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# Low socioeconomic status amplifies the perceived rarity of large rewards

Simon Ciranka, Coralie Chevallier, Daniel Nettle

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Simon Ciranka, Coralie Chevallier, Daniel Nettle. Low socioeconomic status amplifies the perceived rarity of large rewards. Cognition, 2026, 270, 10.1016/j.cognition.2025.106409. hal-05428595

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# Low socioeconomic status amplifies the perceived rarity of large rewards

Simon Ciranka<sup>a,b,*</sup>, Coralie Chevallier<sup>b</sup>, Daniel Nettle<sup>b,c</sup>

ARTICLE INFO

Keywords:

Socioeconomic status

Risk-reward heuristic

JDM

Ecology

ABSTRACT

Decision-making relies on heuristics derived from past experiences that likely vary with socioeconomic status. We investigate socioeconomic differences in people's reliance on a risk-reward heuristic, which exploits the tendency for larger rewards to be less probable in the world. In simulations, we show that a stronger relationship between rewards and their occurrence probability is expected under conditions of scarcity and high competition when foraging for resources. We then developed lottery and vignette tasks in which participants estimated the probability of rewards of different magnitudes. We found, and replicated, that participants with lower socioeconomic status (n = 144) rate larger rewards as less probable, but smaller rewards as more probable, than participants with higher socioeconomic status (n = 154). The same interaction was observed when comparing success probabilities of competitive foragers in environments where resources are scarce versus abundant. Telling participants about different levels of scarcity and competition in the vignettes altered their probability estimates in a manner consistent with the foraging model. We suggest that the heuristics used by people with lower socioeconomic status are a concrete adaptation to experiences of scarcity. This means psychological socioeconomic differences will persist unless actions directly tackle scarcity.

# 1. Low socioeconomic status amplifies the perceived rarity of large rewards

There is inherent inequality in human societies, because important resources that help people thrive in them, such as jobs, housing, money, and access to education, are limited. Such scarcity creates competition to secure these resources. People with higher socioeconomic status—who have high-paying jobs, education, and financial assets—can readily invest their resources to procure even more resources and may have plenty left to invest in their own comfort. People with lower socioeconomic status often struggle to make ends meet. Because scarcity and competition makes life harder, for instance by a higher demand to manage resources, having a lower socioeconomic status is often associated with experiencing more stress (Haushofer & Fehr, 2014), which connects to a greater probability of psychological and physical ill-being (Freeman et al., 2016), and premature death, even in wealthy WHO member countries whose citizens should have access to the worlds' highest quality healthcare (Stringhini et al., 2017).

Differences in the material conditions in which people live and grow up affect concrete aspects of their psychology and behaviour that are

relevant to their wellbeing. For example, when facing a decision about uncertain prospects that could pay off or not, people with lower socioeconomic status are, on average, more averse to risk and uncertainty (Frey et al., 2021), which may lead them to miss out on opportunities to improve their socioeconomic situation (Haushofer & Salicath, 2023). This risk aversion might be due to higher pessimism (Robb et al., 2009) about, for instance, future career prospects, or future physical and emotional wellbeing (Guyon & Huillery, 2021; Manstead, 2018). Pessimism about the future can lead people to make decisions attempting to obtain smaller, but more likely rewards in ways that come with catastrophic costs in the long run (Ciranka & Hertwig, 2023).

More generally, decision making is often guided by internalized assumptions about the prospect of reward or success in a given context (Bein & Niv, 2025). The assumptions can take the form of heuristics, which are simple strategies that exploit statistical aspects of the environment to make fast and frugal decisions (Hayden & Niv, 2021; Mousavi & Gigerenzer, 2017; Simon, 1956; Todd & Gigerenzer, 2001). For example, people may be quickly suspicious of an email offering them a large amount of money for minimal effort. Most people intuitively know such a prospect is too good to be true and conclude that they

<footnote><sup>a</sup> Center for Adaptive Rationality, Max Planck Institute for Human Development, 14195 Berlin, Germany</footnote>

<footnote><sup>b</sup> Institut Jean Nicod, Département d'études cognitives, Ecole normale supérieure, Université PSL, EHESS, CNRS, 75005 Paris, France</footnote>

<footnote><sup>c</sup> Department for Social Work, Education and Community Wellbeing, Northumbria University, NE7 7XA Newcastle, UK</footnote>

<footnote>* Corresponding author at: Center for Adaptive Rationality, Max Planck Institute for Human Development, 14195 Berlin, Germany.
E-mail address: ciranka@mpib-berlin.mpg.de (S. Ciranka).</footnote>
would rather not share their bank details with the sender. In this case, without having to search for additional information, people instead use a “risk-reward heuristic” which rests on the experience that high rewards are unlikely, and helps them decide they would be better off to ignore that mail.

