Social Decisions from Description Compared to Experience Rely on Different Cognitive and Neural Processes


 Social decisions reveal the degree to which people consider societal needs relative to their own desires. Although many studies showed how social decisions are taken when the consequences of actions are given as explicit information, little is known about how social choices are made when the relevant information was learned through repeated experience. Here, we compared how these two different ways of learning about the value of alternatives (description versus experience) impact social decisions in 147 healthy young adult humans. Using diffusion decision models, we show that, although participants chose similar outcomes across the learning conditions, they sampled and processed information differently. During description decisions, information sampling depended on both chosen and foregone rewards for self and society, while during experience decisions sampling was proportional to chosen outcomes only. Our behavioral data indicate that description choices involved the active processing of more information. Additionally, neuroimaging data from 40 participants showed that the brain activity was more closely associated with the information sampling process during description relative to experience decisions. Overall, our work indicates that the cognitive and neural mechanisms of social decision making depend strongly on how the values of alternatives were learned in addition to individual social preferences.


Introduction
Social preferences are re ect how much individuals care about others relative to themselves [1][2][3][4][5][6][7] . These preferences are important at the individual, organization, and societal levels. For example, they may in uence local acts to help one's neighbor as well as global efforts to reduce carbon emissions or decrease societal inequality. However, there is still much to discover about how social preferences are learned and how they are shaped by the decision context. Previous work examining human social decisions has focused mostly on how explicitly described tradeoffs between sel shness and prosociality are represented and resolved at the behavioral and neural levels 8- 10 . This line of work has examined how factors such as cognitive load, time pressure or delays, priming and nudging, age, training, competition/cooperation, or social value orientation in uence the neural representation of social information and outcomes of social decisions [11][12][13][14][15][16][17][18] . These previous studies of human social preferences have focused almost exclusively on decisions made on the basis of explicit information given at the time of choice, about the costs and bene ts to self or others [19][20][21][22][23][24] . However, experiments in non-human animals have shown that social factors also in uence choices between highly trained stimuli that were learned through repeated experience 25,26 . Thus far, the potential behavioral and neural distinctions between social choices based on information learned through experience versus concurrent descriptions are unknown.
Decisions from experience and description differ in the way individuals learn about the value or utility the options. As the names indicate, in description decisions the information about potential outcomes is displayed and described explicitly at or near the time of choice, while in experience decisions stimulusoutcome associations were learned through past experience in the absence of explicit descriptions of potential outcomes 27,28 . Humans often learn about the world through experience, without ever receiving any explicit instructions or descriptions [29][30][31][32] . They way humans learn about the choice options can in uence the decisions they make. For example, risk preferences are expressed differently in description relative to experience-based decisions, speci cally individuals underweight rare outcomes when decisions are made based on past experience relative to description 29,33 . In addition, two functional magnetic resonance imaging (fMRI) studies have shown that decisions over individual rewards whose subjective values were previously learned through extensive training compared to decisions based on planning of the potential outcomes show different BOLD activity patterns in the caudate versus putamen and medial versus more lateral regions of ventral prefrontal cortex 34,35 . However, other work using rewards learned through experience (e.g., familiar snack foods or conditioned stimuli) has shown very similar BOLD activity patterns in the striatum, prefrontal cortex, and posterior cingulate cortex between familiar, experienced stimuli and decisions based on concurrently described alternatives within and across participants [36][37][38][39][40][41] . Thus, it is not yet clear how and when behavior and/or brain activity during social decision making differs between experience and description contexts. Therefore, it is important to understand how the processes underlying social decisions may differ as a function of the ways in which the values of the rewards for self and others were learned.
In this study, we used a social decision task together with fMRI to investigate individuals' behavior and brain activity in description and experience-based choices. The participants made decisions involving tradeoffs between monetary rewards for the self and a charity of their choosing (i.e., society). The amounts available for self and society were learned through either concurrent description or over three days of experience training (Description or Experience trials). The social tradeoffs underlying each decision problem were matched across the description and experience trials. We t hierarchical diffusion decision models (HDDM) 42 to the description and experience decisions to determine how information about individual and social rewards was used during the decision process. Our hypothesis was that the evaluation and accumulation of subjective option values and value differences would differ when the information about rewards was learned from description relative to experience. Lastly, we used fMRI to test if activity in the striatum, prefrontal cortex, posterior cingulate cortex or other key decision-related brain regions differed in description versus experience trials.
We found that, even though participants chose the same outcomes in the two conditions, the information that individuals used to make decisions from experience or description, differed. Evidence accumulation in description decisions was based on the chosen and foregone payoffs to self and society. In contrast, experience decisions were based only the chosen self-payoff and societal payoffs. Furthermore, description decisions exhibited increased brain activity in many regions compared to experience decisions, consistent with more extensive information processing of both chosen and unchosen outcome in description trials. Overall, we found that behavioral and neural mechanisms underlying social choices depend on how the value of the decisions was learned.

