In the behavioral sciences, a popular approach to describe and predict behavior is cognitive modeling with adjustable parameters (i.e., which can be fitted to data). Modeling with adjustable parameters allows, among other things, measuring differences between people. At the same time, parameter estimation also bears the risk of overfitting. Are individual differences as measured by model parameters stable enough to improve the ability to predict behavior as compared to modeling without adjustable parameters? We examined this issue in cumulative prospect theory (CPT), arguably the most widely used framework to model decisions under risk. Specifically, we examined (a) the temporal stability of CPT's parameters; and (b) how well different implementations of CPT, varying in the number of adjustable parameters, predict individual choice relative to models with no adjustable parameters (such as CPT with fixed parameters, expected value theory, and various heuristics). We presented participants with risky choice problems and fitted CPT to each individual's choices in two separate sessions (which were 1 week apart). All parameters were correlated across time, in particular when using a simple implementation of CPT. CPT allowing for individual variability in parameter values predicted individual choice better than CPT with fixed parameters, expected value theory, and the heuristics. CPT's parameters thus seem to pick up stable individual differences that need to be considered when predicting risky choice.

Cognitive Models of Risky Choice: Parameter Stability and predictive accuracy of prospect theory. Data for Glöckner & Pachur (2012)

To be useful, cognitive models with fitted parameters should show generalizability across time and allow accurate predictions of future observations. It has been proposed that hierarchical procedures yield better estimates of model parameters than do nonhierarchical, independent approaches, because the formers’ estimates for individuals within a group can mutually inform each other. Here, we examine Bayesian hierarchical approaches to evaluating model generalizability in the context of two prominent models of risky choice—cumulative prospect theory (Tversky & Kahneman, 1992) and the transfer-of-attention-exchange model (Birnbaum & Chavez, 1997). Using empirical data of risky choices collected for each individual at two time points, we compared the use of hierarchical versus independent, nonhierarchical Bayesian estimation techniques to assess two aspects of model generalizability: parameter stability (across time) and predictive accuracy. The relative performance of hierarchical versus independent estimation varied across the different measures of generalizability. The hierarchical approach improved parameter stability (in terms of a lower absolute discrepancy of parameter values across time) and predictive accuracy (in terms of deviance; i.e., likelihood). With respect to test–retest correlations and posterior predictive accuracy, however, the hierarchical approach did not outperform the independent approach. Further analyses suggested that this was due to strong correlations between some parameters within both models. Such intercorrelations make it difficult to identify and interpret single parameters and can induce high degrees of shrinkage in hierarchical models. Similar findings may also occur in the context of other cognitive models of choice.

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Using Bayesian hierarchical parameter estimation to assess the generalizability of cognitive models of choice

a b s t r a c t There is evidence for reliable individual differences in the tendency to use an intuitive (i.e., sponta- neous, affect-based) and a deliberative (i.e., effortful, planned, and analytic) decision mode. Even though other individual characteristics in decision making (e.g., risk attitude) seem to be domain-specific, it is commonly assumed that a person's decision style is relatively stable across decision domains. Using a domain-specific extension of the Unified Scale to Assess Individual Differences in Intuition and Delibera- tion (USID), we found that preference for intuition and preference for deliberation showed considerable variability across domains (e.g., choosing a dress vs. choosing a doctor). In addition, domain-specific pre- ferences for intuition were consistently correlated with self-rated expertise in making decisions in the respective domain. Our results indicate that a person's domain-general decision style does not necessar- ily generalize across decision domains, and that the domain-specificity of preferences for intuition seems to be driven partly by differences in expertise.

https://pure.mpg.de/pubman/item/item_2214191_5/component/file_2214663/id10000041.pdf

Domain-specific preferences for intuition and deliberation in decision making

People's choices between prospects with relatively affect-rich outcomes (e.g., medical side effects) can diverge markedly from their choices between prospects with relatively affect-poor outcomes (e.g., monetary losses). We investigate the cognitive mechanisms underlying this 'affect gap' in risky choice. One possibility is that affect-rich prospects give rise to more distortion in probability weighting. Another is that they lead to the neglect of probabilities. To pit these two possibilities against each other, we fitted cumulative prospect theory (CPT) to the choices of individual participants, separately for choices between options with affect-rich outcomes (adverse medical side effects) and options with affect-poor outcomes (monetary losses); additionally, we tested a simple model of probability neglect, the minimax rule. The results indicated a qualitative difference in cognitive mechanisms between the affect-rich and affect-poor problems. Specifically, in affect-poor problems, the large majority of participants were best described by CPT; in affect-rich problems, the proportion of participants best described by the minimax rule was substantially higher. The affect gap persisted even when affect-rich outcomes were supplemented by numerical information, thus providing no support for the thesis that choices in affect-rich and affect-poor problems diverge because the information provided in the former is nonnumerical. Our findings suggest that the traditional expectation-based framework for modeling risky decision making may not readily generalize to affect-rich choices. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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How Affect Shapes Risky Choice: Distorted Probability Weighting Versus Probability Neglect

Objective. Decisions about cancer screenings often involve the consideration of complex and counterintuitive evidence. We investigated psychological factors that promote the comprehension of benefi...

Understanding the harms and benefits of cancer screening: A model of factors that shape informed decision making

It has been suggested that people decide differently when faced with affect-rich prospects (e.g., medical side effects) than with prospects triggering more moderate amounts of affect (e.g., monetary losses). Does this potential impact of affect on risky decision making even result in preference reversals? And if so, how do the cognitive processes underlying the respective decisions differ? Using a within-subjects design, the current research contrasted choices between prospects with relatively affect-rich outcomes and choices between prospects with relatively affect-poor but monetarily equivalent outcomes. Across three studies, findings consistently showed a substantial divergence in participants’ affect-rich and affectpoor choices, resulting in systematic within-subject preference reversals. This “affect gap” held for outcomes associated both with negative affect (Studies 1 and 3) and with positive affect (Study 2). Furthermore, computational modeling suggested that in affect-poor choice people commonly rely on a compensatory process that trades off outcome and probability, whereas in affect-rich choice (in particular between outcomes invoking negative affect) people more often rely on a noncompensatory, heuristic process that compares outcomes between options while disregarding probabilities. This interpretation is also supported by process data (Study 3) showing that people pay less attention to probability information and conduct more intradimensional comparisons in affect-rich choices than in affect-poor choices.

/pdf/the-affect-gap-in-risky-choice-affect-rich-outcomes-590qt5jc0s.pdf

Roland Wolkewitz

Papers

The affect gap in risky choice : Affect-rich outcomes attenuate attention to probability information

BRIEF REPORT The Affect Gap in Risky Choice: Affect-Rich Outcomes Attenuate Attention to Probability Information