What's in a sample? Epistemic uncertainty and metacognitive awareness in risk taking.

In a fundamentally uncertain world, sound information processing is a prerequisite for effective behavior. Given that information processing is subject to inevitable cognitive imprecision, decision makers should adapt to this imprecision and to the resulting epistemic uncertainty when taking risks. We tested this metacognitive ability in two experiments in which participants estimated the expected value of different number distributions from sequential samples and then bet on their own estimation accuracy. Results show that estimates were imprecise, and this imprecision increased with higher distributional standard deviations. Importantly, participants adapted their risk-taking behavior to this imprecision and hence deviated from the predictions of Bayesian models of uncertainty that assume perfect integration of information. To explain these results, we developed a computational model that combines Bayesian updating with a metacognitive awareness of cognitive imprecision in the integration of information. Modeling results were robust to the inclusion of an empirical measure of participants' perceived variability. In sum, we show that cognitive imprecision is crucial to understanding risk taking in decisions from experience. The results further demonstrate the importance of metacognitive awareness as a cognitive building block for adaptive behavior under (partial) uncertainty.

Structural parameter interdependencies in computational models of cognition.

Computational modeling of cognition allows latent psychological variables to be measured by means of adjustable model parameters. The estimation and interpretation of the parameters are impaired, however, if parameters are strongly intercorrelated within the model. We point out that strong parameter interdependencies are especially likely to emerge in models that combine a subjective value function with a probabilistic choice rule-a common structure in the literature. We trace structural parameter interdependencies between value function and choice rule parameters across several prominent computational models, including models on risky choice (cumulative prospect theory), categorization (the generalized context model), and memory (the SIMPLE model of free recall). Using simulation studies with a generic choice model, we show that the accuracy in parameter estimation is hampered in the presence of high parameter intercorrelations, particularly the ability to detect group differences on the parameters and associations of the parameters with external variables. We demonstrate that these problems can be alleviated by using a different specification of stochasticity in the model, for example, by assuming parameter stochasticity or a constant error term. In addition, application to two empirical data sets of risky choice shows that alleviating parameter interdependencies in this way can lead to different conclusions about the estimated parameters. Our analyses highlight a common but often neglected problem of computational models of cognition and identify ways in which the design and application of such models can be improved. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

How does the peak-end rule smell? Tracing hedonic experience with odours.

The peak-end rule predicts that retrospective evaluations of affective events heavily depend on their most intense and last moment and imply duration neglect. It was originally proposed for negative experiences such as painful medical procedures. It is unclear, however, to what degree it also applies to positive experiences. Previously, rigorous comparisons between the two domains were limited due to the use of qualitatively different stimuli. Hence, it is not clear if the peak-end rule holds for short positive and negative experiences alike. To address these questions in a genuinely emotional domain, we conducted two experiments (n = 48 each) in which we used odours as stimuli. Participants repeatedly evaluated continuous odour sequences delivered into their noses via an olfactometer. The sequences differed in valence (positive vs. negative), length (36 vs. 72 s), and trajectory (increasing, decreasing, U-shaped, and inverse U-shaped). Results provide evidence for the peak-end rule for both positive and negative experiences alike. Results further show an overweighting of intense negative experiences for sequences that contain both pleasant and unpleasant episodes but provide little evidence for an effect of the trajectory manipulation.

Accuracy of food preference predictions in couples.

The goal of this study was to identify and empirically test variables that indicate how well partners in relationships know each other's food preferences. Participants (n = 2,854) lived in the same household and were part of a large, nationally representative panel study in Germany. Each partner independently predicted the other's preferences for several common food items. Results show that predictive accuracy was higher for likes and for extreme and stereotypical preferences as compared to dislikes and for moderate and idiosyncratic preferences. Accuracy was also higher for couples with a high similarity in preferences and with longer relationship duration but was independent of participants' age after controlling for relationship duration. The data also show that relationship duration was accompanied by higher similarity in couples' food preferences. There was a small positive correlation between partner knowledge and both partner similarity and satisfaction with family life, but no correlation between partner knowledge and general life satisfaction. The results reconcile both valence and base-rate accounts of preference prediction accuracy.

Empirical comparison of the adjustable spanner and the adaptive toolbox models of choice.

Past research indicates that individuals respond adaptively to contextual factors in multiattribute choice tasks. Yet it remains unclear how this adaptation is cognitively governed. In this article, empirically testable implementations of two prominent competing theoretical frameworks are developed and compared across two multiattribute choice experiments: the adaptive toolbox framework assuming discrete choice strategies and the adjustable spanner framework assuming one comprehensive adaptive strategy. Results from two experiments indicate that in the environments we tested, in which all cue information was presented openly, the toolbox makes better predictions than the adjustable spanner both in- and out-of-sample. Follow-up simulation studies indicate that it is difficult to discriminate the models based on choice outcomes alone but allowed the identification of a small subset of cases where the predictions of both models diverged. Our results suggest that people adapt their decision strategies by flexibly switching between using as little information as possible and use of all of the available information. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Taxing cognitive capacities reduces choice consistency rather than preference: A model-based test.

