2.1 Participants

A total of 56 participants were recruited through Amazon Mechanical Turk. Each participant was paid USD$8.00 for completing the experiment. There were 37 male participants and 19 female participants, with ages ranging from 20 to 61 (M = 36.4 years, SD = 11.6 years).

2.2 Procedure

Each of the four cognitive tasks took about 20–30 minutes to complete. The RPS and RTI took about 5 minutes each, while the DOSPERT took about 10–15 minutes. Each participant completed all of the questionnaires and decision-making tasks. Because the entire experiment took about two hours to complete, the experiment was split into two parts of about one hour each. Each part included two decision-making tasks and either the RPS and RTI or the DOSPERT. The RPS and RTI were completed in the same part because these two questionnaires are much shorter than the DOSPERT. The order of questionnaires and decision-making tasks was randomized across participants.

Upon completing Part 1 of the experiment, each participant was given a unique code. This code allowed them to complete Part 2 and receive compensation. All participants who completed Part 1 returned and completed Part 2. Participants were also encouraged to take a break between Part 1 and Part 2, subject to the requirement that they complete both parts within six days.

3.2 Two-Parameter BART Model

Reference Wallsten, Pleskac and LejuezWallsten et al. (2005) pioneered the development of cognitive models for the BART that are capable of inferring latent parameters measuring risk propensity. Their modeling approach was further developed by Reference PleskacPleskac (2008) and Reference Zhou, Myung and PittZhou et al. (2019). We use the two-parameter BART model developed by Reference PleskacPleskac (2008), see also (Reference Van Ravenzwaaij, Dutilh and WagenmakersVan Ravenzwaaij et al., 2011) as a simplification of one of the original Reference Wallsten, Pleskac and LejuezWallsten et al. (2005) models. The two-parameter model assumes that a decision maker believes that there is a single constant probability that a pump will make a balloon burst p ^belief that is fixed over all problems. It also assumes that they decide on a number of pumps prior to the first pump in a problem, and do not adjust this number during pumping. This number of pumps that the participant considers to be optimal, denoted by ω, depends on their propensity for risk taking, γ⁺, and their belief about the bursting probability of the balloon when it is pumped. It is defined as

where γ⁺ ∼ uniform (0,10 ).

Our implementation of the two-parameter BART model naturally incorporates censoring by modeling the probability of each of participant pumping or banking on each trial they completed. Thus, the behavioral data are represented as y _ijk = 1 if the ith participant pumped on the kth trial of the jth problem, and y _ijk = 0 if they banked.

In the two-parameter BART model, the probability that the ith participant will pump on the kth trial of the jth problem, p ^pump_ijk depends on both ω_i and a behavioral consistency parameter β_i, in terms of a logistic function

with β_i ∼ uniform (0,10 ). Given this pumping probability, the observed data are simply modeled as y ^pump_ijk∼Bernoulli(p _ijk) over all observed trials, finishing on the trial for each problem at which the participant banked or the balloon burst.

The logistic relationship that defines the pumping probabilities means that relatively higher values for β_i correspond to more consistency in decision making. If β_i = 0 then p ^pump_ijk = 0.5, and the participant’s decision to pump or bank is random. As β_i → ∞, the participant’s behavior becomes completely determined by whether or not the number of pumps k is greater than ω_i.

The γ_i⁺ parameter provides a measure of risk propensity, since it controls the number of pumps attempted. Larger values of γ_i⁺ correspond to more pumps and greater risk seeking. Smaller values of γ_i⁺ correspond to fewer pumps, and more risk-averse behavior.

We implemented the two-parameter model model, and all of the other cognitive models considered in this article, as graphical models using JAGS (Reference Plummer, Hornik, Leisch and ZeileisPlummer, 2003). JAGS is software that facilitates MCMC-based computational Bayesian inference (Reference Lee and WagenmakersLee & Wagenmakers, 2013). All of our modeling results are based on four chains of 1,000 samples each, collected after 2,000 discarded burn-in samples. The chains were verified for convergence using visual inspection and the standard statistic (Reference Brooks and GelmanBrooks & Gelman, 1997).

3.3 Modeling Results

For all of the cognitive modeling in this article, we apply the model to the data in three steps. First, we define task-specific contaminant models, identifying those participants who did not understand the task, or did not complete it in a motivated way. These contaminant participants are removed from the subsequent analysis. Secondly, we examine the descriptive adequacy of the model for the remaining participants, using the standard Bayesian approach of posterior-predictive checking (Reference Gelman, Carlin, Stern and RubinGelman et al., 2004). Finally, we report the inferences for the model parameters, usually starting with a few illustrative participants who demonstrate the range of interpretable individual differences, before showing the inferences for all non-contaminant participants.

3.3.2 Descriptive Adequacy

Figure 1 summarizes a posterior predictive check of the descriptive adequacy of the two-parameter BART model. The distributions of the number of pumps are shown as gray squares, with areas proportional to the posterior predictive mass. The observed data are shown to the left, with dots representing the median number of pumps, thicker solid lines representing the 0.25 and 0.75 quantiles, and thinner lines spanning the minimum and maximum observed number of pumps. The posterior predictive distributions generally match the observed data, suggesting that the model provides a reasonable account of people’s behavior.

Figure 1:

Posterior predictive distributions of the number of pumps for each participant in each condition, sorted by the mean number of pumps per participant in the p = 0.1 condition. The top panel corresponds to the condition with bursting probability p = 0.1, and the bottom panel corresponds to the condition with bursting probability p = 0.2. The posterior predictive distributions are shown as gray squares. The minimum and maximum, as well as the 0.25 and 0.75 quantiles, and the median of the behavioral data are shown to the immediate left in blue for the p = 0.1 condition and red for the p =0.2 condition.

