It is cool to see where most of our Thanksgiving dishes came from. Like the reason we have all of these dishes was because of the Native of North America and the things they found and ate.

@45:27

Blow: games for a while was known as the place where there were still a lot of really good programmers because we were forged in this fire of really difficult stuff, and I think that's become increasingly untrue. I think game programmers are reverting to the mean.

Or superinferior, even:

• https://hypothes.is/a/0aJmbia_Ee2UcD9QSXCpVA
• https://hypothes.is/a/zPdzGCaeEe6XyvOHOHZBcQ

I remember a few years back playing a video game. I forget which one, but at some point a character said something along the lines of "After everything, all you've got to show is some metal," in relation to the pursuit of gold. That's always stuck with me. Metal has value, yes, but it's not worth blood. People shouldn't die for it, and no man with an ounce of honor should kill for it. I think that Bartolomé would've thought much the same.

Akin to McLuhan's tetrad :)

Note the impact shall be different depending on the game! In social games, take VR chat, or Second Lind, perhaps even The Sims, this is much more prominent. These aren't examined. Games like Minecraft allow more than parametric customisation, they have mods and skins... and in this sense, ethnographies on game worlds, will be exemplatory, but also limited by these constraints. Sure, white is a terrible default... but we shouldn't ask indie devs to add perfect customisation settings when their games lack basic accessibility features like high contrast or text read-aloud.

And gender, and any/most self-defined identities, which can and do change over time (and probably will more in the future, with transhumanism), even if they are stereotypically pushed top-down, like functional diversity.

Not necessarily... you know, there is a vibrant community behind videogame cracking (and virtualisation). And DRM-free titles (from GOG, or itch) provide a cheesy way of sharing games more easily than sharing "disks".

Think of finding the buttons to actually find a competitive match for a game like League, not to mention the "accept" to enter it.

They are also a partial spoiler, for you can deduce information based on how long the game will last.

This raises an interesting connection for me because hedge fund "quants" also use this strategy: finding seemingly useless or irrelevant data to game the market. Somehow, both are effective, if not only for a short period

yes!!!! However, they don't always work out. I personally love when stats can back up something, like stealing bases, but I know that although stealing bases is statistically not a smart choice, it adds drama to the game. The same can be said with boxing, but when we lose the integrity of sport, it just becomes numbers and playing in the favor of whomever get to benefit from those numbers

SO messed up. Using this type of data and justifying it as a form of subconscious racism

can the same be said for any sport? I think of the times where underdogs win. I watch a lot of volleyball and never thought A&M would win the national championship. But their game throughout the season became increasingly better. Were player statistics getting to the level of a #1 team?

Sports data amazes me and I see how coaches make so much. Not only do they have to be experts in the game, but they also have to constantly monitor trends on game days and make critical decisions, especially during high stakes games.

They're also told, "answer truthfully for a best case outcome"

At what point in history did men begin to have more power in leadership roles then, and why?

One solution might be to. Have NPCs use the mechanics in ways that teach the player how effective they are. Similar to the way that Pokemon uses mechanics to teach people how to play the Pokemon game and helps them to avoid the trap of simply getting large damage moves and using only them.

The main idea of “Time Management Strategies for Success” is that managing your time well is about working smarter, not just harder. This section gives practical, realistic strategies students can use right away to stay productive, reduce stress, and avoid procrastination—while still having a life.

In simple terms, it teaches you how to:

Plan ahead with a positive mindset, so studying feels less stressful and more motivating.

Use your energy wisely by doing tasks at the time of day when you focus best.

Break big tasks into smaller, manageable pieces to avoid feeling overwhelmed.

Set priorities, so the most important work gets done first.

Build momentum by starting with easier tasks when motivation is low.

Stay flexible by reviewing your schedule when things feel out of control.

Ask for help when needed, instead of staying stuck and confused.

Take regular breaks to avoid burnout and stay mentally fresh.

Use small pockets of free time during the day to get work done early.

Avoid distractions, especially multitasking, to keep your focus strong.

Reward yourself after completing tasks to stay motivated.

Learn to say no to interruptions without feeling guilty.

Balance work and life, making time for rest, friends, and personal well-being.

Use planners and to-do lists to stay organized and on track.

Game theory anyone?

eLife Assessment

This important work investigates cooperative behaviors in adolescents using a repeated Prisoner's Dilemma game. The approach used in the study is solid. The impact of this work could be further enhanced with more rigorous modelling procedures and more modeling selection/comparison details, as well as by framing the findings in terms of the specific game-theoretic context, rather than general cooperation. Findings from this study will be of interest to developmental psychologists, economists, and social psychologists.

Reviewer #1 (Public review):

Summary:

Wu and colleagues aimed to explain previous findings that adolescents, compared to adults, show reduced cooperation following cooperative behaviour from a partner in several social scenarios. The authors analysed behavioural data from adolescents and adults performing a zero-sum Prisoner's Dilemma task and compared a range of social and non-social reinforcement learning models to identify potential algorithmic differences. Their findings suggest that adolescents' lower cooperation is best explained by a reduced learning rate for cooperative outcomes, rather than differences in prior expectations about the cooperativeness of a partner. The authors situate their results within the broader literature, proposing that adolescents' behaviour reflects a stronger preference for self-interest rather than a deficit in mentalising.

Strengths:

The work as a whole suggests that, in line with past work, adolescents prioritise value accumulation, and this can be, in part, explained by algorithmic differences in weighted value learning. The authors situate their work very clearly in past literature, and make it obvious the gap they are testing and trying to explain. The work also includes social contexts which move the field beyond non-social value accumulation in adolescents. The authors compare a series of formal approaches that might explain the results and establish generative and model-comparison procedures to demonstrate the validity of their winning model and individual parameters. The writing was clear, and the presentation of the results was logical and well-structured.

Weaknesses:

I had some concerns about the methods used to fit and approximate parameters of interest. Namely, the use of maximum likelihood versus hierarchical methods to fit models on an individual level, which may reduce some of the outliers noted in the supplement, and also may improve model identifiability.

There was also little discussion given the structure of the Prisoner's Dilemma, and the strategy of the game (that defection is always dominant), meaning that the preferences of the adolescents cannot necessarily be distinguished from the incentives of the game, i.e. they may seem less cooperative simply because they want to play the dominant strategy, rather than a lower preferences for cooperation if all else was the same.

The authors have now addressed my comments and concerns in their revised version.

Appraisal & Discussion:

Overall, I believe this work has the potential to make a meaningful contribution to the field. Its impact would be strengthened by more rigorous modelling checks and fitting procedures, as well as by framing the findings in terms of the specific game-theoretic context, rather than general cooperation.

Comments on revisions:

Thank you to the authors for addressing my comments and concerns.

Reviewer #2 (Public review):

Summary:

This manuscript investigates age-related differences in cooperative behavior by comparing adolescents and adults in a repeated Prisoner's Dilemma Game (rPDG). The authors find that adolescents exhibit lower levels of cooperation than adults. Specifically, adolescents reciprocate partners' cooperation to a lesser degree than adults do. Through computational modeling, they show that this relatively low cooperation rate is not due to impaired expectations or mentalizing deficits, but rather a diminished intrinsic reward for reciprocity. A social reinforcement learning model with asymmetric learning rate best captured these dynamics, revealing age-related differences in how positive and negative outcomes drive behavioral updates. These findings contribute to understanding the developmental trajectory of cooperation and highlight adolescence as a period marked by heightened sensitivity to immediate rewards at the expense of long-term prosocial gains.

Strengths:

Rigid model comparison and parameter recovery procedure. Conceptually comprehensive model space. Well-powered samples.

Weaknesses:

A key conceptual distinction between learning from non-human agents (e.g., bandit machines) and human partners is that the latter are typically assumed to possess stable behavioral dispositions or moral traits. When a non-human source abruptly shifts behavior (e.g., from 80% to 20% reward), learners may simply update their expectations. In contrast, a sudden behavioral shift by a previously cooperative human partner can prompt higher-order inferences about the partner's trustworthiness or the integrity of the experimental setup (e.g., whether the partner is truly interactive or human). The authors may consider whether their modeling framework captures such higher-order social inferences. Specifically, trait-based models-such as those explored in Hackel et al. (2015, Nature Neuroscience)-suggest that learners form enduring beliefs about others' moral dispositions, which then modulate trial-by-trial learning. A learner who believes their partner is inherently cooperative may update less in response to a surprising defection, effectively showing a trait-based dampening of learning rate.