Results
We tested behavior and brain activity during a social decision task in which individuals faced the same underlying tradeoffs between options, but learned about these tradeoffs through past experience or concurrent description. In each condition, individuals made tradeoff decisions between either maximization of self-pro ts or societal-bene ts (FIGURE 1). We de ne self-pro t (society-bene t) maximizing decisions as those in which the individual receives more (less) points than society. In addition to the decision task, we also measured social preferences using the Social Value Orientation scale (SVO), which gives the degree of a person's prosociality angle with lower values indicating more sel sh preferences 46 .

Prosocial choices in description and experience trials
We rst tested the outcomes of social decisions as a function of prosocial preferences. We computed a hierarchical Bayesian beta regression model that sought to explain the proportion of prosocial choices each participant made as a function of the condition (either description (DE) or experience (EX) trials), controlling for their prosociality, as determined by the SVO scale. Prosocial choices in the description and experience trials did not differ (Bayes Factor 10 = 0.06; regression coe cient = -0.02 95% credible interval (CI) = [-0.13, 0.08]; see Equation 1). According to scores on the independent SVO scale, most participants had moderately sel sh preferences, but there was considerable variability in prosocial preferences across participants (FIGURE 2B). As expected prosocial choices in the decision task increased signi cantly as a However, the relationship between SVO and prosocial choices did not differ between description and experience choices (Bayes Factor in favor of a regression model without an SVO*condition = 18). In summary, the regression results indicate that decision outcomes did not differ between description and experience trials in the speci c sets of social tradeoffs we tested here.

Response times in experience versus description decisions
We also compared response times across conditions and found that they were faster in experience relative to description trials. A hierarchical Bayesian linear regression explaining the natural logarithm of response times as a function of condition and SVO showed that the mean response times were faster in experience than description trials (EX mean = 0.98 ± 0. However, there was also an interaction between SVO and condition such that response times were less strongly related to SVO in experience compared to description trials (coef = -0.05, 95% CI = [-0.07, -0.03], PP mcmc > 0.999). These results indicate that highly prosocial participants required more time than less prosocial participants to resolve the tradeoffs between self-pro t and society bene ts they faced in this task, especially if the tradeoffs were presented as explicit descriptions rather than learned through experience.

Information sampling in description and experience decisions
We t hierarchical diffusion decision models (HDDM) [47][48][49] to test how the chosen and foregone payoffs to self and society in uenced the decision process in description relative to experience trials. We t and compared two types of models that differed in how the chosen and unchosen payoffs to self and society in uenced the evidence accumulation or drift rate (see Methods section on HDDM). The best-tting HDDM parameters were able to explain the distributions of response times for prosocial and sel sh choices in both the description and experience trials and (FIGURE 3).
Behavior during the description trials was explained by HDDM1, in which the mean drift rate was proportional to the chosen payoffs for self and society as well as the differences between chosen and unchosen payoffs for self and society (HDDM1 Description, DIC = 27419; TABLE 1). This is consistent with a large body of past literature reporting that evidence accumulation rates are proportional to the difference in (subjective) values between options during description decisions 42,[50][51][52][53][54] . In contrast, including the differences between self and society payoffs did not improve the model ts to experience decisions. Instead, behavior on experience trials was slightly better t by the simpler model, HDDM2, in which the mean drift rate was proportional to the chosen payoffs for self and society only (HDDM1 Experience, DIC = 11397; HDDM2 Experience, DIC = 11392; TABLE 1). Although, the model comparison results did not provide strong evidence in favor of either HDDM for experience trials, the posterior estimates of the drift weighting parameters from both HDDM1 and HDDM2 show that the evidence accumulation rates during experience trials are more strongly related to chosen outcomes than differences between chosen and unchosen outcomes. We used hierarchical drift diffusion models (HDDM) t to the response time data from description and experience decisions. In HDDM 1 (see Equation 3 in the Methods section), we modeled the evidence accumulation rate as a function of both payoff outcomes (b1, b2) and payoff differences (b3, b4). In HDDM2 (see Equation 4 in the Methods section), we modeled the evidence accumulation rate only as a function of payoff outcomes (b1, b2). Additionally, we included the following free parameters at the group and subject levels: boundary separation (i.e., the evidence threshold for making a response), starting point bias for evidence accumulation and non-decision times. We list the mean and standard deviation (SD) for all parameter estimates. A lower DIC indicates better model t.
Thus, the information upon which description and experience choices are based appears to differ, with experience choices being less sensitive to tradeoffs between chosen and unchosen outcomes for self and society. This decrease in sensitivity to unchosen outcomes during experience relative to description trials may explain why response times show a weaker relationship to social preferences (i.e., SVO) in experience compared to description decisions.