3.3.3 Inferred Risk Propensity and Consistency

Figure 2 shows the inferred γ⁺ and β parameter values for four representative participants, together with a summary of their observed behavior. Each panel shows the distribution of the number of pumps that the participant made, excluding problems on which the balloon burst. The left column shows the condition with p = 0.1 and the right column shows the condition with p = 0.2.

Figure 2:

Observed behavior and inferred parameter values for four representative participants. The left column corresponds to the condition with bursting probability p = 0.1 and the right column corresponds to the condition with bursting probability p = 0.2. The distributions show the number of pumps each participant made before banking, excluding problems where the balloon burst. The inferred values of the γ⁺, β, and ω parameters are also shown.

Participant 1 can be seen to be consistently risk seeking. They choose to pump a relatively large number of times in both conditions. This pattern of behavior is captured by their risk and consistency parameters, with relatively high values of γ⁺ and β parameter values. Participant 2 is also risk seeking, in the sense that they generally pump a relatively large number of times across both conditions, but they do so less consistently. The number of times they pump in both conditions varies widely from 3 to more than 15 pumps. This behavior is quantified by their inferred parameter values, with relatively high values of γ⁺ but relatively low values of β. Participant 3 is consistently risk averse. They pump a relatively small number of times across both conditions and are very consistent in doing so. This is reflected in a relatively low γ⁺ and high β parameter values. Participant 4 is also risk averse, but is more inconsistent than Participant 3. This is captured with relatively low values of both γ⁺ and β parameter values .

Figure 3 shows the joint and marginal distributions of the posterior expectations of γ⁺ and β for all participants and for both conditions. The four representative participants shown from Figure 2 are labeled. It is evident that there is a wide range of individual differences in both risk propensity and consistency parameters. There appears to be a negative and nonlinear relationship between the two parameters in both conditions. Participants with relatively high values of γ⁺ also tend to have low values of β, and vice versa. Participants near the origin have low values of both γ⁺ and β, and are consequently both risk averse and inconsistent. However, as participants move from the origin closer to the lower-right corner, they become more risk seeking but continue to lack consistency. As participants move further away from the origin and closer to the top-left corner, they become consistently risk averse.

Figure 3:

Joint and marginal distributions of β and γ⁺ posterior expectations across the two conditions for each participant. The four representative participants shown from Figure 2 are labeled.

4.2 Cumulative Prospect Theory Model

Important cognitive models of how people choose between gambles include regret theory (Reference Loomes and SugdenLoomes & Sugden, 1982), decision-field theory (Reference Busemeyer and TownsendBusemeyer & Townsend, 1993), the priority heuristic (Reference Brandstätter, Gigerenzer and HertwigBrandstätter et al., 2006), anticipated utility theory (Reference QuigginQuiggin, 1982), and prospect theory (Reference Kahneman and TverskyKahneman & Tversky, 1979; Reference Tversky and KahnemanTversky & Kahneman, 1981). All of these models extend the standard economic account of choice as maximizing expected utility (Reference von Neumann and Morgensternvon Neumann & Morgenstern, 1947) and attempt to provide an account in terms of cognitive processes and parameters.

We use cumulative prospect theory (CPT), which makes a set of assumptions about how people subjectively weigh the value of outcomes and probabilities. CPT assumes that the outcomes of risky alternatives are evaluated relative to a reference point, so that outcomes can be framed in terms of losses and gains. In particular, it assumes that the same absolute value of a loss has a larger impact on the decision than a gain, consistent with the phenomenon of loss aversion (Reference Kahneman and TverskyKahneman & Tversky, 1979). In addition, prospect theory assumes that people subjectively represent probabilities, typically overestimating small probabilities and underestimating large probabilities.

We use a variant of the CPT model developed and implemented by Reference Nilsson, Rieskamp and WagenmakersNilsson et al. (2011). In this model, the expected utility of an alternative O is defined as

where u(·) defines the subjective utility of x _i.

This subjective utility is weighted by the probability p _i that the ith outcome occurs. According to the CPT model, if alternative O has two possible outcomes, then the subjective value V of O is defined as

where π(·) is a weighting function of the objective probabilities and v(·) is a function defining the subjective value of the ith outcome. The probability weighting function and the value function differ for gains and losses. The subjective value of payoff x is defined as

where 0 < α < 1 is a parameter that controls the curvature of the value function. Reference Nilsson, Rieskamp and WagenmakersNilsson et al. (2011) used different value functions for gains and losses. We use a simplification of the model in which the shape of the value function, determined by α, is the same for gains and losses. If λ > 1, losses carry more weight than gains, corresponding to the theoretical assumption of loss aversion. The larger the value of λ, the greater the relative emphasis given to losses. When 0 < λ < 1, in contrast, gains have more impact on the decision than losses. Although prospect theory expects loss aversion, we use a prior λ ∼ uniform (0,10 ) that tests this assumption.

The CPT model generates subjective probabilities by a weighting function which, for two possible outcomes, is defined as

where c = γ for gains and c = δ for losses. The parameter 0 < c < 1 determines the inverse S-shape transformation of the weighting function.

Finally, our CPT model allows for probabilistic decision making by assuming a choice rule in which choice probabilities are a monotonic function of the differences of the subjective values of the gambles. Specifically, the exponential Luce choice rule, rewritten as a logistic choice rule, assumes that the probability of choosing Gamble A over Gamble B is

The parameter φ can be interpreted as a measure of the consistency of choice behavior. When φ=0, the probability of choosing Gamble A over Gamble B becomes 1/2, and choice behavior is random. As φ increases, choice behavior becomes increasingly determined by the difference in subjective value between Gamble A and Gamble B. As φ→ ∞, choices become increasingly consistent in the underlying preference, until in the limit the preferred gamble is always chosen.

We use independent priors for all five parameters for each participant. Besides the prior λ ∼ uniform (0,10 ) already mentioned, the remaining parameters have priors α ∼ uniform (0,1), γ ∼ uniform(0,1), δ ∼ uniform (0,1), and φ ∼ gamma (2,1). Note that the final prior on the response consistency gives the highest density to φ=1, which corresponds to probability matching, while also allowing for more random or more deterministic behavior.