This asymmetry in belief updating has been observed in prior work (e.g., Siegel et al., 2018, Nature Human Behaviour) and could be captured using a dynamic or belief-weighted learning rate. Models incorporating such mechanisms (e.g., dynamic learning rate models as in Jian Li et al., 2011, Nature Neuroscience) could better account for flexible adjustments in response to surprising behavior, particularly in the social domain.

Second, the developmental interpretation of the observed effects would be strengthened by considering possible non-linear relationships between age and model parameters. For instance, certain cognitive or affective traits relevant to social learning-such as sensitivity to reciprocity or reward updating-may follow non-monotonic trajectories, peaking in late adolescence or early adulthood. Fitting age as a continuous variable, possibly with quadratic or spline terms, may yield more nuanced developmental insights.

Finally, the two age groups compared-adolescents (high school students) and adults (university students)-differ not only in age but also in sociocultural and economic backgrounds. High school students are likely more homogenous in regional background (e.g., Beijing locals), while university students may be drawn from a broader geographic and socioeconomic pool. Additionally, differences in financial independence, family structure (e.g., single-child status), and social network complexity may systematically affect cooperative behavior and valuation of rewards. Although these factors are difficult to control fully, the authors should more explicitly address the extent to which their findings reflect biological development versus social and contextual influences.

Comments on revisions:

The authors have addressed most of my previous comments adequately. I only have a minor question: The models with some variations of RL seem to have very similar AIC. What were the authors' criteria in deciding which model is the "winning" model when several models have similar AIC? Are there ways of integrating models with similar structures into a "model family"? Alternatively, is it possible that different models fit better for different subgroups of participants (e.g., high schoolers vs. college students)?

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

Wu and colleagues aimed to explain previous findings that adolescents, compared to adults, show reduced cooperation following cooperative behaviour from a partner in several social scenarios. The authors analysed behavioural data from adolescents and adults performing a zero-sum Prisoner's Dilemma task and compared a range of social and non-social reinforcement learning models to identify potential algorithmic differences. Their findings suggest that adolescents' lower cooperation is best explained by a reduced learning rate for cooperative outcomes, rather than differences in prior expectations about the cooperativeness of a partner. The authors situate their results within the broader literature, proposing that adolescents' behaviour reflects a stronger preference for self-interest rather than a deficit in mentalising.

Strengths:

The work as a whole suggests that, in line with past work, adolescents prioritise value accumulation, and this can be, in part, explained by algorithmic differences in weighted value learning. The authors situate their work very clearly in past literature, and make it obvious the gap they are testing and trying to explain. The work also includes social contexts that move the field beyond non-social value accumulation in adolescents. The authors compare a series of formal approaches that might explain the results and establish generative and modelcomparison procedures to demonstrate the validity of their winning model and individual parameters. The writing was clear, and the presentation of the results was logical and wellstructured.

We thank the reviewer for recognizing the strengths of our work.

Weaknesses:

(Q1) I also have some concerns about the methods used to fit and approximate parameters of interest. Namely, the use of maximum likelihood versus hierarchical methods to fit models on an individual level, which may reduce some of the outliers noted in the supplement, and also may improve model identifiability.

We thank the reviewer for this suggestion. Following the comment, we added a hierarchical Bayesian estimation. We built a hierarchical model with both group-level (adolescent group and adult group) and individual-level structures for the best-fitting model. Four Markov chains with 4,000 samples each were run, and the model converged well (see Figure supplement 7)

We then analyzed the posterior parameters for adolescents and adults separately. The results were consistent with those from the MLE analysis (see Figure 2—figure supplement 5). These additional results have been included in the Appendix Analysis section (also see Figure supplement 5 and 7). In addition, we have updated the code and provided the link for reference. We appreciate the reviewer’s suggestion, which improved our analysis.

(Q2) There was also little discussion given the structure of the Prisoner's Dilemma, and the strategy of the game (that defection is always dominant), meaning that the preferences of the adolescents cannot necessarily be distinguished from the incentives of the game, i.e. they may seem less cooperative simply because they want to play the dominant strategy, rather than a lower preferences for cooperation if all else was the same.

We thank the reviewer for this comment and agree that adolescents’ lower cooperation may partly reflect a rational response to the incentive structure of the Prisoner’s Dilemma.

However, our computational modeling explicitly addressed this possibility. Model 4 (inequality aversion) captures decisions that are driven purely by self-interest or aversion to unequal outcomes, including a parameter reflecting disutility from advantageous inequality, which represents self-oriented motives. If participants’ behavior were solely guided by the payoff-dominant strategy, this model should have provided the best fit. However, our model comparison showed that Model 5 (social reward) performed better in both adolescents and adults, suggesting that cooperative behavior is better explained by valuing social outcomes beyond payoff structures.

Besides, if adolescents’ lower cooperation is that they strategically respond to the payoff structure by adopting defection as the more rewarding option. Then, adolescents should show reduced cooperation across all rounds. Instead, adolescents and adults behaved similarly when partners defected, but adolescents cooperated less when partners cooperated and showed little increase in cooperation even after consecutive cooperative responses. This pattern suggests that adolescents’ lower cooperation cannot be explained solely by strategic responses to payoff structures but rather reflects a reduced sensitivity to others’ cooperative behavior or weaker social reciprocity motives. We have expanded our Discussion to acknowledge this important point and to clarify how the behavioral and modeling results address the reviewer’s concern.

“Overall, these findings indicate that adolescents’ lower cooperation is unlikely to be driven solely by strategic considerations, but may instead reflect differences in the valuation of others’ cooperation or reduced motivation to reciprocate. Although defection is the payoffdominant strategy in the Prisoner’s Dilemma, the selective pattern of adolescents’ cooperation and the model comparison results indicate that their reduced cooperation cannot be fully explained by strategic incentives, but rather reflects weaker valuation of social reciprocity.”

Appraisal & Discussion:

(Q3) The authors have partially achieved their aims, but I believe the manuscript would benefit from additional methodological clarification, specifically regarding the use of hierarchical model fitting and the inclusion of Bayes Factors, to more robustly support their conclusions. It would also be important to investigate the source of the model confusion observed in two of their models.

We thank the reviewer for this comment. In the revised manuscript, we have clarified the hierarchical Bayesian modeling procedure for the best-fitting model, including the group- and individual-level structure and convergence diagnostics. The hierarchical approach produced results that fully replicated those obtained from the original maximumlikelihood estimation, confirming the robustness of our findings. Please also see the response to Q1.

Regarding the model confusion between the inequality aversion (Model 4) and social reward (Model 5) models in the model recovery analysis, both models’ simulated behaviors were best captured by the baseline model. This pattern arises because neither model includes learning or updating processes. Given that our task involves dynamic, multi-round interactions, models lacking a learning mechanism cannot adequately capture participants’ trial-by-trial adjustments, resulting in similar behavioral patterns that are better explained by the baseline model during model recovery. We have added a clarification of this point to the Results:

“The overlap between Models 4 and 5 likely arises because neither model incorporates a learning mechanism, making them less able to account for trial-by-trial adjustments in this dynamic task.”

(Q4) I am unconvinced by the claim that failures in mentalising have been empirically ruled out, even though I am theoretically inclined to believe that adolescents can mentalise using the same procedures as adults. While reinforcement learning models are useful for identifying biases in learning weights, they do not directly capture formal representations of others' mental states. Greater clarity on this point is needed in the discussion, or a toning down of this language.

We sincerely thank the reviewer for this professional comment. We agree that our prior wording regarding adolescents’ capacity to mentalise was somewhat overgeneralized. Accordingly, we have toned down the language in both the Abstract and the Discussion to better align our statements with what the present study directly tests. Specifically, our revisions focus on adolescents’ and adults’ ability to predict others’ cooperation in social learning. This is consistent with the evidence from our analyses examining adolescents’ and adults’ model-based expectations and self-reported scores on partner cooperativeness (see Figure 4). In the revised Discussion, we state:

“Our results suggest that the lower levels of cooperation observed in adolescents stem from a stronger motive to prioritize self-interest rather than a deficiency in predicting others’ cooperation in social learning”.

(Q5) Additionally, a more detailed discussion of the incentives embedded in the Prisoner's Dilemma task would be valuable. In particular, the authors' interpretation of reduced adolescent cooperativeness might be reconsidered in light of the zero-sum nature of the game, which differs from broader conceptualisations of cooperation in contexts where defection is not structurally incentivised.

