in Hawkins et al.’s (2014) exemplar-based model for decisions from experience, the Exemplar Confusion (ExCon) model, and adapt it to account for biased probability estimates in decisions from description. Using Bayesian model selection techniques, we demonstrate that it is able to account for real choice data from a Rieskamp (2008) study using gains, losses, and mixed description-based gambles, and performs at least as well as CPT.

Utility based models are common in both the risky and intertemporal choice literatures. Recently there have been efforts to formulate models of choices which involve both risks and time delays. An important question then is whether the concept of utility is the same for risky and inter-temporal choices. We address this question by fitting versions of two popular utility based models, Cumulative Prospect Theory for risky choice, and Hyperbolic Discounting for inter-temporal choice, to data from three experiments which involved both choice types. The models were fit assuming either the same concept of utility for both, by way of a common value function, or different utilities with separate value functions. Our results show that while many participants seem to require the flexibility of different value functions, an approximately equal number do not suggesting they may have a single concept of utility. Furthermore for both choice types value functions were concave