To measure risk propensity using the CPT model we focus on the loss aversion parameter λ. The motivation is that an individual who exhibits strong loss aversion can be interpreted as being risk averse, since their preference will be for gambles that avoid the possibility of a large loss. For the inference about loss aversion to be meaningful, there must be some level of behavioral consistency, and so we place a secondary focus on the φ parameter. We acknowledge that there are other ways in which the CPT model could be interpreted in terms of risk propensity. For example, if the probability weighting function infers that an individual perceives probabilities in extreme ways, significantly underestimating small probabilities and overestimating large probabilities, this could be seen as supporting a risky perception of the gambles. Alternatively, a lack of consistency in decision-making corresponds to a form of risk-seeking, but is more in line with erratic behavior than the underlying risk propensity trait we aim to measure.

4.3 Modeling Results

4.3.3 Inferred Subjective Value Functions

We found large individual differences in the subjective value functions and probability weighting functions that participants use. Figure 4 shows the inferred functions for a set of representative participants. In the left panel the first participant, shown by the dotted line, has a relatively high value of λ but a low value of α. Consequently, their subjective value curve significantly undervalues the magnitude of both gains and losses, but still shows loss aversion in the sense that the magnitude of losses are weighed more heavily than gains. The second participant, shown by the dashed line, has a relatively high value of both α and λ. This participant’s subjective value curve also undervalues the magnitude of both gains and losses, but shows strong loss aversion. The subjective magnitude of losses are much larger than gains. The third participant, shown by the solid line, has a relatively high value of α but λ is close to one. Consequently, the effect of undervaluing the magnitude of both gains and losses is weaker.

Figure 4:

Inferred subjective value function curves and probability weighting function curves for representative participants. The three participants in the left panel span the range of inferred individual differences in α and λ. The three participants in the right panel span the range of inferred individual differences in γ and δ.

The first participant in the right panel of Figure 4, shown by the dotted line, has relatively lower values of both γ and δ. Consequently, their weighting functions for both conditions overestimate smaller probabilities and underestimate larger probabilities. The second participant, shown by the dashed line, has relatively high values of both γ and δ. Their probability weighting functions are extremely close to the diagonal, which corresponds to good calibration. The third participant, shown by the solid line, has a relatively low value of γ but high value of δ. This participant significantly underestimates small probabilities and overestimates large probabilities.

Figure 5 shows the joint and marginal distributions of the posterior expectations of the loss aversion parameter λ as a measure of risk propensity, and the consistency parameter φ, over all participants. The representative participants from the left panel of Figure 4 are labeled. It is clear from that there is a range of inferred individual differences in both loss aversion and consistency. About one-third of the participants exhibit the opposite of loss aversion, with λ values below 1. About one-third of the participants exhibit relatively strong loss aversion with values of λ values over 1.5. All of the φ consistency parameters are inferred to be well above 0, as expected given the removal of guessing contaminants, but many are less consistent than probability matching.

Figure 5:

The joint and marginal distributions of the posterior expectations of the λ risk aversion and φ consistency parameters over all participants. The representative participants from the left panel of Figure 4 are labeled.

5.1 Theoretical Background

Optimal stopping problems are sequential decision-making tasks in which people must choose the best option from a sequence, under the constraint that an option can only be chosen when it is presented (Reference FergusonFerguson, 1989; Reference Gilbert and MostellerGilbert & Mosteller, 1966). These problems are sometimes called secretary problems, based on the analogy of interviewing a sequence of candidates for a job with the requirement that offers must be made immediately after an interview has finished, and before the next candidate is evaluated.

People’s behavior on optimal stopping problems has been widely studied in a variety of contexts, using a number of different versions of the task (Reference Bearden, Rapoport and MurphyBearden et al., 2006; Reference Christian and GriffithsChristian & Griffiths, 2016; Reference KogutKogut, 1990; Reference LeeLee, 2006; Reference Seale and RapoportSeale & Rapoport, 1997; Reference Seale and RapoportSeale & Rapoport, 2000). Some studies have used the classic rank-order version of the problem, in which only the rank of the current option relative to the options already seen is presented (Reference Seale and RapoportSeale & Rapoport, 1997; Reference Seale and RapoportSeale & Rapoport, 2000; Reference Bearden, Rapoport and MurphyBearden et al., 2006). Other studies have used the full-information version of the task, in which the values of the alternatives are presented (Reference Goldstein, McAfee, Suri and WrightGoldstein et al., 2020; Reference LeeLee, 2006; Reference Guan, Lee, Silva, Bello, Mcshane and ScassellatiGuan et al., 2014; Reference Guan, Lee and VandekerckhoveGuan et al., 2015; Reference ShuShu, 2008). For both of these versions there are known optimal solution processes to which people’s performance can be compared (Reference FergusonFerguson, 1989; Reference Gilbert and MostellerGilbert & Mosteller, 1966).

We use the full-information version of the problem, for which the optimal solution is to choose the first number that is both currently maximal and above a threshold that depends upon the position in the sequence. The values of the optimal thresholds also depend on two properties of the problem. One is the number of options in the sequence, known as the length of the problem. Intuitively, the more options a problem has, the higher thresholds should be, especially early in the sequence. The second property is the distribution from which values of the options are chosen, known as the environment distribution. Intuitively, distributions that generate many large values require setting higher thresholds, while distributions that generate many small values require setting lower thresholds.

