Toward a computational understanding of how reminiscing about positive autobiographical memories influences decision-making under risk

Recent computational psychiatric research has dissected decision-making under risk into different underlying cognitive computational constructs and identified disease-specific changes in these constructs. Studies are underway to investigate what kind of behavioral or psychological interventions can restore these cognitive, computational constructs. In our previous study, we showed that reminiscing about positive autobiographical memories reduced risk aversion and affected probability weighting in the opposite direction from that observed in psychiatric disorders. However, in that study, we compared positive versus neutral memory retrieval by using a within-subjects crossover posttest design. Therefore, the change of decision-making from baseline is unclear. Furthermore, we used a hypothetical decision-making task and did not include monetary incentives. We attempt to address these limitations and investigated how reminiscing about positive autobiographical memories influences decision-making under risk using a between-subjects pretest posttest comparison design with performance-contingent monetary incentives. In thirty-eight healthy, young adults, we found that reminiscing about positive memories reinforced the commonly observed inverted S-shaped nonlinear probability weighting (f = 0.345, medium to large in effect size). In contrast, reminiscing about positive memories did not affect risk aversion in general. Given that the change in probability weighting after reminiscing about positive memories is in the opposite direction from that observed in psychiatric disorders, our results indicate that positive autobiographical memory retrieval might be a useful behavioral intervention strategy for amending the altered decision-making under risk in psychiatric diseases.

Communicating risk: How relevant and irrelevant probabilistic information influences risk perception in medical decision-making

Patients rely on knowing potential risks before accepting medical treatments, but risk perception can be distorted by cognitive biases and irrelevant information. We examined the interactive effects of subjective processes, objective knowledge, and demographic characteristics on how individuals estimate risks when provided with relevant and irrelevant probabilistic information. Participants read medical scenarios describing potential adverse effects associated with declining and accepting preventative treatment, as well as the objective likelihood of experiencing adverse effects associated with one of these two courses of action. We found that the perceived negativity of outcomes influenced perceptions of risk regardless of whether relevant probabilities were available and that the use of affect heuristics to estimate risk increased with age. Introducing objective estimates ameliorated age-related increases in affective distortions. Sensitivity to relevant probabilities increased with greater perceived outcome severity and was greater for men than for women. We conclude that relevant objective information may reduce the propensity to conflate outcome severity with likelihood and that medical judgments of risk vary depending on exposure to relevant and irrelevant probabilities. Implications for how medical professionals should communicate risk information to patients are considered.

Attribute attention and option attention in risky choice

Probability weighting is one of the most powerful theoretical constructs in descriptive models of risky choice and constitutes a central component of cumulative prospect theory (CPT). Probability weighting has been shown to be related to two facets of attention allocation: one analysis showed that differences in the shape of CPT's probability-weighting function are linked to differences in how attention is allocated across attributes (i.e., probabilities vs. outcomes); another analysis (that used a different measure of attention) showed a link between probability weighting and differences in how attention is allocated across options. However, the relationship between these two links is unclear. We investigate to what extent attribute attention and option attention independently contribute to probability weighting. Reanalyzing data from a process-tracing study, we first demonstrate links between probability weighting and both attribute attention and option attention within the same data set and the same measure of attention. We then find that attribute attention and option attention are at best weakly related and have independent and distinct effects on probability weighting. Moreover, deviations from linear weighting mainly emerged when attribute attention or option attention were imbalanced. Our analyses enrich the understanding of the cognitive underpinnings of preferences and illustrate that similar probability-weighting patterns can be associated with very different attentional policies. This complicates an unambiguous psychological interpretation of psycho-economic functions. Our findings indicate that cognitive process models of decision making should aim to concurrently account for the effects of different facets of attention allocation on preference. In addition, we argue that the origins of biases in attribute attention and option attention need to be better understood.

Computational markers of experience- but not description-based decision-making are associated with future depressive symptoms in young adults

BACKGROUND

Early prediction of high depressive symptoms is crucial for selective intervention and the minimization of functional impairment. Recent cross-sectional studies indicated decision-making deficits in depression, which may be an important contributor to the disorder. Our goal was to test whether description- and experience-based decision making, two major neuroeconomic paradigms of decision-making under uncertainty, predict future depressive symptoms in young adults.