The risk-reward heuristic exploits experiences with previous attempts to receive a reward when information about the probability of receiving it was hidden or uncertain. The heuristic predicts whether a current decision will result in a reward based on visible cues that previously correlated with reward receipt (Brunswik, 1955; Mack et al., 2020). The magnitude of a prospective reward is such a cue that is often negatively correlated with its probability to occur (Pleskac & Hertwig, 2014). Negative correlations between rewards and their probabilities occur for instance in modern economic markets (Guo & Whitelaw, 2006; Leuker et al., 2020), in games such as roulette, lotteries, and bets (Leuker et al., 2019), and in other contexts as varied as breeding cows, obtaining life insurance, trusting others or getting publishing in high-impact scientific journals (Pleskac & Hertwig, 2014). Being able to infer the probability of getting a reward when this probability information is lacking guides adaptive decision-making under uncertainty.

A risk reward-heuristic is not only adaptive in modern environments but has likely already been useful to human ancestors when they foraged (Todd & Hills, 2020), and thus the readiness to learn about the relationships between rewards and probabilities may be deeply ingrained in the mind. A negative correlation between rewards and their probability in foraging not only emerges because of how resources are naturally distributed in the world, but also because often there is competition for resources (Pleskac et al., 2021). The probability of success in foraging is a function of the ratio between the number of available resources and the intensity of competition for them. When many individuals try to obtain the same resource, the probability of a single individual getting the best resource decreases either when less resources are available and with every additional competitor. If for instance many individuals want to find an apartment in dense cities such as Paris or Berlin, they are much more likely to get a bad than a good apartment for a reasonable rent. Since more individuals try to gain the best apartments and fewer aim for the worst ones, competition for the best resources becomes more intense, and the probability of obtaining them decreases

further.

Limited access to resources and increased competition are fundamental aspects of economic disadvantage (Willis et al., 2022). People with lower socioeconomic status experience more intense competition for scarce resources like jobs or housing (Deming, 2020), by definition. Therefore, if people use a risk-reward heuristic that is informed by their previous experience when attempting to obtain resources, then people with a lower socioeconomic status might estimate reward probabilities in a way that is coherent with an environment of resource scarcity and high competition. In the following, we first demonstrate how the relationship between reward magnitudes and the probability of obtaining rewards differs in environments that differ in scarcity and competition. This generates concrete hypotheses about how probability judgments should differ between people with a lower and a higher socioeconomic status when using the risk-reward heuristic, which we subsequently test in human participants.

Scarcity and competition intensify the correlation between reward magnitude and success probability when foraging for resources

We first simulated agents that collectively forage for a scarce resource in a landscape of resource patches. The expected utility of one patch y is given by the number $m_y$ and reward magnitude $q_y$ of its resources. The number of competitors who target the same patch $c_y$, is proportional to this utility because each agent goes to patches where they expect the highest utility. This results in an equilibrium state of an “ideal free distribution” (Krivan et al., 2008) with more competitors at better patches (Fig. 1a):

$$c_y \sim m_y \times q_y \quad (1)$$

A new agent entering such an environment at equilibrium will have a probability, p, of successfully obtaining a resource that is proportional to the number of resources and inversely proportional to the number of competitors desiring the same resource:

$$p_y = \frac{m_y}{c_y} \quad (2)$$

In other words, the probability of foraging success is given by a ratio between scarcity and competition. From substituting Eq. 2 into Eq. 1, it follows that success probabilities, p, are inversely proportional to reward

<figure><img src="image_3.png" /><figcaption>Fig. 1. Competitive foraging simulations and success probabilities a) Optimal foraging decisions of competing agents produce individual success probabilities that are inversely proportional to reward magnitude. When competitors outnumber the available resources (here, 15 monkeys competing for five apples, yielding a ~ 0.3 (5/15) ratio between the total number of resources and competitors), higher quality resources (bigger apples) attract more competitors; thus, the probability of success (fractions) for one individual is smaller. b) Success probabilities for resources of different quality (reward magnitudes) under different assumptions about the intensity of competition. E indicates the smallest reward; A indicates the largest reward. Colours denote different ratios between number of resources and number of competitors. Lower ratios mean increased scarcity or competition, which in turn steepens the relationship between foraging success probability and reward size. Lines show the fit of a general additive model predicting success probability as a function of reward size based on 1000 simulations of the foraging model.</figcaption></figure>
magnitude in foraging:

$$p_y \sim \frac{1}{q_y} \qquad (3)$$

Fig. 1b illustrates that for a new agent entering such a foraging environment, the relationship between success probability and resource quality is steep when the ratio between available resources and competitors is low—that is, when resources are relatively scarce, or competition is relatively high. Conversely, when new agents enter an environment with a higher ratio, i.e. abundant resources or little competition, they experience a flatter relationship between success probabilities and rewards.

In sum, decision-making under uncertainty relies on heuristics, which exploit statistical regularities of the environment and often are the consequence of repeated learning experiences with these regularities. We here hypothesized a risk-reward heuristic, which exploits the experience that large rewards are unlikely, is a particularly relevant mechanism behind socioeconomic differences in judgments and decision-making.