BOLD activity during description and experience social choices
To investigate how brain activity differed in social decisions made from description and experience, we analyzed the BOLD signal during each type of choice. We t a generalized linear model that included regressors for the mean level of activity during description and experience trials as well as parametric regressors for the mean HDDM evidence accumulation rate on each trial. We report results that survive correction for multiple comparisons at the whole-brain level using non-parametric permutation tests with 5000 permutations for each contrast.
We found signi cant differences in mean BOLD activity during social decisions in description compared to experience trials. There was greater average activity during description than experience trials in several brain regions including the caudate, middle frontal gyrus, paracingulate and cingulate gyrus, frontal pole and dorsal precuneus (FIGURE 4A and Supplementary Table S1A). In contrast, average activity was signi cantly greater in more ventral portions of the precuneus and posterior cingulate during experience than description trials (FIGURE 4A and Supplementary Table S1B). Note that these contrasts testing for differences in mean activity across trial types included the participants' SVO angles as a continuous covariate (z-scored across participants) to account for variation in social preferences.
Next, we investigated whether brain activation patterns during experience and description trials differed in participants who were more versus less prosocial. We divided our participants into two groups based on the median SVO in our sample (45.30 points). Previous fMRI studies investigating how BOLD activity during social decisions relates to SVO reported greater activity in medial orbitofrontal cortex (mOFC) and dorsal medial prefrontal cortex (dmPFC) for more sel sh (i.e., low SVO) relative to prosocial individuals during a social choice task using the typical choices from description 22 . We found a similar difference in the mOFC (MNI: -8, 44, -14; TFCE t-stat: 5.00), with low SVO individuals showing greater BOLD activity during description choices than high SVO participants. However, there were no signi cant differences in dmPFC activity in our sample after correcting for multiple comparisons. In contrast to description trials, we did not nd any signi cant differences between high and low SVO participants' BOLD responses during experience choices. A direct comparison between description and experience choices showed that there were signi cant interactions in the association between mean activity and SVO between experience and description trials within the set of brain regions listed in Supplementary Table S2,  Trial-wise levels of BOLD activity were more closely associated with HDDM evidence accumulation rates in description than experience trials. Note that our DDM model was speci ed such that positive evidence accumulation rates promoted prosocial choices while negative rates promoted sel sh choices, and that the evidence accumulation rates on each trial were participant-speci c and thus incorporated participants' social preferences. During description choices, the evidence accumulation rate was positively correlated with BOLD activity in regions including the caudate, dorsolateral prefrontal cortex, insula, putamen, and thalamus (Supplementary Table S3). No regions showed signi cant negative correlations with trial-wise mean evidence accumulation rates during description trials, and there were no signi cant positive or negative correlations in any regions during experience trials after correcting for multiple comparisons. Furthermore, the association between evidence accumulation rates and BOLD activity was signi cantly more positive during description than experience trials in the caudate nucleus, cerebellum, cingulate cortex, middle temporal and frontal gyri, and thalamus (Supplementary Table S4).