METHODS

One hundred young adults performed two decision-making tasks, one description-based, in which subjects chose between two gambling options given explicitly stated rewards and their probabilities, and the other experience-based, in which subjects were shown rewards but had to learn the probability of those rewards (or cue-outcome contingencies) via trial-and-error experience. We evaluated subjects' depressive symptoms with BDI-II at baseline (T1) and half a year later (T2).

RESULTS

Comparing subjects with low versus high levels of depressive symptoms at T2 showed that the latter performed worse on the experience- but not description-based task at T1. Computational modeling of the decision-making process suggested that subjects with high levels of depressive symptoms had a more concave utility function, indicating enhanced risk aversion. Furthermore, a more concave utility function at T1 increased the odds of high depressive symptoms at T2, even after controlling depressive symptoms at T1, perceived stress at T2, and several covariates (OR = 0.251, 95% CI [0.085, 0.741]).

CONCLUSIONS

This is the first study to demonstrate a prospective link between experience-based decision-making and depressive symptoms. Our results suggest that enhanced risk aversion in experience-based decision-making may be an important contributor to the development of depressive symptoms.

The prevention puzzle

Promoting prevention is an important goal of public policy. Fifty years ago, Ehrlich and Becker (J Polit Econ 80:623-648, 1972) proposed a simple model of prevention (or self-protection as they called it). Surprisingly enough, subsequent research, mainly within the expected utility paradigm, showed that it is hard to derive clear predictions within this simple model that can help to guide policy. This is what I refer to as the prevention puzzle: why is it so hard for economic theory to guide prevention decisions? In this article I try to shed light on this question. I review the existing literature and add some tentative new results under nonexpected utility. While the impact of risk aversion on prevention is complex, three factors seem to contribute unambiguously to underprevention: prudence, likelihood insensitivity, and loss aversion. I conclude by giving some ideas how empirical research may contribute to the understanding of prevention decisions and help to solve the prevention puzzle.

Willingness to pay for reductions in health risks under anticipated regret

In this paper, we study the willingness to pay for reductions in health risks within a framework of anticipated regret. We show that ex post information provision can be a relevant factor for regret theory to account for why people are sometimes so inclined to protect themself against certain types of health risks but not others. In particular, we find that under full resolution of uncertainty disproportionate aversion to large regrets exaggerates willingness to pay estimates. The effect induced by this notion of regret aversion can be interpreted as if regret-averse people overweight risk reductions due to prevention. However, as feedback over forgone acts is missing, the regret aversion effect disappears. Finally, we show that information avoidance induced by regret aversion can significantly bias our evaluation to prefer those health programs that completely eliminate a risk, i.e., the certainty effect.

Risk Taking with Left- and Right-Skewed Lotteries. J Risk Uncertain 2021;62:89-112. [PMID: 33967390 PMCID: PMC8087892 DOI: 10.1007/s11166-021-09345-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

While much literature has focused on preferences regarding risk, preferences over skewness also have significant economic implications. An important and understudied aspect of skewness preferences is how they affect risk taking. In this paper, we design a novel laboratory experiment that elicits certainty equivalents over lotteries where the variance and skewness of the outcomes are orthogonal to each other. This design enables us to cleanly measure both skewness seeking/avoiding and risk taking behavior, and their interaction, without needing to make parametric assumptions. Our experiment includes both left- and right-skewed lotteries. The results reveal that the majority of subjects are skewness avoiding risk takers who correspondingly also take more risk when facing less skewed lotteries. Our second contribution is to link these choices to individual rank-dependent utility preference parameters estimated using a separate lottery choice protocol. Using a latent-class model, we are able to identify two classes of subjects: skewness avoiders with the classic inverse s-shaped probability weighting function and skewness neutral subjects that do not have an inverse s-shaped probability weighting function. Our results thus demonstrate the link between probability distortion and skewness seeking/avoidance choices. They also highlight the importance of accounting for individual heterogeneity.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11166-021-09345-w.

Sensitivity to shifts in probability of harm and benefit in moral dilemmas

Psychologists and philosophers who pose moral dilemmas to understand moral judgment typically specify outcomes as certain to occur in them. This contrasts with real-life moral decision-making, which is almost always infused with probabilities (e.g., the probability of a given outcome if an action is or is not taken). Seven studies examine sensitivity to the size and location of shifts in probabilities of outcomes that would result from action in moral dilemmas. We find that moral judgments differ between actions that result in an equal increase in probability of harm (equal size), but have different end-states (e.g., an increase in harm probability from 25% to 50% or from 50% to 75%). This deviation from expected value is robust under separate evaluation, and increases when the comparison between shifts is made explicit under simultaneous evaluation. Consistent with the centrality of perceived harm in some models of moral judgment, perceived harm partially mediates sensitivity to location of harm probability shift. Unlike for shifts in harm probabilities, participants are insensitive to the location of shifts in probability of beneficial outcomes. They are also insensitive to the location of shifts in probability of analogous monetary losses and gains, suggesting an asymmetry between harm and benefit in moral reasoning, as well as an asymmetry between moral and monetary decision-making more broadly. Implications for normative philosophical theory and moral psychological theory, as well as practical applications, are discussed.