To test the hypotheses that the probability judgments of people who differ in their socioeconomic status look as if they adapted to different ecologies of scarcity and competition, we developed lottery and vignette tasks in which participants, grouped by socioeconomic status, estimated the probability of rewards of different magnitudes. To support the claim that participants' responses could be driven by an internalized experience of scarcity and competition, we formulated different vignettes where competition was explicitly relaxed or scarcity was explicitly eliminated. We expected that all participants in general will use a risk-reward heuristic when reasoning about the probability of uncertain rewards. Further, we predicted (preregistration: https://osf.io/5har8) that the slope of the relationship between reward size and probability will be particularly steep for people of lower socioeconomic status because stronger negative reward-probability correlations occur in environments with relatively high competition for scarce resources (Pleskac et al., 2021), such as the environments that people with a lower socioeconomic status are more likely to live in.

## 2. Methods

### 2.1. Participants

We recruited one original and one replication (preregistration: https://osf.io/5har8) participant sample in Prolific Academic. Both samples completed exactly the same tasks. All participants were residents of the United Kingdom. For the original and replication studies, we preregistered to recruit 200 participants, attempting to measure 100 participants who indicated having lower socioeconomic status, and 100 who indicated having higher socioeconomic status at time of registering for Prolific (i.e. < 3 and > 7 on the MacArthur ladder scale that ranges from 1 to 10; Moss, Kelly, Bird, Nutting, & Pickett, 2023, Moss, Kelly, Bird, & Pickett, 2023). Socioeconomic status groups were then based on a median split of the MacArthur ladder as remeasured within our initial survey. On this basis, participants lower than or equal to rung 4 were classified as “low socioeconomic status”. Participants who only indicated one probability estimate in a given condition (see below) were excluded from further analyses.

We recruited participants to contain an equal amount of people on every ladder rung, with balanced gender, between 30 and 35 years of age. Because these criteria could not be fulfilled by Prolific within two weeks, we relaxed the age requirements and gender balance, but did not terminate the original study, leading us to eventually over-sample 208 and 212 instead of 200 participants in the original and the replication study, respectively. In a robustness check, we generated 1000 bootstrapped samples to match the originally planned sample size and show that all reported conclusions hold in the resampled datasets (supplementary fig. S3).

We estimated the effects in separate analyses of our original and replication samples. For readability, and in alignment with our preregistration, in the Results section, we report results of a pooled analysis that combines the two studies, with n = 144 (mean age = 32.57, sd age = 2.21) in the low and n = 154 in the high socioeconomic status group (mean age = 32.28, sd age = 1.81). Unless otherwise stated, the reported findings were equivalent in separate analyses of the two samples. We report separate results of the two samples in supplementary tables S5-S10.

### 2.2. Tasks

We hypothesized that people with different experiences of scarcity and competition—such as people with different socioeconomic statuses—should use risk-reward-heuristics differently because scarcity and competition alter the statistics of rewarding experiences on which the heuristic is based. We tested this hypothesis in two tasks, a lottery and a vignettes task, in which participants estimated the probabilities that they would receive rewards of different magnitudes.

In the lottery task, participants saw an urn containing red and blue balls. Participants were asked to imagine that they would win a reward if they drew a blue ball. They were told a hypothetical lottery reward, which varied from 4 to 270 GBP. Unlike most studies using lottery paradigms, participants were not told the probability of winning the reward but instead had to estimate it (Fig. 2a). After they registered their probability estimates, participants were asked whether they would choose to play the lottery for a fee of 2 GBP (Fig. 2b). This choice was merely hypothetical and not incentivized. Participants completed 60 estimation and choice trials.

In the vignettes task, we were interested in whether the conclusions based on abstract lotteries generalize to other scenarios that are more relevant to daily life (jobs, apartments, dates, and movie tickets; Fig. 4 columns The exact wording of the vignettes is shown in supplementary tables S1-S4. Participants estimated probabilities of securing a resource (Fig. 2c) in four scenarios involving competition for real-life resources: jobs, apartments, dates, and movie tickets. “Reward” quality in each scenario ranged from “A” to “E”: For instance, “A” jobs pay well, have nice co-workers, are close to home, are interesting, and allow for work-life balance. Each lower quality job has one less desirable feature (see Supplementary materials for details).

In addition to a scenario where “everything is as usual”, henceforth the standard condition, we asked for probability estimates in two other conditions: one where we emphasized the randomness of obtaining the resource (henceforth the randomness condition) and one where we emphasized that resources were abundant (henceforth the abundance condition). By doing so, we attempted to manipulate the boundary conditions of scarcity and competition that people use to inform their judgments, in order to demonstrate that these factors can indeed directly influence participants’ estimates of the probability of obtaining rewards of different magnitudes. The conditions in the vignettes were randomized between participants.

### 2.3. Analyses

#### 2.3.1. Probability estimates

We were most interested in socioeconomic differences in how our participants’ probability estimates depended on reward magnitude, which we measured with the interaction between socioeconomic status and reward magnitude (value). As preregistered, socioeconomic status was a factor indicating whether participants were on the lower or upper group of the median split on the McArthur ladder.