We thank the reviewer for this comment and agree that adolescents’ lower cooperation may partly reflect a rational response to the incentive structure of the Prisoner’s Dilemma. However, our behavioral and computational evidence suggests that this pattern cannot be explained solely by strategic responses to payoff structures, but rather reflects a reduced sensitivity to others’ cooperative behavior or weaker social reciprocity motives. We have expanded the Discussion to acknowledge this point and to clarify how both behavioral and modeling results address the reviewer’s concern (see also our response to Q2).

(Q6) Overall, I believe this work has the potential to make a meaningful contribution to the field. Its impact would be strengthened by more rigorous modelling checks and fitting procedures, as well as by framing the findings in terms of the specific game-theoretic context, rather than general cooperation.

We thank the reviewer for the professional comments, which have helped us improve our work.

Reviewer #2 (Public review):

Summary:

This manuscript investigates age-related differences in cooperative behavior by comparing adolescents and adults in a repeated Prisoner's Dilemma Game (rPDG). The authors find that adolescents exhibit lower levels of cooperation than adults. Specifically, adolescents reciprocate partners' cooperation to a lesser degree than adults do. Through computational modeling, they show that this relatively low cooperation rate is not due to impaired expectations or mentalizing deficits, but rather a diminished intrinsic reward for reciprocity. A social reinforcement learning model with asymmetric learning rate best captured these dynamics, revealing age-related differences in how positive and negative outcomes drive behavioral updates. These findings contribute to understanding the developmental trajectory of cooperation and highlight adolescence as a period marked by heightened sensitivity to immediate rewards at the expense of long-term prosocial gains.

Strengths:

(1) Rigid model comparison and parameter recovery procedure.

(2) Conceptually comprehensive model space.

(3) Well-powered samples.

We thank the reviewer for highlighting the strengths of our work.

Weaknesses:

(Q1) A key conceptual distinction between learning from non-human agents (e.g., bandit machines) and human partners is that the latter are typically assumed to possess stable behavioral dispositions or moral traits. When a non-human source abruptly shifts behavior (e.g., from 80% to 20% reward), learners may simply update their expectations. In contrast, a sudden behavioral shift by a previously cooperative human partner can prompt higher-order inferences about the partner's trustworthiness or the integrity of the experimental setup (e.g., whether the partner is truly interactive or human). The authors may consider whether their modeling framework captures such higher-order social inferences. Specifically, trait-based models-such as those explored in Hackel et al. (2015, Nature Neuroscience)-suggest that learners form enduring beliefs about others' moral dispositions, which then modulate trial-bytrial learning. A learner who believes their partner is inherently cooperative may update less in response to a surprising defection, effectively showing a trait-based dampening of learning rate.

We thank the reviewer for this thoughtful comment. We agree that social learning from human partners may involve higher-order inferences beyond simple reinforcement learning from non-human sources. To address this, we had previously included such mechanisms in our behavioral modeling. In Model 7 (Social Reward Model with Influence), we tested a higher-order belief-updating process in which participants’ expectations about their partner’s cooperation were shaped not only by the partner’s previous choices but also by the inferred influence of their own past actions on the partner’s subsequent behavior. In other words, participants could adjust their belief about the partner’s cooperation by considering how their partner’s belief about them might change. Model comparison showed that Model 7 did not outperform the best-fitting model, suggesting that incorporating higher-order influence updates added limited explanatory value in this context. As suggested by the reviewer, we have further clarified this point in the revised manuscript.

Regarding trait-based frameworks, we appreciate the reviewer’s reference to Hackel et al. (2015). That study elegantly demonstrated that learners form relatively stable beliefs about others’ social dispositions, such as generosity, especially when the task structure provides explicit cues for trait inference (e.g., resource allocations and giving proportions). By contrast, our study was not designed to isolate trait learning, but rather to capture how participants update their expectations about a partner’s cooperation over repeated interactions. In this sense, cooperativeness in our framework can be viewed as a trait-like latent belief that evolves as evidence accumulates. Thus, while our model does not include a dedicated trait module that directly modulates learning rates, the belief-updating component of our best-fitting model effectively tracks a dynamic, partner-specific cooperativeness, potentially reflecting a prosocial tendency.

(Q2) This asymmetry in belief updating has been observed in prior work (e.g., Siegel et al., 2018, Nature Human Behaviour) and could be captured using a dynamic or belief-weighted learning rate. Models incorporating such mechanisms (e.g., dynamic learning rate models as in Jian Li et al., 2011, Nature Neuroscience) could better account for flexible adjustments in response to surprising behavior, particularly in the social domain.

We thank the reviewer for the suggestion. Following the comment, we implemented an additional model incorporating a dynamic learning rate based on the magnitude of prediction errors. Specifically, we developed Model 9:  Social reward model with Pearce–Hall learning algorithm (dynamic learning rate), in which participants’ beliefs about their partner’s cooperation probability are updated using a Rescorla–Wagner rule with a learning rate dynamically modulated by the Pearce–Hall (PH) Error Learning mechanism. In this framework, the learning rate increases following surprising outcomes (larger prediction errors) and decreases as expectations become more stable (see Appendix Analysis section for details).

The results showed that this dynamic learning rate model did not outperform our bestfitting model in either adolescents or adults (see Figure supplement 6). We greatly appreciate the reviewer’s suggestion, which has strengthened the scope of our analysis. We now have added these analyses to the Appendix Analysis section (also Figure Supplement 6) and expanded the Discussion to acknowledge this modeling extension and further discuss its implications.

(Q3) Second, the developmental interpretation of the observed effects would be strengthened by considering possible non-linear relationships between age and model parameters. For instance, certain cognitive or affective traits relevant to social learning-such as sensitivity to reciprocity or reward updating-may follow non-monotonic trajectories, peaking in late adolescence or early adulthood. Fitting age as a continuous variable, possibly with quadratic or spline terms, may yield more nuanced developmental insights.

We thank the reviewer for this professional comment. In addition to the linear analyses, we further conducted exploratory analyses to examine potential non-linear relationships between age and the model parameters. Specifically, we fit LMMs for each of the four parameters as outcomes (α+, α-, β, and ω). The fixed effects included age, a quadratic age term, and gender, and the random effects included subject-specific random intercepts and random slopes for age and gender. Model comparison using BIC did not indicate improvement for the quadratic models over the linear models for α⁺ (ΔBIC_{quadratic-linear} = 5.09), α^-(ΔBIC_{quadratic-linear} = 3.04), β (ΔBIC_{quadratic-linear} = 3.9), or ω (ΔBIC_{quadratic-linear}= 0). Moreover, the quadratic age term was not significant for α⁺, α⁻, or β (all ps > 0.10). For ω, we observed a significant linear age effect (b = 1.41, t = 2.65, p = 0.009) and a significant quadratic age effect (b = −0.03, t = −2.39, p = 0.018; see Author response image 1). This pattern is broadly consistent with the group effect reported in the main text. The shaded area in the figure represents the 95% confidence interval. As shown, the interval widens at older ages (≥ 26 years) due to fewer participants in that range, which limits the robustness of the inferred quadratic effect. In consideration of the limited precision at older ages and the lack of BIC improvement, we did not emphasize the quadratic effect in the revised manuscript and present these results here as exploratory.

Author response image 1.

Linear and quadratic model fits showing the relationship between age and the ω parameter, with 95% confidence intervals.

(Q4) Finally, the two age groups compared - adolescents (high school students) and adults (university students) - differ not only in age but also in sociocultural and economic backgrounds. High school students are likely more homogenous in regional background (e.g., Beijing locals), while university students may be drawn from a broader geographic and socioeconomic pool. Additionally, differences in financial independence, family structure (e.g., single-child status), and social network complexity may systematically affect cooperative behavior and valuation of rewards. Although these factors are difficult to control fully, the authors should more explicitly address the extent to which their findings reflect biological development versus social and contextual influences.

We appreciate this comment. Indeed, adolescents (high school students) and adults (university students) differ not only in age but also in sociocultural and socioeconomic backgrounds. In our study, all participants were recruited from Beijing and surrounding regions, which helps minimize large regional and cultural variability. Moreover, we accounted for individual-level random effects and included participants’ social value orientation (SVO) as an individual difference measure.