5.3 Bias-From-Optimal Model

Previous work modeling decision making in optimal stopping problems has found evidence that people use a series of thresholds to make decisions, and that there are large individual differences in thresholds (Reference Goldstein, McAfee, Suri and WrightGoldstein et al., 2020; Reference Guan, Lee, Silva, Bello, Mcshane and ScassellatiGuan et al., 2014; Reference Guan and LeeGuan & Lee, 2018; Reference LeeLee, 2006). A surprising but reliable finding is that, beyond the initial few problems in an environment (Reference Goldstein, McAfee, Suri and WrightGoldstein et al., 2020), there is relatively little learning or adjustment of thresholds (Reference Baumann, Singmann, Kaxiras, Gershman, von Helversen, Kalish, Rau, Zhu and RogersBaumann et al., 2018; Reference Campbell, Lee and SunCampbell & Lee, 2006; Reference Guan, Lee, Silva, Bello, Mcshane and ScassellatiGuan et al., 2014; Reference LeeLee, 2006). This justifies modeling an individual’s decisions in terms of the same set of thresholds being applied to all of the problems.

We use the previously-developed Bias-From-Optimal (BFO) model to characterize the thresholds people use. (Reference Guan, Lee and VandekerckhoveGuan et al., 2015). The BFO model represents the thresholds an individual uses in terms of how strongly they deviate from the optimal thresholds for the problem length and environmental distribution. We denote the optimal thresholds as for a problem of length m (Reference Gilbert and MostellerGilbert & Mosteller, 1966, Table 2). Naturally, the last threshold in the sequence must be 0 since the last value must be chosen. The ith participant’s thresholds depend on a parameter β_i^m∼Gaussian(0,1) that determines how far above or below their threshold is from optimal, and a parameter γ_i^m∼Gaussian(0,1) that determines how much their bias increases or decreases as the sequence progresses. Formally, under the BFO model, the ith participant’s kth threshold in a problem of length m is

Table 2:Results of the CLVM analysis. DIC = deviance information criterion. ΔDIC measures difference in DIC to the best-performed “Surveys, BART β” model.

for the first m − 1 positions, and τ^m_im = 0 for the last. The link functions Φ(·) and Φ⁻¹(·) are the Gaussian cumulative distribution and inverse cumulative distribution functions, respectively.

According to the BFO model, the probability that the ith participant will choose the value they are presented in the kth position on their jth problem is

for the first m positions and

for the last position. The parameter α_i^m ∼ uniform(0,1) is the individual-level accuracy of execution that corresponds to how often the deterministic threshold model is followed (Reference Guan, Lee, Silva, Bello, Mcshane and ScassellatiGuan et al., 2014; Reference Rieskamp and OttoRieskamp & Otto, 2006).

Figure 6 shows how the shape of threshold functions changes with different values of β and γ, as compared to the optimal decision threshold for a problem of length eight in the neutral environment. The optimal threshold corresponds to the case with β = 0 and γ = 0, and is shown in bold. The β parameter represents a shifting bias from this optimal curve, with positive values resulting in thresholds that are above optimal and negative values resulting in thresholds that are below optimal. The γ parameter represents how quickly thresholds are reduced throughout the problem sequence, relative to the optimal rate of reduction. Positive values of γ produce thresholds that drop too slowly, while negative values of γ produce thresholds that drop too quickly. Priors are placed on the two risk parameters and consistency parameter for each participant so that γ, β ∼ Gaussian(0, 1) and α ∼ uniform (0, 1).

Figure 6:

The thresholds produced by the bias-from-optimal threshold model under different parameterizations of β and γ. The optimal threshold, corresponding to β = γ = 0, is also shown.

Our decision to use the BFO model was based on the direct interpretability of its parameters in terms of risk propensity. It is an unrealistic model of the cognitive processes involved in optimal stopping problem decisions, because it assumes perfect knowledge of the optimal thresholds, which are difficult to derive and compute. Alternative models based on fixed and linearly decreasing thresholds provide more realistic cognitive processing accounts (Baumann et al., in press; Reference Goldstein, McAfee, Suri and WrightGoldstein et al., 2020; Reference LeeLee, 2006; Lee & Courey, in press). The BFO model is better interpreted as a measurement model, with the β and γ parameters quantifying how a set of thresholds are more or less risky than optimal.

One interpretation is that higher thresholds that require higher values represent risk seeking and lower thresholds represent risk aversion. Larger values β increase thresholds, and larger values of γ maintain higher thresholds for longer. Under this interpretation larger values of β and γ correspond to greater risk propensity. In contrast, smaller values of β and γ both lead to lower thresholds over the course of the sequence and correspond to lower risk propensity.

5.4 Modeling Results

Before applying the BFO model, we checked that there was no clear evidence of learning or adaptation. As discussed above, this is a basic empirical pre-condition for the application of threshold models. Figure 7 shows the performance of participants, measured by the proportion of problems for which they correctly chose the maximum. The problems were split into four blocks of 10 problems each. In the two length-four conditions mean performance is between about 0.5 and 0.6. In the two length-eight conditions mean performance is between about 0.3 and 0.5. Participant performance is better in the shorter problems, but there do not appear to be large differences in performance between the neutral and plentiful environments. These results do not suggest there is any significant learning or adaptation.

Figure 7:

Mean proportion correct over all participants on successive blocks of 10 problems for the four different optimal stopping conditions.

5.4.3 Inferred Thresholds

Figure 8 shows the marginal posterior expectations for all the inferred thresholds under all four conditions for all of the participants. The optimal decision threshold in each condition is also shown as a solid black line. It is clear that participants are generally sensitive to both length of the problem and the environmental distribution from which values are drawn. The thresholds in the plentiful environment conditions are relatively higher than the thresholds in the neutral environment conditions. The thresholds in the length-eight conditions remain higher longer into the sequence than the thresholds in the length-four conditions. Interestingly, it appears that participants in the length-eight conditions tend to use thresholds that are lower than optimal in both environments, and especially so in the plentiful environment.

Figure 8:

The inferred thresholds for all participants in the optimal stopping conditions corresponding to the length-four neutral environment (top-left), the length-four plentiful environment (top-right), the length-eight neutral environment (bottom-left), and the length-eight plentiful environment (bottom-right). Two representative participants are shown by the dashed and dotted lines.