Intuitive symbolic magnitude judgments and decision making under risk in adults

Performance on an intuitive symbolic number skills task-namely the number line estimation task-has previously been found to predict value function curvature in decision making under risk, using a cumulative prospect theory (CPT) model. However there has been no evidence of a similar relationship with the probability weighting function. This is surprising given that both number line estimation and probability weighting can be construed as involving proportion judgment, that is, involving estimating a number on a bounded scale based on its proportional relationship to the whole. In the present work, we re-evaluated the relationship between number line estimation and probability weighting through the lens of proportion judgment. Using a CPT model with a two-parameter probability weighting function, we found a double dissociation: number line estimation bias predicted probability weighting curvature while performance on a different number skills task, number comparison, predicted probability weighting elevation. Interestingly, while degree of bias was correlated across tasks, the direction of bias was not. The findings provide support for proportion judgment as a plausible account of the shape of the probability weighting function, and suggest directions for future work.

Experience and rationality under risk: re-examining the impact of sampling experience

A recent strand of the literature on decision-making under uncertainty has pointed to an intriguing behavioral gap between decisions made from description and decisions made from experience. This study reinvestigates this description-experience gap to understand the impact that sampling experience has on decisions under risk. Our study adopts a complete sampling paradigm to address the lack of control over experienced probabilities by requiring complete sampling without replacement. We also address the roles of utilities and ambiguity, which are central in most current decision models in economics. Thus, our experiment identifies the deviations from expected utility due to over- (or under-) weighting of probabilities. Our results confirm the existence of the behavioral gap, but they provide no evidence for the underweighting of small probabilities within the complete sampling treatment. We find that sampling experience attenuates rather than reverses the inverse S-shaped probability weighting under risk.

The value of disease prevention vs treatment

We present an integrated valuation model for diseases that are life-threatening. The model extends the standard one-period value-per-statistical-life model to three health prospects: healthy, ill, and dead. We derive willingness-to-pay values for prevention efforts that reduce a disease's incidence rate as well as for treatments that lower the corresponding health deterioration and mortality rates. We find that the demand value of prevention always exceeds that of treatment. People often overweight small risks and underweight large ones. We use the rank dependent utility framework to explore how the demand for prevention and treatment alters when people evaluate probabilities in a non-linear manner. For incidence and mortality rates associated with common types of cancers, the inverse-S shaped probability weighting found in experimental studies leads to a significant increase in the demand values of both treatment and prevention.

A computational analysis of the neural bases of Bayesian inference

Empirical support for the Bayesian brain hypothesis, although of major theoretical importance for cognitive neuroscience, is surprisingly scarce. This hypothesis posits simply that neural activities code and compute Bayesian probabilities. Here, we introduce an urn-ball paradigm to relate event-related potentials (ERPs) such as the P300 wave to Bayesian inference. Bayesian model comparison is conducted to compare various models in terms of their ability to explain trial-by-trial variation in ERP responses at different points in time and over different regions of the scalp. Specifically, we are interested in dissociating specific ERP responses in terms of Bayesian updating and predictive surprise. Bayesian updating refers to changes in probability distributions given new observations, while predictive surprise equals the surprise about observations under current probability distributions. Components of the late positive complex (P3a, P3b, Slow Wave) provide dissociable measures of Bayesian updating and predictive surprise. Specifically, the updating of beliefs about hidden states yields the best fit for the anteriorly distributed P3a, whereas the updating of predictions of observations accounts best for the posteriorly distributed Slow Wave. In addition, parietally distributed P3b responses are best fit by predictive surprise. These results indicate that the three components of the late positive complex reflect distinct neural computations. As such they are consistent with the Bayesian brain hypothesis, but these neural computations seem to be subject to nonlinear probability weighting. We integrate these findings with the free-energy principle that instantiates the Bayesian brain hypothesis.