Discussion
Individuals and organizations frequently face tradeoffs between bene ts for themselves and others when considering actions that reduce climate change or inequalities in education, healthcare, employment, or wages. It is important for both basic and applied sciences to understand the formation and expression of the social preferences that drive these decisions. For example, within organizations, social preferences are associated with support for interdepartmental goals and problem solving 56 .Most studies of social preferences have investigated decision contexts in which choice options and parameters are explicitly described to the decision makers 19,20 . However, it is often the case that individuals and organizations can only learn about the consequences of their actions through experiencing the outcomes of different options 28 . Far less is known about this type of social decision.
We studied differences in the cognitive and neural processes underlying experience versus description choices in a set of social tradeoffs that were matched in terms of the probability of making a prosocial choice in each context. Thus, any differences in the cognitive processing or neural activity we observe between experience and description trials are due to the decision processes rather than the decision outcomes. We nd that decisions taken from novel, explicit descriptions of the potential outcomes are slower and involve more overall brain activity than decisions based on extensive experience with the options and outcomes. The nding that mean response times decrease in experience relative to description trials is consistent with previous results 34,57 , and suggests that the experience decisions may have become more re exive or automated.
Previous work has shown that differences in SVO are related to response times as well as information search and utilization patterns during decisions from description 58, 59 . In general, response times are proportional to the difference in subjective values in descriptive binary choice problems, and the subjective value difference in social decisions is determined by the decision maker's social preferences 60-62 . Chen and Fischbacher 58 have shown that more prosocial individuals, who weight self and other payoffs more equally, take longer to decide between the alternatives on each trial of the widely used SVO Slider Measure designed by Murphy and colleagues 46 . Using eye-tracking, Fiedler and colleagues 59 showed that, in addition to responding more slowly to decisions involving social tradeoffs, prosocial, relative to sel sh, individuals have more total xations and inspect more of the available information, especially information about others' payoffs. Our results from the description choices replicate the positive correlation between SVO and response times in social decisions.
Interestingly, the association between social value orientation and response times changes across description and experience choices. The positive correlation between the same participants' SVO and response times was signi cantly weaker in experience than description decisions even though chosen outcomes (i.e., the proportion of prosocial decisions) did not differ across the two conditions. Assuming that differences in the information search and accumulation processes leading up to a decision drive the correlations between SVO and response times in description trials, our results suggest that these search and accumulation processes differ between description and experience trials. While our current data do not provide direct insights into information search patterns during description or experience choices, we could quantify the evidence accumulation processes during each type of decision.
We used diffusion decision models to examine evidence accumulation processes during social decisions from experience and description. The ndings from our drift diffusion model ts and comparisons indicate that description choices are based on differences in the subjective values of the two options. This is consistent with a large body of literature applying sequential sampling models to descriptive decisions 42,63 . However, including information about unchosen or foregone payoffs for the self and society isn't necessary to explain experience decisions. Experience decisions can be modeled equally accurately with only information about the chosen payoffs. Thus, in the case of binary choices, experience decisions seem to rely on half the information used in description choices. Fiedler and colleagues 59 showed that longer response times during description choices for higher SVO individuals correspond with those participants making more xations to different pieces of information on the screen and processing a greater proportion of the total available information. We do not have xation data in the current experiments, but our DDM results indicate that less information is processed during experience decisions overall. Less information processing during experience choices could result in smaller absolute differences in processing time between people with high and low SVO, and thus more similar response times.
Consistent with the DDM results, comparisons of average BOLD activity during description and experience trials showed that, in many brain regions, there was less activity during decisions from experience. Activity was greater in description than experience choices in the caudate, occipital, parietal, temporal, and prefrontal cortex, whereas voxels spanning the precuneus and posterior cingulate cortex were more active during experience decisions. Many of the regions more active in description trials overlap with those found in meta-analyses of fMRI studies of valuation and choice 40,64 . Notably, activity in the precuneus and posterior cingulate cortex has been linked to automatic representations of stimulus value 65-67 . Greater activity in these regions during experience compared to description choices is thus consistent with a more automated or re exive representation of the typical outcome (i.e., chosen value) associated with the highly familiar stimuli shown on experience trials. In addition to differences in mean activity levels, BOLD activity in striatal, prefrontal, and thalamic regions were more strongly correlated with trial-wise evidence accumulation rates during description compared to experience trials. Once again, this is consistent with more extensive processing of the potential outcomes for self and others during description relative to experience trials. Thus, overall, the behavioral and neuroimaging data indicate that the neurobiological processes underlying social decisions from experience and description are substantially different even when decision makers ultimately select the same outcomes.