It is also clear that there are individual differences in thresholds in all four conditions. Two participants are highlighted by dotted and dashed lines in Figure 8, showing their inferred thresholds in all four conditions. These participants were chosen because they show very different patterns of risk propensity in terms of their thresholds. The participant represented by the dotted lines can be seen to be risk seeking, because their thresholds for all four conditions are much higher than optimal. The participant starts their threshold high and maintains it at a high level as the sequence progresses. This risk-seeking behavior is quantified by their β and γ parameter values, which are both positive and relatively large. Conversely, the participant represented by the dashed lines can be seen to be risk averse, because their thresholds are much lower than optimal in all four conditions. The participant starts their threshold low and lowers it quickly as the sequence progresses. This risk-averse behavior is also quantified by their large negative β and γ parameter values.

Figure 9 summarizes the individual differences across all participants for all four conditions. The posterior expectations of the β and γ risk parameters are shown jointly in the scatter-plot in the center panel, and their marginal distributions are shown as histograms on the bottom and left margins. The two participants highlighted in Figure 8 are labeled in the joint distribution. The dotted lines represent where β and γ are equal to 0. Where the dotted lines meet in the center represents the optimal threshold. It is clear that there is a wide range of both quantitative and qualitative individual differences in risk propensity, because all four quadrants around optimality are populated.

Figure 9:

The joint and marginal distributions of the posterior expectations of the β and γ parameters, across the four conditions for all of the participants. The risk-seeking and risk-averse participants from Figure 8 are labeled.

6.1 Theoretical Background

Bandit problems are widely used to study human decision making under risk and uncertainty (Reference Banks, Olson and PorterBanks et al., 1997; Reference Daw, O’doherty, Dayan, Seymour and DolanDaw et al., 2006; Reference Meyer and ShiMeyer & Shi, 1995; Reference Lee, Zhang, Munro and SteyversLee et al., 2011). In bandit problems, people must choose repeatedly between a set of alternatives. Each alternative has a fixed reward rate that is unknown to the decision maker, and each time it is chosen this probability is used to generate either a reward or a failure. The goal is to maximize the total number of rewards over the sequence of decisions. Bandit problems are psychologically interesting because they require that the exploration of new good alternatives be balanced with the exploitation of good existing alternatives (Reference MehlhornMehlhorn et al., 2015). People generally start by exploring the different available alternatives before shifting to exploit the alternative with the highest reward rate.

Bandit problems can differ in terms of how many alternatives are available and in terms of how many decisions are made within a problem. In infinite-horizon bandit problems the total number of decisions to be made is not known in advance, but there is some probability that the problem stops after any decision. In finite-horizon bandit problems the total number of decisions to be made within a problem is fixed and known in advance. This corresponds to the length of a problem. Bandit problems can also differ in terms of the distributions of reward rates that underlie each alternative. This distribution corresponds to the environment for the problem.

6.3 Extended Win-Stay Lose-Shift Model

There are many different models of human decision making on bandit problems, including the є-greedy, є-decreasing and the τ-first model (Reference Sutton and BartoSutton & Barto, 1998). We use a variant of perhaps the simplest and most widely used model, known as win-stay lose-shift (WSLS: Reference RobbinsRobbins, 1952; Reference Sutton and BartoSutton & Barto, 1998). In its deterministic form, this model assumes that people stay with the most recently-chosen alternative if it provides a reward, but shift to another alternative if it does not. In the standard stochastic version of the WSLS strategy, there is a probability γ of following this rule for every decision.

In our extended WSLS model there is a probability γ_w of staying after a reward and a potentially different probability γ_l of shifting after a failure. This WSLS model allows there to be a psychological difference between reacting to reward and failure in the decision-making process. This model has been found to account well for people’s behavior (Reference Lee, Zhang, Munro and SteyversLee et al., 2011; Reference Zhang, Lee, Catrambone and OhlssonZhang & Lee, 2010a).

The extended WSLS model does not require memory of previous actions and outcomes beyond the immediately preceding trial. It is also insensitive to whether the horizon is an infinite or finite. Despite this simplicity, it provides a measure of risk propensity. A person who is risk seeking is likely to shift to another alternative with a high probability following a failure, in order to explore the other available options. In contrast, a person who is risk averse is likely to shift to another alternative with a relatively lower probability following a failure.

We represent the behavioral data as y _ijk = 1 if the ith participant chose the left alternative on the kth trial of their jth problem, and y _ijk = 0 if they chose the right option. The extended WSLS model assumes the probability of choosing the left alternative is

where r _ij(k−1) = 1 if the previously selected alternative resulted in a reward, and r _ij(k−1) = 0 if the previously selected alternative resulted in a failure. The observed rewards and failures on each trial r _ijk are generated by r _ijk ∼ Bernoulli(p ^x), where p ^left and p ^right are the reward rates for the two alternatives. These reward rates are generated from either the neutral or plentiful environment. The behavioral data are modeled as y _ijk∼Bernoulli(θ_ijk). Finally, our model uses the priors γ_w, γ_l ∼ uniform(0,1).

6.4 Modeling Results

6.4.3 Inferred Win-Stay Lose-Shift Probabilities

Figure 10 shows the numbers of shifts following rewards and failures across positions for four representative participants. These participants were chosen because they span the range of inferred individual differences. The left panels show the length-eight conditions while the right panels show the length-16 conditions. The numbers of shifts following failure are shown in blue for the neutral condition, and in green for the plentiful condition, while the numbers of shifts following reward are shown in gray. In all four conditions, Participant 1 shifts relatively often after a failure but rarely after a reward. Participant 2 almost never shifts, either following a reward or a failure. Participant 3 shifts relatively more often following failure than Participant 2, but also shifts sometimes following a reward. Participant 4 shifts moderately often following both reward and failure for early decisions in the sequence, but shifts less often as the sequence progresses. The inferred γ_w and γ_l parameters for each participant in the the neutral and plentiful conditions are also shown, and correspond to the observed staying and shifting behavior.