Nonetheless, we acknowledge that other contextual factors, such as differences in financial independence, socioeconomic status, and social experience—may also contribute to group differences in cooperative behavior and reward valuation. Although our results are broadly consistent with developmental theories of reward sensitivity and social decisionmaking, sociocultural influences cannot be entirely ruled out. Future work with more demographically matched samples or with socioeconomic and regional variables explicitly controlled will help clarify the relative contributions of biological and contextual factors. Accordingly, we have revised the Discussion to include the following statement:

“Third, although both age groups were recruited from Beijing and nearby regions, minimizing major regional and cultural variation, adolescents and adults may still differ in socioeconomic status, financial independence, and social experience. Such contextual differences could interact with developmental processes in shaping cooperative behavior and reward valuation. Future research with demographically matched samples or explicit measures of socioeconomic background will help disentangle biological from sociocultural influences.”

Reviewer #3 (Public review):

Summary:

Wu and colleagues find that in a repeated Prisoner's Dilemma, adolescents, compared to adults, are less likely to increase their cooperation behavior in response to repeated cooperation from a simulated partner. In contrast, after repeated defection by the partner, both age groups show comparable behavior.

To uncover the mechanisms underlying these patterns, the authors compare eight different models. They report that a social reward learning model, which includes separate learning rates for positive and negative prediction errors, best fits the behavior of both groups. Key parameters in this winning model vary with age: notably, the intrinsic value of cooperating is lower in adolescents. Adults and adolescents also differ in learning rates for positive and negative prediction errors, as well as in the inverse temperature parameter.

Strengths:

The modeling results are compelling in their ability to distinguish between learned expectations and the intrinsic value of cooperation. The authors skillfully compare relevant models to demonstrate which mechanisms drive cooperation behavior in the two age groups.

We thank the reviewer’s recognition of our work’s strengths.

Weaknesses:

(Q1) Some of the claims made are not fully supported by the data:

The central parameter reflecting preference for cooperation is positive in both groups. Thus, framing the results as self-interest versus other-interest may be misleading.

We thank the reviewer for this insightful comment. In the social reward model, the cooperation preference parameter is positive by definition, as defection in the repeated rPDG always yields a +2 monetary advantage regardless of the partner’s action. This positive value represents the additional subjective reward assigned to mutual cooperation (e.g., reciprocity value) that counterbalances the monetary gain from defection. Although the estimated social reward parameter ω was positive, the effective advantage of cooperation is Δ=p×ω−2. Given participants’ inferred beliefs p, Δ was negative for most trials (p×ω<2), indicating that the social reward was insufficient to offset the +2 advantage of defection. Thus, both adolescents and adults valued cooperation positively, but adolescents’ smaller ω and weaker responsiveness to sustained partner cooperation suggest a stronger weighting on immediate monetary payoffs.

In this light, our framing of adolescents as more self-interested derives from their behavioral pattern: even when they recognized sustained partner cooperation and held high expectations of partner cooperation, adolescents showed lower cooperative behavior and reciprocity rewards compared with adults. Whereas adults increased cooperation after two or three consecutive partner cooperations, this pattern was absent among adolescents. We therefore interpret their behavior as relatively more self-interested, reflecting reduced sensitivity to the social reward from mutual cooperation rather than a categorical shift from self-interest to other-interest, as elaborated in the Discussion.

(Q2) It is unclear why the authors assume adolescents and adults have the same expectations about the partner's cooperation, yet simultaneously demonstrate age-related differences in learning about the partner. To support their claim mechanistically, simulations showing that differences in cooperation preference (i.e., the w parameter), rather than differences in learning, drive behavioral differences would be helpful.

We thank the reviewer for raising this important point. In our model, both adolescents and adults updated their beliefs about partner cooperation using an asymmetric reinforcement learning (RL) rule. Although adolescents exhibited a higher positive and a lower negative learning rate than adults, the two groups did not differ significantly in their overall updating of partner cooperation probability (Fig. 4a-b). We then examined the social reward parameter ω, which was significantly smaller in adolescents and determined the intrinsic value of mutual cooperation (i.e., p×ω). This variable differed significantly between groups and closely matched the behavioral pattern.

Following the reviewer’s suggestion, we conducted additional simulations varying one model parameter at a time while holding the others constant. The difference in mean cooperation probability between adults and adolescents served as the index (positive = higher cooperation in adults). As shown in the Author response image 2, decreases in ω most effectively reproduced the observed group difference (shaded area), indicating that age-related differences in cooperation are primarily driven by variation in the social reward parameter ω rather than by others.

Author response image 2.

Simulation results showing how variations in each model parameter affect the group difference in mean cooperation probability (Adults – Adolescents). Based on the bestfitting Model 8 and parameters estimated from all participants, each line represents one parameter (i.e., α+, α-, ω, β) systematically varied within the tested range (α±:0.1–0.9; ω, β:1–9) while other parameters were held constant. Positive values indicate higher cooperation in adults. Smaller ω values most strongly reproduced the observed group difference, suggesting that reduced social reward weighting primarily drives adolescents’ lower cooperation.

(Q3) Two different schedules of 120 trials were used: one with stable partner behavior and one with behavior changing after 20 trials. While results for order effects are reported, the results for the stable vs. changing phases within each schedule are not. Since learning is influenced by reward structure, it is important to test whether key findings hold across both phases.

We thank the reviewer for this thoughtful and professional comment. In our GLMM and LMM analyses, we focused on trial order rather than explicitly including the stable vs. changing phase factor, due to concerns about multicollinearity. In our design, phases occur in specific temporal segments, which introduces strong collinearity with trial order. In multi-round interactions, order effects also capture variance related to phase transitions.

Nonetheless, to directly address this concern, we conducted additional robustness analyses by adding a phase variable (stable vs. changing) to GLMM1, LMM1, and LMM3 alongside the original covariates. Across these specifications, the key findings were replicated (see GLMM_sup2 and LMM_sup4–5; Tables 9-11), and the direction and significance of main effects remained unchanged, indicating that our conclusions are robust to phase differences.

(Q4) The division of participants at the legal threshold of 18 years should be more explicitly justified. The age distribution appears continuous rather than clearly split. Providing rationale and including continuous analyses would clarify how groupings were determined.

We thank the reviewer for this thoughtful comment. We divided participants at the legal threshold of 18 years for both conceptual and practical reasons grounded in prior literature and policy. In many countries and regions, 18 marks the age of legal majority and is widely used as the boundary between adolescence and adulthood in behavioral and clinical research. Empirically, prior studies indicate that psychosocial maturity and executive functions approach adult levels around this age, with key cognitive capacities stabilizing in late adolescence (Icenogle et al., 2019; Tervo-Clemmens et al., 2023). We have clarified this rationale in the Introduction section of the revised manuscript.

“Based on legal criteria for majority and prior empirical work, we adopt 18 years as the boundary between adolescence and adulthood (Icenogle et al., 2019; Tervo-Clemmens et al., 2023).”

We fully agree that the underlying age distribution is continuous rather than sharply divided. To address this, we conducted additional analyses treating age as a continuous predictor (see GLMM_sup1 and LMM_sup1–3; Tables S1-S4), which generally replicated the patterns observed with the categorical grouping. Nevertheless, given the limited age range of our sample, the generalizability of these findings to fine-grained developmental differences remains constrained. Therefore, our primary analyses continue to focus on the contrast between adolescents and adults, rather than attempting to model a full developmental trajectory.

(Q5) Claims of null effects (e.g., in the abstract: "adults increased their intrinsic reward for reciprocating... a pattern absent in adolescents") should be supported with appropriate statistics, such as Bayesian regression.

We thank the reviewer for highlighting the importance of rigor when interpreting potential null effects. To address this concern, we conducted Bayes factor analyses of the intrinsic reward for reciprocity and reported the corresponding BF10 for all relevant post hoc comparisons. This approach quantifies the relative evidence for the alternative versus the null hypothesis, thereby providing a more direct assessment of null effects. The analysis procedure is now described in the Methods and Materials section:

“Post hoc comparisons were conducted using Bayes factor analyses with MATLAB’s bayesFactor Toolbox (version v3.0, Krekelberg, 2024), with a Cauchy prior scale σ = 0.707.”

(Q6) Once claims are more closely aligned with the data, the study will offer a valuable contribution to the field, given its use of relevant models and a well-established paradigm.

We are grateful for the reviewer’s generous appraisal and insightful comments.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) I commend the authors on a well-structured, clear, and interesting piece of work. I have several questions and recommendations that, if addressed, I believe will strengthen the manuscript.