In a preregistered overall analysis, we used a hierarchical Bayesian regression model that treats scenarios (lotteries and different vignettes) as random effects with random slopes for each scenario and value and per a participant specific intercept (regression formulas are in R-formula notation):
a Lottery estimation

Imagine you could pay £ 2 and would receive

£ 5.5

<figure><img src="image_4.png" /></figure>

b Lottery choice

<figure><img src="image_5.png" /></figure>

c Vignette estimation

What do you think is the probability that you will get the apartment of type "A"-"E"?

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Lottery estimation and choice and vignette task. a) Example of the lottery estimation task. Participants saw a reward value and indicated the probability of winning on a slider, which changed the ratio of red and blue balls in the display. b) Example of the lottery choice task. Participants were asked whether they would play the lotteries, based on their probability estimates. c) Example of the vignette estimation task. Participants estimated the probability that they would obtain a resource according to its value. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

$$ probability\_estimate \sim value^*SES + (value + scenario| participant), \quad (4) $$

In the vignettes task, we expanded this model with categorical predictors for different scenarios (apartments, jobs, dates, movies) as a fixed effect and an interaction with categorical predictors for whether estimates were for the standard, random or abundance competition condition:

$$ probability\_estimate \sim value^*SES^*conditions + scenario + (value| participant) \quad (5) $$

### 2.3.2. Probability weighting

In the lottery task, we also investigated group differences in probability estimates with a probability weighting function (Pleskac et al., 2021; Prelec, 1998). If the probability and rewards would be strictly equivalent, the probability would be given by the ratio between the cost (c) or investment necessary to obtaining a potential reward, and V the resource's reward with the cost subtracted ($p = \frac{c}{V-c}$). We use the same weighting function used by (Pleskac et al., 2021):

$$ p = \exp\left(-\kappa^* \ln\left(\frac{c}{V-c}\right)^{\lambda}\right). \quad (6) $$

In this weighting function, $κ$ is an elevation parameter that shifts probability upwards when higher. $λ$ governs the slope of the function, the lower it is, the less steep the slope. $κ$ and $λ$ were estimated with a differential evolution algorithm (Mullen et al., 2011) minimizing $-2^*$ the log-likelihood of a beta distribution that has $p$ from Eq. 6 as $μ$ and another free variance parameter $φ$

$$ \begin{aligned} \alpha &= \mu^*\phi \\ \beta &= (1 - \mu)^*\phi \end{aligned} \quad (7) $$

Via parameter recovery we demonstrate that this procedure can, in principle, accurately identify plausible data-generating parameters by fitting the model to simulations with known parameter values (see supplementary figs. S5-S7).

Socioeconomic status differences in the elevation $κ$ and slope $λ$ of the weighting function were evaluated by regressing them on the socioeconomic status group from which they arose:

$$ \kappa, \lambda \sim SES, \quad (8) $$

### 2.3.3. Choices

To analyse if there are socioeconomic differences in participants' propensity to make a risky choice beyond socioeconomic differences in
participants' probability judgments, we investigated their hypothetical choice to either play the lottery or not. Using our participants' individual probability judgments, we calculated the expected value of each lottery by multiplying the value by the corresponding probability judgment. Using this expected value and participants' socioeconomic status, we used a binomial likelihood and a logit link function to predict binary choices. We also formulated this as a hierarchical regression model with random slopes for expected values over random intercepts per participant:

$$ \text{choice} \sim \text{expected\_value} * \text{SES} + (\text{expected\_value} | \text{participant}). \quad (9) $$

### 2.3.4. Fitting procedure and reporting of the regression models

All regressions were fitted in a Bayesian framework by using the NUTS Markov Chain Monte Carlo sampler implemented in Stan and accessed via brms (Bürkner, 2017) for R (R Core Team, 2022). We used 20 chains with 4000 samples each, discarding the first 2000 as a warm-up. In general, we report Bayesian indices of effect existence, most prominently Bayes factors, obtained by computing the Savage Dickey density ratio between the posterior mode of the respective regression coefficient and a Cauchy prior with location 0 and dispersion 0.1. We also report posterior medians as estimates of the central tendency of the posterior regression coefficients, followed by the 95% credible interval. Then, we report the probability of direction, pd. This is the probability that the true regression coefficient has the direction of the median. I maps inversely onto a frequentist p-value: Finally, we report the proportion of the posterior samples that fall into a region of practical equivalence (ROPE) for each regression coefficient, which we define by the interval [-0.1*SDy, 0.1*SDy] for regular nd [-0.18, 0.18] for logistic regressions (Kruschke, 2018).

For every analysis we report statistics based on pooling the original and replication study for readability. Figures visually separate the original and replication study. Regression tables that show separate analysis for the pooled, original and replication study are in the supplement (supplementary tables S1 and S2). We explicitly note when conclusions between original and replication sample would differ in the main text. Further, grouping participants into a high and low socioeconomic status group with a median split risks omitting variance in the data that can make a decisive difference for statistical inference. Thus, in an additional robustness check, we used participants' scores on the McArthur scale as a continuous variable and found that the results were unchanged (see supplementary tables S10 & S11).