Figure 10:

The number of shifts following reward versus failure for four representative participants in each of the four conditions. The left panels show the length-eight conditions while the right panels show the length-16 conditions. The numbers of shifts following failure are shown in blue for the neutral condition, and in green for the plentiful condition. The numbers of shifts following reward are shown in gray. The inferred γ_w and γ_l parameters for each participant in the the neutral and plentiful conditions are also shown.

Figure 11 shows the joint and marginal distributions of the posterior means of the γ^w and γ^l for each participant, for all four conditions. The four representative participants from Figure 10 are labeled. It is clear that the γ^w and γ^l parameters capture the consistent differences in their behavior observed in Figure 10. For example, Participant 1, who almost always stays after a reward and shifts after a failure, is consistently in the top right of the scatter plot, corresponding to high values of both the γ^w and γ^l parameters. In contrast, Participant 2, who rarely switches, is consistently located in the bottom right of the scatter plot, corresponding to a high value of the γ^w parameter and a small value of the γ^l parameter.

Figure 11:

Joint and marginal distributions of the means of the γ^w and γ^l posterior expectations across the four conditions for each participant. The four representative participants shown from Figure 10 are labeled.

Overall, it is clear that there is a range of individual differences in both win-stay and lose-shift probabilities, and that there is a negative relationship between the two parameters. Participants who tend to stay following a reward also tend to stay following a failure. Participants who shift relatively more even after a reward also tend to explore the other alternative after a failure.

7.1 Risk Propensity Scale

The Risk Propensity Scale (RPS) was designed to be a short and easily administered test for measuring general risk-taking tendencies. The RPS originally consisted of only nine items, from which two items were later removed. The version of the RPS we use consists of the seven remaining items. All of the items involve statements that are rated on a nine-point scale ranging from “totally disagree” to “totally agree,” except for the last item, which involves a nine-point rating from “risk avoider” to “risk seeker.” Items 1, 2, 3, and 5 were reverse-scored so that high scores represented high risk propensity. Reference Meertens and LionMeertens & Lion (2008) reported an internal reliability coefficient measured by Cronbach’s α of 0.77.

Participants indicated their selection by checking the appropriate box under each number. To obtain an overall RPS score for each participant, the mean of the seven items was taken. The left panel of Figure 12 shows the distribution of RPS scores across all 56 participants. The RPS scores are right-skewed ranging from 1 to 8.14, with M = 3.61 and SD = 1.86. These results are different from Reference Meertens and LionMeertens & Lion (2008), who reported a mean score of 4.63 and standard deviation of 1.23. The Cronbach’s α observed in this sample of 56 participants was 0.90.

Figure 12:

The distributions of questionnaire-based measures of risk. The left panel shows the joint and marginal distributions of RPS and RTI scores. The right panel shows the joint and marginal distributions of the DOSPERT risk taking and risk perception scores.

7.2 Domain Specific Risk Taking Scale

The Domain Specific Risk Taking scale (DOSPERT) was originally developed by Weber et al. (2002) and later revised by Reference Blais and WeberBlais & Weber, 2006) to be shorter and more broadly applicable. The original version was revised from 40 items down to 30 items, evaluating risky behavioral intentions originating from five domains: ethical, financial, health/safety, social, and recreational risks. Each domain involves six items.

The DOSPERT differs from the RPS and RTI in that it attempts to distinguish people’s tendency to be risk seeking from people’s perception of risk. Reference Blais and WeberBlais & Weber (2006) found a negative relationship between the two; people who tend to engage in more risk seeking behavior also tend to perceive situations as less risky, and vice versa. Therefore, the DOSPERT is split into two assessments, separating risk taking from risk perception. Participants rated each of the 30 statements in terms of self-reported likelihood of engaging in risky behaviors to measure risk taking, and in terms of their gut-level assessment of the riskiness of these behaviors to measure risk perception. In the risk-taking assessment, a seven-point rating scale was used, ranging from “extremely unlikely” to “extremely likely.” In the risk-perception assessment, a seven-point rating scale was used ranging from “not at all risky” to “extremely risky”.

Participants indicated their selection by checking the appropriate box under each number. Ratings were summed across all items of each domain to obtain five subscale scores for risk taking and five subscale scores for risk perception. The overall DOSPERT risk taking score is the mean of each subscale score for the risk taking assessment. Similarly, the overall DOSPERT risk perception score is the mean of each subscale score for the risk perception assessment. Therefore, each of the scores can potentially range from 6 to 42, where higher scores indicate higher risk propensity. Reference Blais and WeberBlais & Weber (2006) reported Cronbach’s α’s ranging from 0.71 to 0.86 for the risk-taking scores, and Cronbach’s α values ranging from 0.74 to 0.83 for the risk-perception scores.

The right panel of Figure 12 shows the relationship between the risking taking and risk perception scores from the DOSPERT across all 56 participants, along with the marginal distributions of each. The risk taking scores also appear to be slightly bi-modal, with a large group of participants centered around about 16–18 and then a smaller group near 30. Risk perception scores are unimodal and centered around 27. These results are consistent with the findings from Reference Blais and WeberBlais & Weber (2006), in the sense that there is a negative relationship between risk taking and risk perception scores (r = −0.22). The Cronbach’s α observed in this sample of 56 participants was 0.92 for the overall risk-taking score, and 0.92 for the overall risk-perception score.

8.1 Cognitive Task Overview

Table 1 provides an overview of the four decision-making tasks, models, and relevant parameters. The BART has two risk parameters, γ⁺_1:2, and two consistency parameters, β_1:2. The gambling task has one risk parameter, λ, and one consistency parameter, φ. The optimal stopping task has eight risk parameters, β_1:4 and γ_1:4, and four consistency parameters, α_1:4. The bandit task has four risk parameters, γ^l_1:4, and four consistency parameters, γ^w_1:4. In total, there are 26 relevant parameters from the decision-making tasks to be compared within and across tasks for each individual.