We thank the reviewer for commending the organization of our paper.

(2) Introduction: - Why use a zero-sum (Prisoner's Dilemma; PD) versus a mixed-motive game (e.g. Trust Task) to study cooperation? In a finite set of rounds, the dominant strategy can be to defect in a PD.

We thank the reviewer for this helpful comment. We agree that both the rationale for using the repeated Prisoner’s Dilemma (rPDG) and the limitations of this framework should be clarified. We chose the rPDG to isolate the core motivational conflict between selfinterest and joint welfare, as its symmetric and simultaneous structure avoids the sequential trust and reputation dependencies/accumulation inherent to asymmetric tasks such as the Trust Game (King-Casas et al., 2005; Rilling et al., 2002).

Although a finitely repeated rPDG theoretically favors defection, extensive prior research shows that cooperation can still emerge in long repeated interactions when players rely on learning and reciprocity rather than backward induction (Rilling et al., 2002; Fareri et al., 2015). Our design employed 120 consecutive rounds, allowing participants to update expectations about partner behavior and to establish stable reciprocity patterns over time. We have added the following clarification to the Introduction:

“The rPDG provides a symmetric and simultaneous framework that isolates the motivational conflict between self-interest and joint welfare, avoiding the sequential trust and reputation dynamics characteristic of asymmetric tasks such as the Trust Game (Rilling et al., 2002; King-Casas et al., 2005)”

(3) Methods:

Did the participants know how long the PD would go on for?

Were the participants informed that the partner was real/simulated?

Were the participants informed that the partner was going to be the same for all rounds?

We thank the reviewer for the meticulous review work, which helped us present the experimental design and reporting details more clearly. the following clarifications: I. Participants were not informed of the total number of rounds in the rPDG. This prevented endgame expectations and avoided distraction from counting rounds, which could introduce additional effects. II. Participants were told that their partner was another human participant in the laboratory. However, the partner’s behavior was predetermined by a computer program. This design enabled tighter experimental control and ensured consistent conditions across age groups, supporting valid comparisons. III. Participants were informed that they would interact with the same partner across all rounds, aligning with the essence of a multiround interaction paradigm and stabilizing partner-related expectations. For transparency, we have clarified these points in the Methods and Materials section:

“Participants were told that their partner was another human participant in the laboratory and that they would interact with the same partner across all rounds. However, in reality, the actions of the partner were predetermined by a computer program. This setup allowed for a clear comparison of the behavioral responses between adolescents and adults. Participants were not informed of the total number of rounds in the rPDG.”

(4) The authors mention that an SVO was also recorded to indicate participant prosociality. Where are the results of this? Did this track game play at all? Could cooperativeness be explained broadly as an SVO preference that penetrated into game-play behaviour?

We thank the reviewer for pointing this out. We agree that individual differences in prosociality may shape cooperative behavior, so we conducted additional analyses incorporating SVO. Specifically, we extended GLMM1 and LMM3 by adding the measured SVO as a fixed effect with random slopes, yielding GLMM_sup3 and LMM_sup6 (Tables 12–13). The results showed that higher SVO was associated with greater cooperation, whereas its effect on the reward for reciprocity was not significant. Importantly, the primary findings remained unchanged after controlling for SVO. These results indicate that cooperativeness in our task cannot be explained solely by a broad SVO preference, although a more prosocial orientation was associated with greater cooperation. We have reported these analyses and results in the Appendix Analysis section.

(5) Why was AIC chosen rather an BIC to compare model dominance?

Sorry for the lack of clarification. Both the Akaike Information Criterion (AIC, Akaike, 1974) and Bayesian Information Criterion (BIC, Schwarz, 1978) are informationtheoretic criterions for model comparison, neither of which depends on whether the models to be compared are nested to each other or not (Burnham et al., 2002). We have added the following clarification into the Methods.

“We chose to use the AICc as the metric of goodness-of-fit for model comparison for the following statistical reasons. First, BIC is derived based on the assumption that the “true model” must be one of the models in the limited model set one compares (Burnham et al., 2002; Gelman & Shalizi, 2013), which is unrealistic in our case. In contrast, AIC does not rely on this unrealistic “true model” assumption and instead selects out the model that has the highest predictive power in the model set (Gelman et al., 2014). Second, AIC is also more robust than BIC for finite sample size (Vrieze, 2012).”

(6) I believe the model fitting procedure might benefit from hierarchical estimation, rather than maximum likelihood methods. Adolescents in particular seem to show multiple outliers in a^+ and w^+ at the lower end of the distributions in Figure S2. There are several packages to allow hierarchical estimation and model comparison in MATLAB (which I believe is the language used for this analysis;

see https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007043).

We thank the reviewer for this helpful comment and for referring us to relevant methodological work (Piray et al., 2019). We have addressed this point by incorporating hierarchical Bayesian estimation, which effectively mitigates outlier effects and improves model identifiability. The results replicated those obtained with MLE fitting and further revealed group-level differences in key parameters. Please see our detailed response to Reviewer#1 Q1 for the full description of this analysis and results.

(7) Results: Model confusion seems to show that the inequality aversion and social reward models were consistently confused with the baseline model. Is this explained or investigated? I could not find an explanation for this.

The apparent overlap between the inequality aversion (Model 4) and social reward (Model 5) models in the recovery analysis likely arises because neither model includes a learning mechanism, making them unable to capture trial-by-trial adjustments in this dynamic task. Consequently, both were best fit by the baseline model. Please see Response to Reviewer #1 Q3 for related discussion.

(8) Figures 3e and 3f show the correlation between asymmetric learning rates and age. It seems that both a^+ and a^- are around 0.35-0.40 for young adolescents, and this becomes more polarised with age. Could it be that with age comes an increasing discernment of positive and negative outcomes on beliefs, and younger ages compress both positive and negative values together? Given the higher stochasticity in younger ages (\beta), it may also be that these values simply represent higher uncertainty over how to act in any given situation within a social context (assuming the differences in groups are true).

We appreciate this insightful interpretation. Indeed, both α+ and α- cluster around 0.35–0.40 in younger adolescents and become increasingly polarized with age, suggesting that sensitivity to positive versus negative feedback is less differentiated early in development and becomes more distinct over time. This interpretation remains tentative and warrants further validation. Based on this comment, we have revised the Discussion to include this developmental interpretation.

We also clarify that in our model β denotes the inverse temperature parameter; higher β reflects greater choice precision and value sensitivity, not higher stochasticity. Accordingly, adolescents showed higher β values, indicating more value-based and less exploratory choices, whereas adults displayed relatively greater exploratory cooperation. These group differences were also replicated using hierarchical Bayesian estimation (see Response to Reviewer #1 Q1). In response to this comment, we have added a statement in the Discussion highlighting this developmental interpretation.

“Together, these findings suggest that the differentiation between positive and negative learning rates changes with age, reflecting more selective feedback sensitivity in development, while higher β values in adolescents indicate greater value sensitivity. This interpretation remains tentative and requires further validation in future research.”

(9) A parameter partial correlation matrix (off-diagonal) would be helpful to understand the relationship between parameters in both adolescents and adults separately. This may provide a good overview of how the model properties may change with age (e.g. a^+'s relation to \beta).

We thank the reviewer for this helpful comment. We fully agree that a parameter partial correlation matrix can further elucidate the relationships among parameters. Accordingly, we conducted a partial correlation analysis and added the visually presented results to the revised manuscript as Figure 2-figure supplement 4.

(10) It would be helpful to have Bayes Factors reported with each statistical tests given that several p-values fall within the 0.01 and 0.10.

We thank the reviewer for this important recommendation. We have conducted Bayes factor analyses and reported BF10 for all relevant post hoc comparisons. We also clarified our analysis in the Methods and Materials section:

“Post hoc comparisons were conducted using Bayes factor analyses with MATLAB’s bayesFactor Toolbox (version v3.0, Krekelberg, 2024), with a Cauchy prior scale σ = 0.707.”

(11) Discussion: I believe the language around ruling out failures in mentalising needs to be toned down. RL models do not enable formal representational differences required to assess mentalising, but they can distinguish biases in value learning, which in itself is interesting. If the authors were to show that more complex 'ToM-like' Bayesian models were beaten by RL models across the board, and this did not differ across adults and adolescents, there would be a stronger case to make this claim. I think the authors either need to include Bayesian models in their comparison, or tone down their language on this point, and/or suggest ways in which this point might be more thoroughly investigated (e.g., using structured models on the same task and running comparisons: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0087619).