Limitations
We wish to acknowledge an important limitation of our study and results. We intentionally used a small number of unique tradeoffs (5-7) so that we could be sure the tradeoffs would be well-learned after the three online training sessions. Moreover, we compared identical tradeoffs across description and experience. The small number of tradeoffs means that we have a limited decision space in which to measure social preferences. At the individual level, we mitigated this limitation by measuring social value orientations with a separate instrument. However, the decision space limits our ability to determine how the different processes underlying experience and decision choices may lead to different outcomes (e.g., more prosocial choices) for speci c tradeoffs between self and society-gains. Future studies using either larger or individually tailored sets of social tradeoffs are needed to shed light on the boundary conditions under which prosocial preferences may be expressed differently across description, experience, or other decision contexts.

Conclusion
Our work indicates that the neural mechanisms of social decision making depend strongly on how the value of choices was learned or determined in addition to an individual's social preferences. The results from description choices in our study are generally consistent with this existing body of literature showing that regions such as anterior insula, striatum, temporoparietal junction and the prefrontal cortex contribute to social and charitable decisions 10,[19][20][21]23,68,69 . However, the stark differences in brain activity patterns that we observe in experience relative to description choices suggest that the neural mechanisms of social decision making are not xed, but rather depend on how the potential outcomes for self and others are learned and/or processed at the time of choice. The differences across decision type in average BOLD activity patterns and the relationships between BOLD activity and participants' social value orientation highlight the fact that our understanding of how social preferences are instantiated in the brain remains incomplete. Studying when and how social preferences and decision mechanisms remain generalizable (i.e., remain consistent) or differ between speci c learning and decision contexts will be important for furthering our understanding of social decision making and brain mechanisms. During the fth wave, we collected fMRI data in addition to behavior from forty participants (19 female) while they completed the description-experience decision task (see Supplementary Methods S1 for inclusion criteria). Participants received a base level of compensation and earnings from their choices in the online training sessions and description-experience decision task. Individuals earned on average $18 during the online training and $40 if the description-experience decision task was completed in the behavioral lab or $61 if it was done in conjunction with MRI scanning. Description-experience decision task. The description-experience decision task included both the description and experience trials and took place after the online training sessions described in the subsection below. Within both the online training sessions and the description-experience decision task,