## 3. Results

### 3.1. Combined analyses

First, we performed a preregistered combined analysis of probability judgments in the lottery and vignette tasks (regression formula in Eq. 4). We combined probability estimates from the lotteries and the standard condition of the vignette tasks and predicted probability estimates with SES and the prospective reward magnitude (Fig. 3; Table S5). In line with the risk-reward heuristic, participants were on average more pessimistic for higher rewards (BF10 > 100; median = -0.13, 95% CI = [-0.16, -0.10]; pd. = 1; posterior in ROPE = 0%). There was no average difference in probability estimates between participants with higher compared to those with a lower socioeconomic status (BF10 = 0.15; median = 0.004; 95% CI = [-0.03, 0.02]; pd. = 0.68; posterior in ROPE = 96.81%). There was, as predicted, an interaction between our participants SES and the reward value when predicting participants probability estimates (BF10 = 8.45; median = -0.06; 95% CI = [-0.10, -0.02]; pd. > 0.99; posterior in ROPE = 6.94%). Compared to people with higher socioeconomic status, people with lower socioeconomic status estimated low-reward outcomes to be more likely and high-reward outcomes to be less likely. This was true in the pooled data, as well as both the original and replication samples considered individually (Panels in Fig. 3).

### 3.2. Lotteries

We then analysed socioeconomic differences in using the risk reward heuristic when participants estimated reward probabilities in the lotteries, by examining socioeconomic differences in the parameters of a probability weighting function and in participants' hypothetical choices. Probability estimates for the different lotteries by socioeconomic status group are shown in Fig. 4a to which we fitted a weighting function (Prelec, 1998). The weighting function has a parameter governing its curvature (Fig. 4b) and another governing its elevation (Fig. 4c). The curvature parameter determines how strongly small probabilities are overweighted and how strongly large probabilities are underweighted compared to a case in which they would be strictly proportional. This parameter thus reflects the strength of the risk-reward heuristic. The elevation reflects overall optimism or pessimism, setting a baseline from which probabilities are perceived. With these two parameters, we evaluated the deviation between participants' probability estimates and a probability dictated by a risk-reward heuristic (Leuker

<figure><img src="image_7.png" /><figcaption>Fig. 3. Regression coefficients for participants' probability estimates. The x-axes show the probability densities of the regression coefficients on the y-axes. Green denotes probability densities smaller than zero; blue denotes probability densities larger than 0. Opaque density plots show the probability density of the posterior regression coefficient; transparent densities denote the prior. The red point ranges under the densities are centered around the densities' mean and show the 95% credible intervals as error bars. Dashed vertical lines at zero show where the density ratio between prior and posterior was computed to obtain Bayes factors. Dotted vertical lines denote the region of practical equivalence (ROPE). Panels show the pooled, original, and replication regression results. SES = socioeconomic status. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>a</figcaption></figure>

**Fig. 4.** Socioeconomic differences in probability estimates and decisions on the lottery task. a) Participants' probability estimates given the value of the lottery reward they could receive if they won (logscale), by socioeconomic status (colours). Solid lines and dots denote results from the original and dotted lines and triangles results from the replication. Deviations from direct proportionality between probability and reward are captured by fitting a probability weighting function to participants' estimates. b) Denotes the slope of the weighting function, indicating the strength of the risk-reward heuristic. c) Denotes the elevation of the weighting function, indicating average pessimism respectively, plotted against participants' self-assigned socioeconomic status ranking on the McArthur ladder. d) Probability of playing the lottery depending on socioeconomic status, with shapes and the bars' height denoting the average and errorbars the bootstrapped 95 % confidence interval. e) Probability to play the lottery based on socioeconomic status and the lotteries' expected value. Fit lines show choice probabilities of a logistic regression predicting risky choices with expected values based on participants' probability estimates. Generally, boxplots show the median in the middle, the interquartile range as boxes, 95 % confidence interval in whiskers and outliers as dots. Ribbons show the 95 % confidence interval of a linear regression. SES = socioeconomic status.

<figure><img src="image_9.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_12.png" /></figure>

et al., 2018) in which probabilities are strictly proportional to the reward.

The curvature of the probability weighting function decreased with increasing socioeconomic status (BF10 = 28.90; median = 0.13; 95 % CI = [0.03, 0.21]; pd. > 0.99; posterior in ROPE = 1 %; Fig. 4b). In other words, people with a lower socioeconomic status assumed a stronger negative relationship between reward magnitudes and their probabilities. There was no socioeconomic status difference in elevation (BF10 = 0.67; median = -0.04; 95 % CI = [-0.14, 0.04]; pd. = 0.83; posterior in ROPE = 58 %; Fig. 4c). In other words, there were, across all reward sizes taken together, no socioeconomic differences in how optimistic or pessimistic participants were about receiving a reward overall.