We thank the reviewer for the comments. Please see our response to Reviewer 1 (Appraisal & Discussion section) for details.

Reviewer #2 (Recommendations for the authors):

(1) The authors may want to show the winning model earlier (perhaps near the beginning of the Results section, when model parameters are first mentioned).

We thank the reviewer for this suggestion. We agree that highlighting the winning model early improves clarity. Currently, we have mentioned the winning model before the beginning of the Results section. Specifically, in the penultimate paragraph of the Introduction we state:

“We identified the asymmetric RL learning model as the winning model that best explained the cooperative decisions of both adolescents and adults.”

Reviewer #3 (Recommendations for the authors):

(1) In addition to the points mentioned above, I suggest the following:

Clarify plots by clearly explaining each variable. In particular, the indices 1 vs. 1,2 vs 1,2,3 were not immediately understandable.

We thank the reviewer for this suggestion. We agree that the indices were not immediately clear. We have revised the figure captions (Figure 1 and 4) to explicitly define these terms more clearly:

“The x-axis represents the consistency of the partner’s actions in previous trials (t₋₁: last trial; t_−1,2: last two trials;_t−1,2,3: last three trials).”

(2) It's unclear why the index stops at 3. If this isn't the maximum possible number of consecutive cooperation trials, please consider including all relevant data, as adolescents might show a trend similar to adults over more trials.

We thank the reviewer for raising this point. In our exploratory analyses, we also examined longer streaks of consecutive partner cooperation or defection (up to four or five trials). Two empirical considerations led us to set the cutoff at three in the final analyses. First, the influence of partner behavior diminished sharply with temporal distance. In both GLMMs and LMMs, coefficients for earlier partner choices were small and unstable, and their inclusion substantially increased model complexity and multicollinearity. This recency pattern is consistent with learning and decision models emphasizing stronger weighting of recent evidence (Fudenberg & Levine, 2014; Fudenberg & Peysakhovich, 2016). Second, streaks longer than three were rare, especially among some participants, leading to data sparsity and inflated uncertainty. Including these sparse conditions risked biasing group estimates rather than clarifying them. Balancing informativeness and stability, we therefore restricted the index to three consecutive partner choices in the main analyses, which we believe sufficiently capture individuals’ general tendencies in reciprocal cooperation.

(3) The term "reciprocity" may not be necessary. Since it appears to reflect a general preference for cooperation, it may be clearer to refer to the specific behavior or parameter being measured. This would also avoid confusion, especially since adolescents do show negative reciprocity in response to repeated defection.

We thank you for this comment. In our work, we compute the intrinsic reward for reciprocity as p × ω, where p is the partner cooperation expectation and ω is the cooperation preference. In the rPDG, this value framework manifests as a reciprocity-derived reward: sustained mutual cooperation maximizes joint benefits, and the resulting choice pattern reflects a value for reciprocity, contingent on the expected cooperation of the partner. This quantity enters the trade-off between U_cooperation and U_defection and captures the participant’s intrinsic reward for reciprocity versus the additional monetary reward payoff of defection. Therefore, we consider the term “reciprocity” an acceptable statement for this construct.

(4) Interpretation of parameters should closely reflect what they specifically measure.

We thank the reviewer for pointing this out. We have refined the relevant interpretations of parameters in the current Results and Discussion sections.

(5) Prior research has shown links between Theory of Mind (ToM) and cooperation (e.g., Martínez-Velázquez et al., 2024). It would be valuable to test whether this also holds in your dataset.

We thank the reviewer for this thoughtful comment. Although we did not directly measure participants’ ToM, our design allowed us to estimate participants’ trial-by-trial inferences (i.e., expectations) about their partner’s cooperation probability. We therefore treat these cooperation expectations as an indirect representation for belief inference, which is related to ToM processes. To test whether this belief-inference component relates to cooperation in our dataset, we further conducted an exploratory analysis (GLMM_sup4) in which participants’ choices were regressed on their cooperation expectations, group, and the group × cooperation-expectation interaction, controlling for trial number and gender, with random effects. Consistent with the ToM–cooperation link in prior research (MartínezVelázquez et al., 2024), participants’ expectations about their partner’s cooperation significantly predicted their cooperative behavior (Table 14), suggesting that decisions were shaped by social learning about others’ inferred actions. Moreover, the interaction between group and cooperation expectation was not significant, indicating that this inference-driven social learning process likely operates similarly in adolescents and adults. This aligns with our primary modeling results showing that both age groups update beliefs via an asymmetric learning process. We have reported these analyses in the Appendix Analysis section.

(6) More informative table captions would help the reader. Please clarify how variables are coded (e.g., is female = 0 or 1? Is adolescent = 0 or 1?), to avoid the need to search across the manuscript for this information.

We thank the reviewer for raising this point. We have added clear and standardized variable coding in the table notes of all tables to make them more informative and avoid the need to search the paper. We have ensured consistent wording and formatting across all tables.

(7) I hope these comments are helpful and support the authors in further strengthening their manuscript.

We thank the three reviewers for their comments, which have been helpful in strengthening this work.

References

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Video games can be both negative and positive. Negative because the screen time can cause behavioral issues. Positive because it could aid in the student's skillset to complete academic tasks.

While the data is not mainly negative, it is advised that screen time should be reduced to avoid a decrease in academic performance.

Before the 1830s, newspapers were expensive and mostly for rich political nerds. Benjamin Day changed the game by making the paper smaller, using a faster press, and selling it for just a penny.

The Penny Press turned newspapers into the first true form of mass media.

The text states that Day slashed the price of the paper to a penny and utilized a two cylinder steam engine to massively increase production.

Because the paper became affordable and shifted its focus to human-interest stories rather than just dry politics, it reached the average person for the first time. This expanded the audience from a small elite group to the general public, creating the massmarket journalism we recognize today.

I have experienced this when modifying game files on my PC, finding the directory, opening a file, then finding even more files, and even more when opening one of those.

I think this still occurs in settings like video games. For example, tricking a new player into doing something that every experienced player knows doesn't work or negatively impacts the new player in some way. I see this most often when streamers are trying out a new game (typically well-known and the streamer is just late to playing it, like minecraft), where they suggest things they know are trolls. Something that comes to mind would be like playing a bed in the nether for Minecraft.

Misogyny on the internet seems to be more severe than in real life—especially in the realm of online gaming. At first, I thought this was because gaming is a space that glorifies skill and power, where authority is tied almost exclusively to “game performance,” and stereotypes about women being worse at games lead to a loss of discursive power. However, if misogyny was already pervasive in the early internet, then I think there must be other contributing factors and explanations as well.

Sartre pointed out the strategic advantage of the "bad faith" argument: one party treats the dialogue not as a search for truth, but as a game; the other party, however, is bound by norms (reason, evidence, politeness). Applied to modern trolling, this explains why "seriously responding" often fails—the opponent's goal is not to be persuaded, but to make you invest time and effort, lose patience, and appear "too serious" in the public sphere. Therefore, the strategy is often not to argue more forcefully, but to identify their incentive structure (wasting your time, disrupting order) and reduce your susceptibility to being exploited.

I noticed this in "depression quest" that there was no way of getting a sense of where you were in the game. For me, it made me connect better with my own personal journey through the story.

I found it interesting how broad this definition is and how well it fits many of the video games people play. As someone who plays a lot of games, I can think about how it applies to almost all of them that I am personally familiar with.

As a player, you feel the strong sense of power described in agency previously through solving puzzles and thinking outside of the box. You start to feel that you have outsmarted the game or the circumstances. This accomplished feeling leads to greater engagement with the narrative and gameplay elements.

Through the medium of a game, agency is limited but the environment is engineered in a way that makes helplessness as just another step or another obstacle adding to the enjoyment. Much like Odysseus, you're placed in a situation where there's not much to work with but through your agency you can make what there is of the situation to get out of it.

Great questions to be asking

This is totally understandable. No one would like to be deceived, the feeling of it I can tell that is not good. However this small sections makes me think that how awful it is, as I never realised that authenticity can be this serious. as it made us suspect on ourself that if we can make correct judgement of the posts in the social media. Once I saw a post that it said slide left to see the cat, it turns out to be an advertisement of a boring poorly made game, everyone in the comment was upset and angry.

I do feel like sharing ideas is not bad, when we need help, we don't always walk to the instructor maybe we just ask a classmate and have ideas and become to one another.