Methods
participants were asked to adopt the mindset of a manager of an independent business taking a decision in favor of pro t for the business (paid out as money to the participant) or society-bene ts (paid out as money to a charity of the participant's choosing). At the beginning of the study, participants were asked to choose their preferred charitable organization. Participants could choose either from a list of eight wellknown charities or could suggest their preferred charity.
The description-experience decision task presented tradeoffs between self-pro t and society-bene ts. On each trial, participants had to select between two payoff combinations. Individuals made their choice within 3.5 seconds (mean 1.79 ± 1.53 s.d. seconds). One option maximized self-pro t and the other maximized societal-bene t. The choice pairs contained two payoff combinations, a payoff to self (business) and a payoff to society (charity): E.g. the payoff pair 60/50 vs. 30/70, which signi ed the business-/society payoffs for the left payoff combination vs. business-/society payoffs for the right payoff combination. The payoffs ranged between 30-80 points and we used 5-7 pre-selected payoff pairs that were the same for all the participants sampled in a wave, but differed between waves. Supplementary Table S5 lists all payoff combinations for each wave of data collection. In waves 1-4, participants completed on average 122 (± 14 s.d.) description and 116 (± 16 s.d.) experience trials during the description-experience decision task. In data collection wave 5, participants completed 61 description and 49 experience trials during three runs while undergoing fMRI (Supplementary Table S6).
The description-experience decision task consisted of description (DE) and experience (EX) trials. In the description trials,the payoff combinations for self-and societal pro t were displayed directly on novel cue images that were not used during the training sessions or repeated during the description-experience decision task. All cue images were unique fractals taken from the Mandelbrot set, and in description trials the cues showed the payoffs explicitly. Thus, the description trials required participants to acquire and compare the payoffs associated with each option, but there was no need to remember cue-payoff associations from one trial to the next because cues were never repeated. In addition to the 5-7 unique payoff combinations per wave, we added 20% more novel description trials to serve as foils in that condition. The foil trials had similar, but not identical, payoff combinations to the primary trials that were matched between description and experience conditions, and were used to make it less obvious that the payoffs and tradeoff were matched across the two conditions. The foil trials were excluded from behavioral analyses comparing description and experience choices. In the experience trials, payoff combinations were displayed as hash tags (#/#) on the image instead of numbers. One speci c cue image was associated with each of the 5-7 unique payoffs for each participant. The image-payoff pairings were learned during three days of online training before the main experiment session (see Supplementary Table S7 and Supplementary Methods S2), which presented a mix of description and experience choices. Participants accurately remembered and reported the payoff alternatives associated with the learned cues (see Supplementary Results S1). The positioning of the payoff combinations on the left, right, top, or bottom of the fractals was fully randomized across participants to avoid systematic bias. The choices were incentive compatible; two trials were selected at random and the participant's decisions about self-and societal pro t on those trials were converted into a cash payment to the participant or a donation to his or her chosen charitable organization, respectively. We also used a hierarchical linear regression with grouping effects for each participant (n = 147) and unique tradeoff (n = 5 or 7 depending on the data collection wave) to test whether the natural logarithm of the response time differed across description and experience trials or was linearly related to SVO angle. The dependent variable in this regression was the mean of the log response times for each unique tradeoff per condition and participant. Once again, the SVO angle was z-scored across participants. Hierarchical Drift Diffusion Model (HDDM). We t hierarchical drift diffusion models to the response time data from description and experience decisions separately. We speci ed two types of HDDM that differed in which aspects of the decision options could in uence the mean evidence accumulation or drift rates. In HDDM1, we modeled the evidence accumulation rate as a function of both payoff differences and chosen payoff outcomes (Equation 3). In HDDM2, only the chosen self and society outcomes had an in uence the evidence accumulation rate (Equation 4). In addition to the drift rates, both HDDMs included the following free parameters at the group and subject levels: boundary separation (i.e., the evidence threshold for making a response), starting point bias for evidence accumulation and non-decision times. constructed a generalized linear model using the HDDM weights from the behavioral data. Our regressors identify two events, description choices and experience choices. We used the trial-speci c mean drift rates from the HDDM as parametric modulator (all parameteric modulators were mean centered). To facilitate the comparison of fMRI responses between description and experience decisions, we used the best-tting parameters from the drift diffusion model speci cation that included both payoff differences and outcomes to compute the mean drift rates for both description and experience trials (HDDM1, Equation 3). Note that the mean drift rates on experience trials derived from HDDMs 1 and 2 were highly correlated (mean r across participants = 0.988, s.d. = 0.04), and thus the parametric modulators derived from either HDDM speci cation yield similar results. Regressors for head motion, cardiac effects, and respiratory effects were included in the GLM to help to control for BOLD signal variability related to those non-task factors. For the second level analysis as well as for the visualization of images, we used the non-parametric permutation test (n=5000 permutations) with threshold-free cluster enhancement (TFCE) and the randomise function from the FMRIB software Library 5.0.10 (FSL; FMRIB Centre, Oxford, UK, RRID:SCR_002823)). Results are FWE corrected and coordinates are given in Montreal Neurological Institute (MNI) space.
Declarations Figure 1 Task structure of the social decision task. Participants chose in 3.5 seconds between two pairs of outcomes for business and society, that represented choices that were paid out to them and a charity of their choice at the end of the experiment. The choice was followed by an outcome screen and 1-8 second jittered inter-trial interval. We compared two different types of trials, description and experience trials were both presented in blocks ranging between 2-10 trials. A) In description trials each pair of outcomes was shown explicitly on the left and right sides or top and bottom of a novel fractal image and participants pressed the left or right button to indicate their preferred outcomes. B) In experience trials, participants were shown a unique fractal image but could not see the outcomes at the time of choice. Instead, they had learned the outcomes for the unique fractal images through extensive training over three days prior to the main experimental session. show the group means. B) There was considerable variability in prosocial preferences across the 147 participants in our sample. The x-axis shows the social preference angles from the SVO measure.
Prosociality scores near zero indicate sel sh preferences whereas higher scores indicate more prosocial preferences. C) More sel sh participants made fewer society-bene t maximizing choices compared to prosocial individuals. The y-axis shows the proportion of choices that maximize the bene ts to society (i.e., proportion of prosocial decisions) for each individual in description (white dots, solid regression line) and experience (grey dots, dotted regression line) trials. The x-axis shows the individual level of prosociality derived from the SVO measure46. The proportion of prosocial decisions across description and experience trials were similar.