8.2 Estimating Correlations with Uncertainty

The correlations between each risk and consistency parameter from all four decision-making tasks were estimated, using a Bayesian approach, based on Reference Lee and WagenmakersLee & Wagenmakers (2013), Chap. 5. A key feature of this approach is that it incorporates uncertainty in the inferences of the parameters themselves (Reference Matzke, Ly, Selker, Weeda, Scheibehenne, Lee and WagenmakersMatzke et al., 2017). That is, we do not use point estimates of the various risk and consistency parameters, but instead acknowledge that participant’s behavior is consistent with a range of possible values, given the limited behavioral data. Our inferences about the correlations between parameters are thus sensitive to the precision with which their values are determined from the cognitive models and decision-making tasks we used.

Formally, for each pair of parameters, we correlate a set of samples for the ith participant, rather than just a single best estimate for each participant. These samples are generated by assuming Gaussian marginal posterior distributions

where y_i = (y _i1, y _i2) represents the latent true value of the parameters, and denotes the precision of the inference about them. The precisions are estimated as the standard deviations of the marginal posterior distributions from the inferences of the decision-making models. The correlation focuses on the latent true values of the cognitive measures, by modeling them as a draw from a multivariate Gaussian distribution

Our hierarchical correlation model uses the following priors on r, σ₁, σ₂, µ₁, and µ₂:

The correlation analysis was implemented as a Bayesian graphical model in JAGS. It was applied independently to all possible parameter combinations, inferring the posterior distribution of the correlation coefficient in each case. We generally use the posterior mean as a summary of the inference, but also use the Savage-Dickey method (Reference Wetzels, Grasman and WagenmakersWetzels et al., 2010) to estimate Bayes factors to compare the hypotheses of correlation and no correlation.

An advantage of Bayesian analysis is that it can find evidence in favor of a null hypothesis such as no correlation. Whereas null hypothesis significance testing can either find evidence for a correlation, or fail to find evidence for a correlation, the Bayesian analysis can produce three outcomes. These possible outcomes are evidence for a correlation, evidence for the absence or a correlation, or no strong evidence for either possibility. This is important in evaluating whether the data contain enough information to make meaningful claims about the correlations. To the extent that the Bayes factors provide evidence in favor of either the presence or absence or correlations, the data can be considered sufficiently powerful to have answered the research question. Evidence for the data being insufficient would be provided by Bayes factors that provide no strong evidence in either direction.

8.3 Correlation Results

Combining the scores from the three questionnaires to the parameters from the four decision-making tasks gives a total of 30 risk and consistency measures to be compared, which leads to 435 pairwise correlations. Figure 14 shows the results for all of these correlations. The dashed lines divide the grid into the three questionnaires and four decision-making tasks. The circles indicate parameter pairs for which the Bayes factor provides evidence of a correlation. We used a cutoff of 3 for the Bayes factor, because it is a standard boundary corresponding what is variously labeled “substantial” (Reference De Martino, Kumaran, Seymour and DolanJeffreys, 1961), “positive” (Reference Kass and RafteryKass & Raftery, 1995), and “moderate” (Reference Lee and WagenmakersLee & Wagenmakers, 2013) evidence.Footnote ¹ The areas of the circles correspond to the magnitudes of the correlations, given by the posterior expectation of r. Blue circles indicate that a correlation is positive while red circles indicate that a correlation is negative. Meanwhile, the cross markers correspond to those comparisons where Bayes factor was at least 3 in favor of the null hypothesis of no correlation.

Figure 14:

Correlation matrix of the risk and consistency parameters. Blue circles represent positive correlations for which the Bayes factor provided at least moderate evidence, while red circles represent negative correlations for which the Bayes factor provided at least moderate evidence. The areas of the circles correspond to the absolute values of the posterior expectation of the correlation r. Cross markers indicate that the Bayes factor provided at least moderate evidence for the absence of a correlation. The parameters within tasks are identified by the dashed gray lines. OS α represents consistency in the optimal stopping problem and OS β and γ represent risk in the optimal stopping problem. Bandit γ^w represents consistency in the bandit task and Bandit γ^l represents risk. Bart γ⁺ represents risk in the BART task and Bart β represents consistency. Gamble φ represents consistency and Gamble λ represents risk.

It is clear that there are positive correlations between the same parameters within tasks. For example, all of the consistency parameters across conditions from optimal stopping are highly correlated, as are the risk parameters within the BART. This is clear from the patterns of blue circles along the diagonal. The positive correlations across conditions within the same task are expected, given the stability we observed in representative participants across conditions in the decision-making task analyses. Furthermore, the RTI, RPS, and DOSPERT RT are also positively correlated with each other, replicating previous findings.

There also appear to be some negative correlations between different parameters within tasks. For example, the γ^w and γ^l parameters in the bandit task are negatively correlated with each other, and the risk and consistency parameters in the BART are also negatively correlated. As we noted in the task-specific analyses, there is some trade-off between parameters for some of these tasks.

There appears, however, to be less evidence for systematic correlations across tasks. Indeed, there is generally evidence for a lack of correlation between parameters from different tasks, and between cognitive parameters and the questionnaire measures. The one exception relates to the gambling tasks parameters, for which there is no evidence for or against correlations with other cognitive parameters and questionnaire measures. This result likely reflects a failure of the experimental design to measure the risk aversion and consistency parameters with enough precision. In contrast, the results in Figure 14 show that there is enough information to make inferences, either in favor or against the presence of a correlation, for all of the other cognitive parameters and questionnaire measures. This finding speaks directly to the adequacy of the data to address the main research question about correlations between model parameters and questionnaire measures.