It's better to be safe than sorry! You don't want to guess yourself into a grade or a higher education. It's okay to ask questions to your peers or even your instructor.

Farewell words of Ophelia, in her madness, to the ladies of the court of the King of Denmark

"Thanks" in Cockney.

Pharmacist.

Abortion pills.

The barman is telling everyone to get out.

Released from military service.

Folk song.

"The Tempest."

The sound of the nightingale.

Mentions of abuse.

In medieval art, the dolphin was associated with love.

The panels of a coffered ceiling.

The first ecounter of Enea and Didone in Virgil's "Aeneid".

Shakespeare's "Antony and Cleopatra".

God of love.

Satyrical play of Thomas Middleton "A game at chess".

Although bulletin board systems originated decades ago, many websites today still use this forum-style structure. For example, the Dungeons & Dragons (D&D) community relies heavily on well-known bulletin board forums where players share discussions, homebrew rules, and game resources. Similarly, the fighting game MUGEN, which comes from the arcade era, has dedicated forums where users upload character models, stages, and other custom assets. These modern bulletin board systems show how this format continues to support niche communities by organizing discussions and resources in a clear, thread-based way.

In video games one who guides you learning a new game is often called a sherpa. They carry you, but you also remind them of how the game can be fun and not always competitive.

For mechanics, they wouldn't have as much work if beginners didn't break stuff to begin with :)

Rock climbing something repeatedly can feel like doing the same math problem over and over again but still getting the wrong answer until someone else comes over and shows you a new way to get to the solution.

Much like in playing a competitive video game it is extremely helpful to know callout locations with your friends to know where enemies might be. Or to know which items are more valuable to pick up.

Cars it is useful to know if your car is an inline-4 cylinder, V6 etc. Knowing your cars brake system if it vacuum boosted or hydro-boosted will help diagnosing issues etc.

When rock climbing understanding callouts of specific holds, how to belay, types of movements can really help you understand more when climbing with someone else.

Trying to compare this to another community I belong to would be having the tutorial mode on when playing a new game.

Working on an older car where the system is easier and there are a lot of YouTube videos out already on this.

In a rock climbing gym there are routes with grades on them to help people know which routes are going to be more approachable.

In the second verse, the rapper expands on the concept of developing wealth and obtaining financial freedom, which he defines as his only hope.

He also makes reference to his streaming platform, Tidal, which offers a million dollars worth of game through its music catalog for $9.99 a month. Tidal is just one of the many business ventures of the rapper.

In the closing lines, he mocks rappers who take "advances" - a form of loan that record labels offer to artists to finance their albums - and also the trend followed by some rappers on Instagram of showing off money by holding it close to their ears. He reveals, with a clever play on words, that there's a disconnect, we don't call that money over here.

In this song, Jay-Z is constantly pointing out at the importance of property and wealth. He is, therefore, to be considered a Black capitalist. He shows how he strongly believes in economic success as the principal means reaching some form of equality or, at least, some form of upliftment.

However, he does not really believe that wealth and status can be a proper shield from racism; thus, wealth is only a way to obtain a very partial equality.

The first verse is written from the perspective of Lamar's mom, dealing with a 7 year old Kendrick. The biggest fear of the kid is being whooped by his mother, that is constantly threatening him in order to teach the boy discipline.

The scenario Kendrick is depicting is also meant to represent the life of many poor African-Americans as the line County's building on my ass, tryna take my food stamps away is underlying. The Food Stamp Program is a program that provides food to low-income families.

Also, the line I beat yo' ass if you tell them social workers he live here is a reference to the “man in the house” rule, where welfare benefits would be denied to a family if a man resided in the house. Kendrick's mom is alluding to this rule.

The closing line gives the idea of how Kendrick's mom is determined to be the first reason of fear for the boy, and although it is for the reason for his own protection, this same sensation of fear will never leave him and will take more complex forms.

Quote: "Elevate your delivery game with GFS’ proven, tech-powered, multi-carrier logistics and operational excellence." Comment: Internal links - very important for this page in particular

• https://gfsdeliver.com/gfs-in-the-press/in-the-press-how-does-last-mile-delivery-affect-your-ecommerce-brand/
• https://gfsdeliver.com/blog/bringing-it-home-expert-tips-for-last-mile-delivery-in-the-homeware-industry/
• https://gfsdeliver.com/blog/final-mile-delivery-tracking-in-multi-carrier-shipping-to-handle-high-order-volumes/

eLife Assessment

This important study combines a two-person joint hand-reaching paradigm with game-theoretical modeling to examine whether, and how, one's reflexive visuomotor responses are modulated by a partner's control policy and cost structure. The study provides a solid and novel set of behavioral findings suggesting that involuntary visuomotor feedback is indeed modulated in the context of interpersonal coordination. The work will be of interest to cognitive scientists studying the motoric and social aspects of action control.

Reviewer #2 (Public review):

Summary:

Sullivan and colleagues studied the fast, involuntary, sensorimotor feedback control in interpersonal coordination. Using a cleverly designed joint-reaching experiment that separately manipulated the accuracy demands for a pair of participants, they demonstrated that the rapid visuomotor feedback response of a human participant to a sudden visual perturbation is modulated by his/her partner's control policy and cost. The behavioral results are well-matched with the predictions of the optimal feedback control framework implemented with the dynamic game theory model. Overall, the study provides an important and novel set of results on the fast, involuntary feedback response in human motor control, in the context of interpersonal coordination.

Review:

Sullivan and colleagues investigated whether fast, involuntary sensorimotor feedback control is modulated by the partner's state (e.g., cost and control policy) during interpersonal coordination. They asked a pair of participants to make a reaching movement to control a cursor and hit a target, where the cursor's position was a combination of each participant's hand position. To examine fast visuomotor feedback response, the authors applied a sudden shift in either the cursor (experiment 1) or the target (experiment 2) position in the middle of movement. To test the involvement of partner's information in the feedback response, they independently manipulated the accuracy demand for each participant by varying the lateral length of the target (i.e., a wider/narrower target has a lower/higher demand for correction when movement is perturbed). Because participants could also see their partner's target, they could theoretically take this information (e.g., whether their partner would correct, whether their correction would help their partner, etc.) into account when responding to the sudden visual shift. Computationally, the task structure can be handled using dynamic game theory, and the partner's feedback control policy and cost function are integrated into the optimal feedback control framework. As predicted by the model, the authors demonstrated that the rapid visuomotor feedback response to a sudden visual perturbation is modulated by the partner's control policy and cost. When their partner's target was narrow, they made rapid feedback corrections even when their own target was wide (no need for correction), suggesting integration of their partner's cost function. Similarly, they made corrections to a lesser degree when both targets were narrower than when the partner's target was wider, suggesting that the feedback correction takes the partner's correction (i.e., feedback control policy) into account.

The strength of the current paper lies in the combination of clever behavioral experiments that independently manipulate each participant's accuracy demand and a sophisticated computational approach that integrates optimal feedback control and dynamic game theory. Both the experimental design and data analysis sound good. While the main claim is well-supported by the results, the only current weakness is the lack of discussion of limitations and an alternative explanation. Adding these points will further strengthen the paper.

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I would like to clarify a point regarding the discovery of lymphatic-like vessels in the brain. The preprint appears to hint towards positioning it as the first report of this finding. However, the first published study on this subject was by Chang et al.(Ref 1 below). The authors reported on the presence and characteristics (diameters, origin, length, depth and regulation by stress) of deep brain lymphatic vessels. Their work was subsequently followed by two other independent studies (Ref 2 and 3). All studies were reviewed in a review article in 2024 (https://www.sciopen.com/article/10.26599/SAB.2024.9060001?issn=2709-1325).

Therefore, the preprint would represent a fourth report—not the first—of this discovery. It is concerning that previous foundational work has been either overlooked or cited in a misleading manner.

References 1. Chang J, Guo B, Gao Y, Li W, Tong X, Feng Y, et al. Characteristic Features of Deep Brain Lymphatic Vessels and Their Regulation by Chronic Stress. Research (Washington, DC), 2023, 6:0120.

1. Öz E. 'Game changer' method lets scientists peer into mice. Science (New York, NY), 2023, 380(6644):443.

2. Liu X-G, Hua Q, Peng T-T, Chang K-X, Deng C-G, Zhang J-N, et al. Histomorphological analysis of perfusion parameters and CNS lymphatic vessels in mice: an experimental method study. NeuroReport, 2024, 35(3).