After participants had provided their probability estimates, we asked them whether they would choose to play the lottery. On first sight it seems as if people with a lower socioeconomic status are less likely to play the lottery (Fig. 4d) and thus “risk averse”. However, by using our participants’ probability estimates to calculate a subjective expected value for each lottery and controlling for it (Fig. 2e), the only remaining credible predictor of choosing the lottery was the lotteries expected value (BF10 > 100; median = 0.35, 95 % CI = [0.26, 0.45]; pd. = 1 %; posterior in ROPE = 0 %; Fig. 4e). Socioeconomic status (BF10 = 1.03; median = -0.03, 95 % CI = [-0.22, 0.16]; pd. = 63.72 %; posterior in ROPE = 95.84 %) and its interaction with expected value were no credible predictors for participants propensity to make a hypothetical risky choice (BF10 = 0.69; median = 0.04, 95 % CI = [-0.07, 0.16]; pd. = 74.58 %; posterior in ROPE = 100 %). In other words, the socioeconomic differences in apparent risk-aversion were fully explained by participants with a lower socioeconomic status thinking that winning

high reward lotteries was less likely.

### 3.3. Vignettes

Using vignettes, we then investigated if the above results generalize to more realistic scenarios. With the vignettes we could also test whether probability estimates were affected by hinting to different competitive conditions, where we emphasized either an element of randomness in getting the resource, or that resources would be abundant. When our participants’ probability estimates and socioeconomic differences are affected by this manipulation, this underscores the relevance of scarcity and competition in foraging for using the risk-reward heuristic (Fig. 5, rows, see below).

In the standard condition, everything was described as being “as usual” (Fig. 5, first row). There was no average difference between the participants’ probability estimates between the lower and higher socioeconomic status group (BF10 = 0.46; median = -0.02; 95 % CI = [-0.05, 0.00]; pd. = 0.94; posterior in ROPE = 73.31 %). However, there was again an interaction between the reward and participants’ socioeconomic status (BF10 = 28.44; median = -0.08; 95 % CI = [-0.13, -0.03]; pd. > 0.99; posterior in ROPE = 0.62 %), suggesting, like the probability estimates in the lottery task, that people with a lower socioeconomic status assumed a steeper relationship between reward sizes and the probabilities of obtaining the reward.
<figure><img src="image_13.png" /><figcaption>**Fig. 5.** Participants' probability estimates for different rewards in the vignettes (columns). Colours denote socioeconomic status, and line types as well as shapes indicate the original and replication study, with solid lines and points denoting the original and dotted lines and triangles the replication study. In every single condition and scenario and in the original as well as the replication, people with a lower socioeconomic status assume on average a stronger correlation between rewards and probabilities. Lines show linear fit, with the 95 % CI in ribbons. SES = socioeconomic status.</figcaption></figure>

**3.4. Hints to decreased competition diminish socioeconomic differences in probability estimates**

To explore directly whether notions of scarcity and competition influence participants' probability estimates in a way that aligns with the socioeconomic differences in the probability judgments we show above, we manipulated these factors in the wordings of our vignettes to either make scarcity of the resource or competition for the resource appear weaker (see methods and supplementary tabs S1–4 for the exact wording).

Emphasizing an element of randomness in getting the resource should equalize competition for rewards (Netz et al., 2023), and diminish socioeconomic differences (Fig. 5 second row). In the “randomness condition”, participants were more pessimistic and estimated lower probabilities on average, as implied by the negative main effect of the randomness condition on probability estimates (BF10 > 100; median = −0.14, 95 % CI = [−0.15, −0.13]; pd. = 1; posterior in ROPE = 0 %). This effect was similar for participants with lower socioeconomic status, there was no interaction between socioeconomic status and the randomness condition (BF10 = 0.41; median = −0.01, 95 % CI = [−0.03, 0.00]; pd. = 0.96; posterior in ROPE = 100 %). Participants on average thought the relationship between rewards and probabilities in the randomness condition was stronger than in the standard condition, which is implied by the interaction between the randomness condition and reward magnitudes (BF10 > 59.86; median = −0.03, 95 % CI = [−0.04, −0.01]; pd. > 0.99; posterior in ROPE = 47.96 %). The interaction between rewards, socioeconomic status and the randomness condition was positive (BF10 > 100; median = 0.04, 95 % CI = [0.02, 0.07]; pd = 1 %; posterior in ROPE = 5.15 %). Because this

three-way interaction is positive, ie. the opposite direction as the two-way interaction between socioeconomic status and value in the standard condition, socioeconomic differences in judgments that exist in the standard condition are attenuated in the randomness condition. In sum, notions of randomness increased the strength of the risk-reward heuristic on average, and decreased socioeconomic differences in participants' probability estimates.

Emphasizing that resources are abundant on the other hand, should make participants think that getting the resource is more likely (Fig. 5 third row). Indeed, in the “abundance condition”, participants were less pessimistic and estimated higher probabilities on average, as implied by the positive main effect of the abundance condition on probability estimates (BF10 > 100; median = 0.10, 95 % CI = [0.09, 0.11]; pd. = 1; posterior in ROPE = 0 %). This effect, was slightly stronger for people with low socioeconomic status, as implied by the interaction between the abundance condition and socioeconomic status, however the Bayes factor suggest that the existence of this interaction remains undecided (BF10 = 2.9; median = 0.02, 95 % CI = [0.01, 0.03]; pd. = 0.96; posterior in ROPE = 90.74 %). Table S4 shows that socioeconomic differences were stronger in the original study but absent in the replication. Participants estimated the relationship between rewards and their probabilities to be less steep than in the standard condition, which is implied by the positive interaction between reward magnitude and the abundance condition (BF10 > 100; median = 0.05, 95 % CI = [0.04, 0.07]; pd. = 1; posterior in ROPE = 0 %), which suggests that thoughts about the availability of resources indeed affected our participants' probability judgments. There was Bayesian evidence for absence of a three-way interaction between socioeconomic status, reward and the abundance condition (BF10 = 0.13; median = 0.00, 95 % CI = [−0.01,
0.03]; pd. = 72.46%; posterior in ROPE = 100%), which suggests that socioeconomic differences in using the risk-reward heuristic prevail even when hinting people to that resources are abundant. In sum, notions of abundance decreased the strength of the risk-reward heuristic on average, but socioeconomic differences in participants probability estimates remained the same.