Figure 15 provides a different presentation of the correlation analysis that focuses on the comparisons for which there is evidence for correlations. Only pairs of parameters or measures with Bayes factors greater than 10 in favor of the alternative model are considered in this analysis, to focus on those pairs for which the evidence of correlation is strongest. The left panel shows the 95% Bayesian credible intervals of r for each comparison. The right panel shows the log Bayes factors for the corresponding comparisons. The strong positive positive correlations between the same cognitive parameters across different conditions within tasks are clear, as are the trade-offs between different parameters within tasks, shown by the strong negative correlations.

Figure 15:

95% Bayesian credible intervals (left panel) and Bayes factors (right panel) for Pearson’s correlation coefficient r for parameter pairs with strong evidence of a correlation. Positive correlations are shown in blue and negative correlations are shown in red.

9.1 Theory-based models

The first theoretical model is the “general risk” model. It has one latent factor for each cognitive model parameter, and combines their independent replication across experimental conditions. For example, with respect to the optimal stopping model, there is one factor for all four of the α error-of-execution parameters applied to the four experimental conditions, one factor for all of the β bias parameters, and one factor for all of the γ decrease parameter. The same separation and grouping of parameters applies to the other cognitive models. In addition, the general risk model has a general factor that all parameters share and is assumed to correlate with the risk surveys. The theoretical motivation for this model is based on the possibility that there is a general factor, which can be conceived as a risk propensity equivalent to the general intelligence factor “g” from cognitive abilities and psychometric testing. The general risk model emphasizes this general factor, while also allowing for the uniqueness of the cognitive tasks.

The left panel of Figure 16 details the structure of the general risk model. Rows represent the cognitive model parameters and questionnaire measures and columns represent the assumed factors. Dark blue squares indicate that a parameter or measure is assumed to load on a factor. Light yellow squares indicate that some level of association is possible. Empty squares assume a lack of association. Thus, the first factor has dark blue squares for the questionnaire measures, since these are assumed to index general risk, and light yellow squares for the cognitive parameters, allowing for the possibility they may also index risk. The remainder of the model structure loads each cognitive parameter in each task on a separate factor.

Figure 16:

The factor loadings structure for the general risk (left panel), two-factor (middle panel), and three-factor (right panel) theory-based CLVMs. In each panel, rows correspond to cognitive model parameters and questionnaire measures, and columns correspond to model factors. Dark blue squares indicate an assumed association between a factor and a parameter or measure. Light yellow squares indicate a possible association, to be estimated. Empty squares indicate an assumed lack of association.

The second theoretical model is the “two-factor” model. It is a simpler model, with only two latent factors. The middle panel of Figure 16 details the structure of this model. One factor corresponds to risk propensity and the other corresponds to behavioral consistency. The risk propensity factor loads on the specific cognitive model parameters we interpret as controlling risk propensity in the tasks. These are the β bias and γ decrease parameters in the optimal stopping model, the γ^l lose-shift parameters in the extended-WSLS model, the γ risk propensity parameter in the BART model, and the λ loss aversion parameter in the cumulative prospect theory model. It also loads on the risk measures produced by the four questionnaires. The behavioral consistency factor loads on the other cognitive model parameters, which control the error of execution and response determinism within the models.

The third theoretical model is the “three-factor” model. It is detailed in the right panel of Figure 16. The three-factor model is an extension of two-factor model that loads the four questionnaire measures on a separate third factor, rather than on the risk propensity factor. This model was included to test the possibility of a difference between behavioral risk taking, as potentially expressed in the cognitive tasks, and self-reported risk taking, as measured by the questionnaires.

9.2 Exploratory models

The exploratory models were constructed based on inspection of the correlation analyses presented in Figure 14. We measure the performance of these models relative to two bookend models. The first bookend is the “unitary” model, which has a single latent factor for all cognitive model parameters and questionnaire measures. It is a very simple CLVM account of the data that provides a lower bound on the goodness-of-fit that can be achieved. The other bookend is the “saturated model”, which has one latent factor for each of the 30 cognitive model parameters and questionnaire measures. It is the most complicated CVLM account of the data. It provides an upper bound on the goodness-of-fit. The role of bookend models is to provide comparison points for substantively interesting models. A useful substantive model should outperform both bookends in terms of a model evaluation measure that balances goodness-of-fit and complexity. In addition, requiring substantive models to outperform the saturated model provides confidence that they are descriptively adequate, because their balance between goodness-of-fit and complexity is better than an account that has high descriptive adequacy. We use the Deviance Information Criterion (DIC: Reference Spiegelhalter, Best, Carlin and van der LindeSpiegelhalter et al., 2002; Reference Spiegelhalter, Best, Carlin and LindeSpiegelhalter et al., 2014), which has theoretical limitations, but provides a useful practical measurement for a coarse-grained assessment of competing models.

The first exploratory model we found is the “questionnaires only” model. It simplifies the saturated model by assuming that a single latent factor underlies all four questionnaire measures, but that the cognitive model parameters continue to have their own factors. The second exploratory model is the “BART β” model. It simplifies the saturated model by assuming that a single latent factor underlies the two Bart β parameters. Finally, the “questionnaires and BART β” model combines the constraints of the first two exploratory models, so that a single factor underlies all of the four questionnaires and the BART β parameters.

9.3 CVLM results

Table 2 summarizes the results of the CLVM analysis. According to the DIC measure, none of the theory-based models performed well. The exploratory models lead to slight improvements. We could not find any other CLVM that improved on the unitary and saturated bookend models. These results are largely consistent with the results of the correlational analysis above: There is not much evidence for a jointly explanatory underlying structure between the cognitive tasks. Even within tasks, the CLVM analysis provides evidence for models with multiple underlying dimensions per task. Perhaps the most interesting exploratory finding from the CLVM analysis is that it is the BART task, and its associated cognitive model parameter measuring behavioral consistency, that most closely aligns with the measure of risk produced by the questionnaires.