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

This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1111/psyp.12627The 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.This work was completed within the Behavioural and Clinical Neuroscience Institute, supported by a consortium award from the Medical Research Council and Wellcome Trust. The Centre for Gambling Research at UBC is supported by funding from the British Columbia Lottery Corporation and the Province of British Columbia government

The impact of distribution properties on sampling behavior

Objective: People often have their decisions influenced by rare outcomes, such as buying a lottery and believing they will win, or not buying a product because of a few negative reviews. Previous research has pointed out that this tendency is due to cognitive issues such as flaws in probability weighting. In this study we examine an alternative hypothesis: that people’s search behavior is biased by rare outcomes, and they can adjust the estimation of option value to be closer to the true mean, reflecting cognitive processes to adjust for sampling bias. Methods: We recruited 180 participants through Prolific to take part in an online shopping task. On each trial, participants saw a histogram with five bins, representing the percentage of one- to five-star ratings of previous customers on a product. They could click on each bin of the histogram to examine an individual review that gave that product the corresponding star; the review was represented using a number from 0–100 called the positivity score. The goal of the participants was to sample the bins so that they could get the closest estimate of the average positivity score as possible, and they were incentivized based on accuracy of estimation. We varied the shape of the histograms within subject and the number of samples they had between subjects to examine how rare outcomes in skewed distributions influenced sampling behavior and whether having more samples would help people adjust their estimation to be closer to the true mean. Results: Binomial tests confirmed sampling biases toward rare outcomes. Compared with 1% expected under unbiased sampling, participants allocated 11% and 12% of samples to the rarest outcome bin in the negatively and positively skewed conditions, respectively (ps < 0.001). A Bayesian linear mixed- effects analysis examined the effect of skewness and samples on estimation adjustment, defined as the difference between experienced /observed means and participants’ estimates. In the negatively skewed distribution, estimates were on average 7% closer to the true mean compared with the observed means (10-sample ∆ = −0.07, 95% CI [−0.08, −0.06]; 20-sample ∆ = −0.07, 95% CI [−0.08, −0.06]). In the positively skewed condition, estimates also moved closer to the true mean (10-sample ∆ = 0.02, 95% CI [0.01, 0.04]; 20-sample ∆ = 0.03, 95% CI [0.02, 0.04]). Still, participants’ estimates deviated from the true mean by about 9.3% on average, underscoring the persistent influence of sampling bias. Conclusion: These findings demonstrate how search biases systematically affect distributional judgments and how cognitive processes interact with biased sampling. The results have implications for human–algorithm interactions in areas such as e-commerce, social media, and politically sensitive decision-making contexts

Complexity aversion in risky choices and valuations: Moderators and possible causes

In the age of digitalization and globalization, an abundance of information is available, and our decision environments have become increasingly complex. However, it remains unclear under what circumstances complexity affects risk taking. In two experiments with monetary lotteries (one with a stratified national sample), we investigate behavioral effects and provide a cognitive explanation for the impact of complexity on risk taking. Results show that complexity, defined as the number of possible outcomes of a risky lottery, decreased the choice probability of an option but had a smaller and less consistent effect when evaluating lotteries independently. Importantly, choices of participants who spent more time looking at the complex option were less affected by complexity. A tendency to avoid cognitive effort can explain these effects, as the effort associated with evaluating the complex option can be sidestepped in choice tasks, but less so in valuation tasks. Further, the effect of complexity on valuations was influenced by individual differences in cognitive ability, such that people with higher cognitive ability showed less complexity aversion. Together, the results show that the impact of complexity on risk taking depends on both, decision format and individual differences and we discuss cognitive processes that could give rise to these effects

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. [Abstract copyright: Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.

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

Change and status quo in decisions with defaults: The effect of incidental emotions depends on the type of default

Affective states can change how people react to measures aimed at influencing their decisions such as providing a default option. Previous research has shown that when defaults maintain the status quo positive mood increases reliance on the default and negative mood decreases it. Similarly, it has been demonstrated that positive mood enhances the preference for inaction. We extend this research by investigating how mood states influence reliance on the default if the default leads to a change, thus pitting preference for status quo against a preference for inaction. Specifically, we tested in an online study how happiness and sadness influenced reliance on two types of default (1) a default maintaining status quo and (2) a default inducing change. Our results suggest that the effect of emotions depends on the type of default: people in a happy mood were more likely than sad people to follow a default when it maintained status quo but less likely to follow a default when it introduced change. These results are in line with mood maintenance theory

Valuation and estimation from experience

The processing of sequentially presented numerical information is a prerequisite for decisions from experience, where people learn about potential outcomes and their associated probabilities and then make choices between gambles. Little is known, however, about how people's preference for choosing a gamble is affected by how they perceive and process numerical information. To address this, we conducted a series of experiments wherein participants repeatedly sampled numbers from continuous outcome distributions. They were incentivized either to estimate the means of the numbers or to state their minimum selling prices to forgo a consequential draw from the distributions (i.e., the certainty equivalents or valuations). We found that participants valued distributions below their means, valued high-variance sequences lower than low-variance sequences, and valued left-skewed sequences lower than right-skewed sequences. Though less pronounced, similar patterns occurred in the mean estimation task where preferences should not play a role. These results are not consistent with prior findings in decision from experience such as the overweighting of high numbers and the underweighting of rare events. Rather, the qualitative effects, as well as the similarity of effects in valuation and estimation, are consistent with the assumption that people process numbers on a compressed mental number line in valuations from experience