## 4. Discussion

We demonstrated robust and replicable differences in people's assumptions about the probabilities of rewards depending on their socioeconomic status. Using simulations, we showed that when foragers search for scarce resources, more scarcity and competition increase the negative correlation between reward magnitude and the probability to get it. We hypothesized that low socioeconomic status participants would judge the negative relationship between probabilities and rewards to be stronger, which would be in line with an adaptation to scarcity and competition. In two tasks, participants estimated probabilities of gaining different rewards using only knowledge about potential reward magnitudes. The hypothesis was confirmed as people with lower socioeconomic status indeed assumed steeper relationships between rewards and their probabilities than did those with higher socioeconomic status. In other words, low rewards seemed more likely to people with lower socioeconomic status, whilst high rewards seemed more likely to those with higher socioeconomic status. This is in line with the idea that people with a lower socioeconomic status adapted their judgments of reward probabilities to a world of scarcity and competition.

That people with different socioeconomic status live in worlds that differ in their reward statistics seems obvious. This fact can in itself lead to different biases in decision making such as anchoring (Tversky & Kahneman, 1974) or adapting to the ranges of outcomes (Bavard & Palminteri, 2023) that people experienced in the past and expect to experience in the future. We suggest that socioeconomic status gradients in decision-making heuristics directly reflect gradients in the lived experience of dwelling in an ecology in which resources are scarcer and competition for resources is stronger for people with a lower socioeconomic status.

Our study enriches the perspective on a behavioural constellation of deprivation (Pepper & Nettle, 2017), characterized by more impulsivity and less risk-taking by people with lower socioeconomic status compared to people with higher socioeconomic status. Our work suggests the prior assumptions that people use to inform their decisions about uncertain prospects affect decisions taken under risk and uncertainty. People with lower socioeconomic status were less optimistic about high rewards and more optimistic about low rewards. These judgments may contribute to explaining why people of lower socioeconomic status are less likely to invest time and effort into outcomes with uncertain consequences, like not enrolling in a higher education program despite financial aid and good grades (Boon-Falleur et al., 2025; Guyon & Huillery, 2021). They might be convinced that the rewards promised by such high-stakes decisions are so unlikely that they are not worth any investment. Instead, rewards that are smaller but more certain, for example, those offered by health-comprising, seemingly impulsive behaviour, are often initially also more likely (Ciranka & Hertwig, 2023). The subjective expected utility of these more concrete rewards is therefore higher for people with lower socioeconomic status compared to those who live a more privileged life.

To our knowledge, no previous theories predict the robust interaction between socioeconomic status and reward-probability relationships that we report. Some, however, predict the main effects of risk avoidance (Amir & Jordan, 2017). We also found evidence for average risk-avoidant behaviour—namely, people with lower socioeconomic status were less likely to play a lottery. However, this socioeconomic difference in risky choice disappeared when adjusting for how likely participants thought they were to win the lottery. While we can not say

for certain whether one group is better adjusted to their personal experiences than the other, we suggest that not only judgments about probability, but also the decisions made by people of differing socioeconomic statuses may be a sign of their ecological rationality, or “contextually appropriate responses” (Pepper & Nettle, 2017), instead of “poor mental power” (Vohs, 2013) or maladaptive behaviour. Our data allows the interpretation that people might maximise their own subjective expected value given the constraints of their subjective representation of the world that was formed by experience.

Socioeconomic differences in an environment of scarcity and competition already impact experiences in children and adolescents, with the ability to leave long-lasting traces even in the neurobiological (Romer et al., 2017) and epigenetic (Tehranifar et al., 2013) underpinnings of decision-making. Socioeconomic differences with long-lasting effects, for example, become apparent already in schools. Less affluent parents can and do invest less in private lessons for their children in the United States (Murnane & Reardon, 2017) and the European Union (Bray, 2021). This leads to fewer rewarding experiences and more stress regarding educational attainment for children in disadvantaged families, particularly if they compare their grade rank (Stewart et al., 2006) with that of more advantaged peers. Educational systems that create reliable rewards for educational attainment and prosociality rather than encouraging competition and comparisons between pupils seem more effective in equalizing the differences between children from lower and higher socioeconomic backgrounds (Marshall, 2017).