How do people make preferential choices in situations where their cognitive capacities are limited? Many studies link the manipulation of cognitive resources to qualitative changes in preferences. However, there is a widely overlooked alternative hypothesis, namely, that a reduction in cognitive capacities leads to an increase in choice inconsistency. We developed a mathematical model and followed a hierarchical Bayesian estimation approach to test to what extent a reduction in cognitive capacities leads to a shift in preference or an increase in choice inconsistency. Using a within-subject n-back task to manipulate cognitive load, we conducted three experiments across different choice domains: risky choice, temporal discounting, and strategic interaction. Across all three domains, results show that a reduction in cognitive capacities predominantly affected participants' level of choice consistency rather than their respective preference. These results hold on an individual and a group level. In sum, our approach and the mathematical model we used provide a rigorous and general test of how reduced cognitive capacities affect people's decision-making. (PsycINFO Database Record

Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach.

People often indicate a higher price for an object when they own it (i.e., as sellers) than when they do not (i.e., as buyers)-a phenomenon known as the endowment effect. We develop a cognitive modeling approach to formalize, disentangle, and compare alternative psychological accounts (e.g., loss aversion, loss attention, strategic misrepresentation) of such buyer-seller differences in pricing decisions of monetary lotteries. To also be able to test possible buyer-seller differences in memory and learning, we study pricing decisions from experience, obtained with the sampling paradigm, where people learn about a lottery's payoff distribution from sequential sampling. We first formalize different accounts as models within three computational frameworks (reinforcement learning, instance-based learning theory, and cumulative prospect theory), and then fit the models to empirical selling and buying prices. In Study 1 (a reanalysis of published data with hypothetical decisions), models assuming buyer-seller differences in response bias (implementing a strategic-misrepresentation account) performed best; models assuming buyer-seller differences in choice sensitivity or memory (implementing a loss-attention account) generally fared worst. In a new experiment involving incentivized decisions (Study 2), models assuming buyer-seller differences in both outcome sensitivity (as proposed by a loss-aversion account) and response bias performed best. In both Study 1 and 2, the models implemented in cumulative prospect theory performed best. Model recovery studies validated our cognitive modeling approach, showing that the models can be distinguished rather well. In summary, our analysis supports a loss-aversion account of the endowment effect, but also reveals a substantial contribution of simple response bias.

Psychophysiological arousal and inter- and intraindividual differences in risk-sensitive decision making.

The current study assessed peripheral responses during decision making under explicit risk, and tested whether intraindividual variability in choice behavior can be explained by fluctuations in peripheral arousal. Electrodermal activity (EDA) and heart rate (HR) were monitored in healthy volunteers (N = 68) during the Roulette Betting Task. In this task, participants were presented with risky gambles to bet on, with the chances of winning varying across trials. Hierarchical Bayesian analyses demonstrated that EDA and HR acceleration responses during the decision phase were sensitive to the chances of winning. Interindividual differences in this peripheral reactivity during risky decision making were related to trait sensitivity to punishment and trait sensitivity to reward. Moreover, trial-by-trial variation in EDA and HR acceleration responses predicted a small portion of intraindividual variability in betting choices. Our results show that psychophysiological responses are sensitive to explicit risk and can help explain intraindividual heterogeneity in choice behavior.

Betting on Illusory Patterns: Probability Matching in Habitual Gamblers.

Why do people gamble? A large body of research suggests that cognitive distortions play an important role in pathological gambling. Many of these distortions are specific cases of a more general misperception of randomness, specifically of an illusory perception of patterns in random sequences. In this article, we provide further evidence for the assumption that gamblers are particularly prone to perceiving illusory patterns. In particular, we compared habitual gamblers to a matched sample of community members with regard to how much they exhibit the choice anomaly 'probability matching'. Probability matching describes the tendency to match response proportions to outcome probabilities when predicting binary outcomes. It leads to a lower expected accuracy than the maximizing strategy of predicting the most likely event on each trial. Previous research has shown that an illusory perception of patterns in random sequences fuels probability matching. So does impulsivity, which is also reported to be higher in gamblers. We therefore hypothesized that gamblers will exhibit more probability matching than non-gamblers, which was confirmed in a controlled laboratory experiment. Additionally, gamblers scored much lower than community members on the cognitive reflection task, which indicates higher impulsivity. This difference could account for the difference in probability matching between the samples. These results suggest that gamblers are more willing to bet impulsively on perceived illusory patterns.

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.

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

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.

Selecting decision strategies: the differential role of affect.

