Probabilistic Inferences Under Emotional Stress: How Arousal Affects Decision Processes

Many models of decision making neglect emotional states that could affect individuals' cognitive processes. The present work explores the effect of emotional stress on people's cognitive processes when making probabilistic inferences. Two contrasting hypotheses are tested against one another: the uncertainty‐reduction and attention‐narrowing hypotheses of how emotional stress affects decision making. In the experimental study, emotional stress was induced through the use of highly aversive pictures immediately before each decision. Emotional state was assessed by both subjective (state anxiety, arousal, and pleasantness ratings) and objective (skin conductance) measures. The results show that emotional stress impacts decision making; in particular, emotionally aroused participants seem to have focused on the most important information and selected simpler decision strategies relative to participants in a control condition. The results are in line with the attention‐narrowing hypothesis and suggest that emotional stress can impact decision making through limited predecisional information search and the selection of simpler decision strategies. Copyright © 2015 John Wiley & Sons, Ltd.     

Inferences under time pressure: how opportunity costs affect strategy selection

Do the inference strategies people select depend on the magnitude of time pressure? Is this dependency modified by the type of time pressure? These questions are addressed in three experimental studies in which participants made inferences after having searched for information on a computerized information board. In Study 1, time pressure was induced indirectly by imposing opportunity costs of being slow, a form of time pressure that is common in daily life but that has rarely been examined in the literature. A simple lexicographic heuristic (LEX) achieved the best fit in predicting participants' inferences. Studies 2 and 3 induced high time pressure either indirectly by imposing opportunity costs in terms of time or directly by limiting the time for each choice. Regardless of how time pressure was induced, under high time pressure the inferences could be best predicted with LEX, whereas under low time pressure a weighted linear model that integrates all available information predicted the inferences best. We conclude that people select strategies adaptively depending on characteristics of the situation.     

Empirical underidentification in estimating random utility models: The role of choice sets and standardizations

A standard approach to distinguishing people’s risk preferences is to estimate a random utility model using a power utility function to characterize the preferences and a logit function to capture choice consistency. We demonstrate that with often-used choice situations, this model suffers from empirical underidentification, meaning that parameters cannot be estimated precisely. With simulations of estimation accuracy and Kullback–Leibler divergence measures we examined factors that potentially mitigate this problem. First, using a choice set that guarantees a switch in the utility order between two risky gambles in the range of plausible values leads to higher estimation accuracy than randomly created choice sets or the purpose-built choice sets common in the literature. Second, parameter estimates are regularly correlated, which contributes to empirical underidentification. Examining standardizations of the utility scale, we show that they mitigate this correlation and additionally improve the estimation accuracy for choice consistency. Yet, they can have detrimental effects on the estimation accuracy of risk preference. Finally, we also show how repeated versus distinct choice sets and an increase in observations affect estimation accuracy. Together, these results should help researchers make informed design choices to estimate parameters in the random utility model more precisely.     

Inferences from memory: strategy- and exemplar-based judgment models compared

What are the cognitive processes underlying people's inferences from memory? To provide an answer, the exemplar-based approach to predicting people's inferences is tested against the strategy-based approach. Exemplar models assume that people make inferences about objects by retrieving similar objects from memory. In contrast, the strategy-based approach assumes that people select cognitive strategies that make inferences based on abstracted knowledge and information the inference situation provides. In Experiment 1, in which dichotomous feedback on the level of pair-comparisons was provided, almost all participants were classified as using a simple lexicographic strategy. In Experiment 2, in which continuous feedback for single objects was provided, most participants were classified as using a compensatory strategy. Both experiments suggest that the strategy-based approach is more suitable for predicting people's inferences from memory than the exemplar-based approach. The strategy-based approach shows how people adapt to inference situations by selecting different cognitive strategies.     

Cognitive models of choice: comparing decision field theory to the proportional difference model

People often face preferential decisions under risk. To further our understanding of the cognitive processes underlying these preferential choices, two prominent cognitive models, decision field theory (DFT; Busemeyer & Townsend, 1993 ) and the proportional difference model (PD; González-Vallejo, 2002 ), were rigorously tested against each other. In two consecutive experiments, the participants repeatedly had to choose between monetary gambles. The first experiment provided the reference to estimate the models' free parameters. From these estimations, new gamble pairs were generated for the second experiment such that the two models made maximally divergent predictions. In the first experiment, both models explained the data equally well. However, in the second generalization experiment, the participants' choices were much closer to the predictions of DFT. The results indicate that the stochastic process assumed by DFT, in which evidence in favor of or against each option accumulates over time, described people's choice behavior better than the trade-offs between proportional differences assumed by PD.     

A generalized distance function for preferential choices

Many cognitive theories of judgement and decision making assume that choice options are evaluated relative to other available options. The extent to which the preference for one option is influenced by other available options will often depend on how similar the options are to each other, where similarity is assumed to be a decreasing function of the distance between options. We examine how the distance between preferential options that are described on multiple attributes can be determined. Previous distance functions do not take into account that attributes differ in their subjective importance, are limited to two attributes, or neglect the preferential relationship between the options. To measure the distance between preferential options it is necessary to take the subjective preferences of the decision maker into account. Accordingly, the multi‐attribute space that defines the relationship between options can be stretched or shrunk relative to the attention or importance that a person gives to different attributes describing the options. Here, we propose a generalized distance function for preferential choices that takes subjective attribute importance into account and allows for individual differences according to such subjective preferences. Using a hands‐on example, we illustrate the application of the function and compare it to previous distance measures. We conclude with a discussion of the suitability and limitations of the proposed distance function.     

Comparing perceptual and preferential decision making

Perceptual and preferential decision making have been studied largely in isolation. Perceptual decisions are considered to be at a non–deliberative cognitive level and have an outside criterion that defines the quality of decisions. Preferential decisions are considered to be at a higher cognitive level and the quality of decisions depend on the decision maker’s subjective goals. Besides these crucial differences, both types of decisions also have in common that uncertain information about the choice situation has to be processed before a decision can be made. The present work aims to acknowledge the commonalities of both types of decision making to lay bare the crucial differences. For this aim we examine perceptual and preferential decisions with a novel choice paradigm that uses the identical stimulus material for both types of decisions. This paradigm allows us to model the decisions and response times of both types of decisions with the same sequential sampling model, the drift diffusion model. The results illustrate that the different incentive structure in both types of tasks changes people’s behavior so that they process information more efficiently and respond more cautiously in the perceptual as compared to the preferential task. These findings set out a perspective for further integration of perceptual and preferential decision making in a single ramework.     

How social and non-social information influence classification decisions: A computational modelling approach

Social information such as observing others can improve performance in decision making. In particular, social information has been shown to be useful when finding the best solution on one’s own is difficult, costly, or dangerous. However, past research suggests that when making decisions people do not always consider other people’s behaviour when it is at odds with their own experiences. Furthermore, the cognitive processes guiding the integration of social information with individual experiences are still under debate. Here, we conducted two experiments to test whether information about other persons’ behaviour influenced people’s decisions in a classification task. Furthermore, we examined how social information is integrated with individual learning experiences by testing different computational models. Our results show that social information had a small but reliable influence on people’s classifications. The best computational model suggests that in categorization people first make up their own mind based on the non-social information, which is then updated by the social information.