### 4.1. Limitations

This work has limitations. For example, we relied on a self-reported measure of socioeconomic status using a scale, the McArthur ladder, that is often disliked and perceived as patronizing, particularly by people with a lower socioeconomic status (Moss, Kelly, Bird, Nutting, & Pickett, 2023; Moss, Kelly, Bird, & Pickett, 2023). This could lead to a biased assessment of our participants' socioeconomic status and, consequently, to misjudging true socioeconomic differences. On the other hand, subjective socioeconomic status is often a more consistent predictor of various psychological outcomes than “objective” measures like household income or education (Adler et al., 2000; Schmalor & Heine, 2022), hence we decided for this scale in awareness of its issues.

Another limitation is that a group difference in probability estimates could be due to a group difference in response noise, leading to stronger regression to the mean and more estimates close to 0.5 in one group. A post-hoc control analysis, in which we investigate separate residual variances for both socioeconomic status groups, suggests that participants with higher socioeconomic status may have indeed responded more noisily in the vignette studies, although not in the lotteries (see Supplementary Fig. S4 for details). Higher impulsivity, lower executive function or less education are often correlated with more noisy responses. However, if these factors primarily drive the group differences we reported here, one would expect participants with a lower socioeconomic status, and not a higher one, to judge the correlation between reward and probability to be weaker, as these factors are thought to be more pronounced in people with a lower socioeconomic status (Haushofer & Salicath, 2023). A more plausible alternative explanation is therefore that participants with higher socioeconomic status were less engaged or took the experiment less seriously than those with lower socioeconomic status. Notably, a statistically meaningful interaction between socioeconomic status and value remains when controlling for group-specific variances. Still, this is a note of caution. Replicating our study on site in a lab creates greater social accountability and experimental control, potentially yielding cleaner data better suited to infer group differences in people's use of the risk-reward heuristic.

Further, while we show socioeconomic differences in people's use of the risk-reward heuristic and suggest that these differences may stem from differences in experienced “foraging” success, we cannot know how and why people actually learn to use the heuristic in the real world.
In changing the wording of the vignettes to emphasize different competitive conditions, we demonstrated that people adjust their probability estimates accordingly, for example, all participants provided more optimistic estimates when mentioning that resources would be abundant and socioeconomic differences diminished when we emphasized an element of randomness that hits everyone equally in getting the resource. This suggests that foraging boundary conditions at least informed our participants' probability estimates. However, systematic ecological and developmental studies on socioeconomic differences in the statistics of real-life experiences (Ciranka & Hertwig, 2023) are necessary to understand the stickiness and specificity of the psychological effects of experiencing scarcity and competition when trying to make ends meet.

## 4.2. Summary and conclusion

In conclusion, we have shown that the judgments about the probability of obtaining valuable resources of people with lower socioeconomic status differ from those with higher socioeconomic status in a way that aligns with rational adaptation to competition and scarcity. We cannot say for certain whether one group is better adjusted to their personal experiences than the other, but our results do not imply that the decision-making of people with lower socioeconomic status is inherently flawed or maladaptive, as some suggest (Vohs, 2013). Instead, we argue that people's behaviour may often be "ecologically rational" (Simon, 1956) and, to some extent, adapted to the statistics of their individual experiences. Our work provides a foundation for supporting people in need by trying to understand their world rather than demonizing or patronizing them. Some people assume that a problem of poverty lies in the minds of people who experience poverty. We suggest that what is in their mind – an assumption that really good things are unlikely to come about – actually comes from the statistical structure of their world and their experiences. This means that the right level to intervene at is the world – the actual distribution of scarcity and competition – and the mind will follow suit.

## Artificial intelligence

ChatGPT was used to help programming the task. Other than that, no artificial intelligence was used in this research or the creation of this article.

## Preregistration

The main hypotheses and methods were preregistered (https://osf.io/5har8) prior to data collection of the replication study. We also preregistered hypotheses for the original study, (https://osf.io/56fmp) which differed from the hypotheses that we preregistered for the replication. There were deviations from the preregistration of the replication study in terms of final sample size and random effects structure (see supplementary material).

## Materials

All study materials are publicly available (https://figshare.com/s/dc2b7ca92d084a4544ec).

## Data

All primary data are publicly available (https://figshare.com/s/dc2b7ca92d084a4544ec).

## Analysis scripts

All analysis scripts are publicly available (https://figshare.com/s/dc2b7ca92d084a4544ec).

## CRediT authorship contribution statement

**Simon Ciranka:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Coralie Chevallier:** Writing – review & editing, Validation, Supervision. **Daniel Nettle:** Writing – review & editing, Writing – original draft, Supervision, Conceptualization.

## Ethics

This research received approval from a local ethics board (Original: A2023_26; Replication C2024_17).

## Funding

This research was supported by the CNRS and the Max Planck Society with funding from their joint SALTO programme, awarded to SC.

## Declaration of competing interest

All authors declare no conflicts of interest.

## Acknowledgments

We thank Tim Pleskac and Zoe Rahwan for helpful comments. We also thank Deborah Ain for invaluable editing support and helpful comments.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.cognition.2025.106409.

## Data availability

data is freely available

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