Many theories on cognition assume that people adapt their decision strategies depending on the situation they face. To test if and how affect guides the selection of decision strategies, we conducted an online study (N = 166), where different mood states were induced through video clips. Results indicate that mood influenced the use of decision strategies. Negative mood, in particular anger, facilitated the use of non-compensatory strategies, whereas positive mood promoted compensatory decision rules. These results are in line with the idea that positive mood broadens the focus of attention and thus increases the use of compensatory decision strategies that take many pieces of information into account, whereas negative mood narrows the focus of attention and thus fosters non-compensatory strategies that rely on a selective use of information. The results further indicate that gaining a deeper theoretical understanding of the cognitive mechanisms that govern decision processes requires taking emotions into account.

Genetic influences on dietary variety - results from a twin study.

The heritability of variety seeking in the food domain was estimated from a large sample (N = 5,543) of middle age to elderly monozygotic and dizygotic twins from the "Virginia 30,000" twin study. Different dietary variety scores were calculated based on a semi-quantitative food choice questionnaire that assessed consumption frequencies and quantities for a list of 99 common foods. Results indicate that up to 30% of the observed variance in dietary variety was explained through heritable influences. Most of the differences between twins were due to environmental influences that are not shared between twins. Additional non-genetic analyses further revealed a weak relationship between dietary variety and particular demographic variables, including socioeconomic status, age, sex, religious faith, and the number of people living in the same household.

A hierarchical Bayesian model of the influence of run length on sequential predictions.

Two models of how people predict the next outcome in a sequence of binary events were developed and compared on the basis of gambling data from a lab experiment using hierarchical Bayesian techniques. The results from a student sample (N = 39) indicated that a model that considers run length ("drift model")-that is, how often the same event has previously occurred in a row-provided a better description of the data than did a stationary model taking only the immediately prior event into account. Both, expectation of negative and of positive recency was observed, and these tendencies mostly grew stronger with run length. For some individuals, however, the relationship was reversed, leading to a qualitative shift from expecting positive recency for short runs to expecting negative recency for long runs. Both patterns could be accounted for by the drift model but not the stationary model. The results highlight the importance of applying hierarchical analyses that provide both group- and individual-level estimates. Further extensions and applications of the approach in the context of the prediction literature are discussed.

Rigorously testing multialternative decision field theory against random utility models.

Cognitive models of decision making aim to explain the process underlying observed choices. Here, we test a sequential sampling model of decision making, multialternative decision field theory (MDFT; Roe, Busemeyer, & Townsend, 2001), on empirical grounds and compare it against 2 established random utility models of choice: the probit and the logit model. Using a within-subject experimental design, participants in 2 studies repeatedly choose among sets of options (consumer products) described on several attributes. The results of Study 1 showed that all models predicted participants' choices equally well. In Study 2, in which the choice sets were explicitly designed to distinguish the models, MDFT had an advantage in predicting the observed choices. Study 2 further revealed the occurrence of multiple context effects within single participants, indicating an interdependent evaluation of choice options and correlations between different context effects. In sum, the results indicate that sequential sampling models can provide relevant insights into the cognitive process underlying preferential choices and thus can lead to better choice predictions.

Testing adaptive toolbox models: a Bayesian hierarchical approach.

Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.

Constructing preference from experience: the endowment effect reflected in external information search.

People often attach a higher value to an object when they own it (i.e., as seller) compared with when they do not own it (i.e., as buyer)--a phenomenon known as the endowment effect. According to recent cognitive process accounts of the endowment effect, the effect is due to differences between sellers and buyers in information search. Whereas previous investigations have focused on search order and internal search processes (i.e., in memory), we used a sampling paradigm to examine differences in search termination in external search. We asked participants to indicate selling and buying prices for monetary lotteries in a within-subject design. In an experience condition, participants had to learn about the possible outcomes and probabilities of the lotteries by experiential sampling. As hypothesized, sellers tended to terminate search after sampling high outcomes, whereas buyers tended to terminate search after sampling low outcomes. These differences in stopping behavior translated into samples of the lotteries that were differentially distorted for sellers and buyers; the amount of the distortion was predictive of the resulting size of the endowment effect. In addition, for sellers search was more extended when high outcomes were rare compared with when low outcomes were rare. Our results add to the increasing evidence that the endowment effect is due, in part, to differences in predecisional information search.

Dining in the dark. The importance of visual cues for food consumption and satiety.

How important are visual cues for determining satiation? To find out, 64 participants were served lunch in a "dark" restaurant where they ate in complete darkness. Half the participants unknowingly received considerably larger "super-size" portions which subsequently led them to eat 36% more food. Despite this difference, participants' appetite for dessert and their subjective satiety were largely unaffected by how much they had consumed. Consistent with expectations, participants were also less accurate in estimating their actual consumption quantity than a control group who ate the same meal in the light.