Robust

This web using clean HTML and work well with different browsers (Chrome, Safari, Firefox,etc). The content processed correctly by tools as I tested the Safari screen reader.

Next chapter also tried to problematise the idea of serious or commerical games as the saviours of gaming, from the lens that they are embeded in a chokepoint technofeudalism that translates volunteering, modding, community building, hacking, and charitative donations, as a solutionist fix that doesn't change the system, rather tries to cover its holes. Against the idea to mobilise the youngster slackers with mainstream video games, it develops the Shirky principle idea that actually, this contributes to the concentration and surveillance data grab of BigTech that's part of the problem. It specially attacked a book cited in multiple chapters: Ellen Middaugh, and Chris Evans’ The civic potential of video games.

It argues from Baudrillard's complicit euphoria, cult of the ego, hedonism, the society of the spectacle, consumerism, the maximisation - compulsive collection of diverse pleasures. I deleted it because it doesn't provide alternative paths, like how against instrumental solutionism, games must die, and culture must die too, but it must be reborn again and again breaking the Overton window of what's accepted, reborn with non-conventional meanings and feelings, reborn without its elitist concentrated play, without its assumptions (with alternatives instead), and reborn with coordinating transgressive revolution ingrained on it.

Footnote 2 from this chapter expands on this a bit.

And yet, September 12th is too much simple. As a simulation, it only conveys the ideal of violence begets violence, but such a principle can leave players with a sour patronising mouth taste of "I already knew that". The world is more complex, but immersion and its mastery from habit also difficult periodically leaving it and challenging one's perspectives, like it happens in McDonald's, Frostpunk, Mini Metro, Democracy, or Cities Skylines. These simulations can be much more insightful, but the requirement is that players know how to read.

From the text: Frasca proposes that players, not only game designers, potentially impact the ultimate rhetorical “outcome” of a game by channeling the course of play into directions unimagined by the game-maker (2003b, 228). Frasca calls upon Brazilian theater director Augusto Boal’s “Theater of the Oppressed” as a model for how a game can depart from Aristotlean narrative closure. Frasca writes “one of [Boal’s] most popular techniques, re-enacts the same play several times by allowing diffferent audience members to get into the stage and take the protagonist’s role”.

This happens in hacking, modding, and maker cultures, cheating in GTA, in Card/Collection games to give yourself the console and obtain whichever item, like how Minecraft creative mode allows. This "becoming" the designer enables "seeing" through its lens. Counter play can also happen when steering against stereotypical gameful intentions, as with Disaster Sims in the series with the same name, or as prompted in reflection simulation games like Proteus.

That's where designing and maker precepts also come in. In reading a game, in watching it reflectively, in playing as a designer, a deconstructor. This is not often taught to players. An issue with the argument is that when they leave, they may leave out of frustration, which can cause missunderstandings and not prompt reflection. It can make these players abstain from simulation genres as a whole, and engage in more arcade "neutral" (immediate gratification) f2p titles.

Flash games died, no... Adobe killed them. Flash games were free. They lived on Kongregate, on Newgrounds, on Miniclip.

As someone learning a new language I have had this exact experience! I started playing a game in Japanese when I was younger without speaking any. I recently started playing again and not only can I understand so much more, I can read and use context to learn new stuff!! It's been such a cool experience!

Indigenous people here are being talked about like if they were pieces in a game of chess rather than actual people. It's a common theme I've seen throughout many of these passages.

As the eastern lands were taken up, migration flowed across them to the west. Daniel Boone, the great backwoodsman, who combined the occupations of hunter, trader, cattle-raiser, farmer, and surveyor -- learning, probably from the traders, of the fertility of the lands of the upper Yadkin, where the traders were wont to rest as they took their way to the Indians, left his Pennsylvania home with his father, and passed down the Great Valley road to that stream.

Learning from a trader whose posts were on the Red River in Kentucky of its game and rich pastures, he pioneered the way for the farmers to that region. Thence he passed to the frontier of Missouri, where his settlement was long a landmark on the frontier. Here again he helped to open the way for civilization, finding salt licks, and trails, and land.

Prob predictable

RPI game design major

Like 60s rhetoric education

Like early days of rhetcomp but with corporate funding pressure

Of course

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Synthèse : Prévention et lutte contre le harcèlement scolaire (Webinaire FCPE-MAE)

Résumé exécutif

Le harcèlement scolaire est une problématique systémique qui touche environ un élève sur dix.

Face à ce constat, le webinaire organisé par la FCPE et la MAE souligne l'impératif d'une action concertée entre parents, professionnels de l'éducation et partenaires institutionnels.

L'approche défendue repose sur trois piliers : la détection précoce des signaux d'alerte, l'utilisation d'outils pédagogiques adaptés à chaque tranche d'âge (de la maternelle au lycée), et une coéducation active.

La MAE, partenaire historique de l'enseignement public, met à disposition des ressources gratuites et agréées par le Ministère de l'Éducation nationale, s'inscrivant notamment dans le cadre du programme national Phare.

L'objectif central est de briser le silence et de passer d'une logique de réaction à une culture de prévention durable.

--------------------------------------------------------------------------------

1. Analyse du phénomène de harcèlement scolaire

Définitions et mécanismes

Le harcèlement se caractérise par un rapport de force déséquilibré où une ou plusieurs personnes exercent une pression ou un contrôle répété sur une victime.

• Formes constatées : Insultes, moqueries, rumeurs, humiliations et mises à l'écart.

• Évolution : Les situations débutent souvent par des faits perçus comme « pour rire » avant de déraper vers une souffrance physique et psychologique grave.

Le défi du cyber-harcèlement

Le cyber-harcèlement transpose ces violences sur les réseaux sociaux, les messageries, les forums et les jeux vidéo.

• Gravité : La circulation des attaques est extrêmement rapide et peut toucher une audience très large.

• Traces : Les agressions en ligne laissent des marques durables et ne s'arrêtent pas aux portes de l'école.

• Statistique clé : Un collégien sur cinq a déjà été victime d'au moins un acte de cyber-violence répété.

Signaux d'alerte pour les adultes

La vigilance des parents et des enseignants doit se porter sur les changements de comportement :

• État émotionnel : Isolement, colère, tristesse subite.

• Vie scolaire : Baisse des résultats, refus d'aller en cours ou de participer à certaines activités.

• Santé physique : Troubles du sommeil, de l'appétit, maux de tête ou de ventre fréquents.

• Signes matériels : Vêtements abîmés, perte d'effets personnels.

• Rapport au numérique : Enfant qui cache son téléphone ou le consulte avec une angoisse permanente.

--------------------------------------------------------------------------------

2. Le cadre institutionnel et l'engagement de la MAE

Un acteur historique

Fondée en 1932 par des enseignants, la MAE est une mutuelle issue de l'économie sociale et solidaire. Elle bénéficie de l'agrément national du Ministère de l'Éducation nationale pour intervenir dans les établissements scolaires.

Soutien aux familles et garanties

Au-delà de la prévention, la MAE propose des protections spécifiques dans ses contrats d'assurance :

• Soutien psychologique en cas de harcèlement avéré.

• Assistance juridique en cas d'atteinte à l'image de l'enfant.

• Aide à la suppression de contenus malveillants sur Internet.

Le programme Phare

Les outils présentés s'inscrivent dans le dispositif ministériel Phare, qui repose sur cinq piliers :

1. Éduquer pour prévenir les phénomènes de harcèlement.

2. Former une communauté protectrice autour des élèves.

3. Intervenir efficacement sur les situations de harcèlement.

4. Associer les parents et les partenaires.

5. Mobiliser les instances de démocratie scolaire (CESCE).

--------------------------------------------------------------------------------

3. Ressources et outils pédagogiques par cycles

Les ressources proposées sont gratuites et conçues en collaboration avec des professionnels de l'éducation (notamment l'AGEEM pour le premier degré).

Pour les 3 - 11 ans (Maternelle et Élémentaire)

| Outil | Description | Objectif | | --- | --- | --- | | Album "Maël le roi des bêtises" | Support de 25 pages avec cahier d'activités. | Apprendre le respect des différences et le vivre-ensemble dès le plus jeune âge. | | BD "Main dans la main" | Format innovant (illustration à gauche, exploitation pédagogique à droite). | Présenter les points de vue de tous les acteurs : victime, harceleur, aidant, suiveur, adulte. | | Jeu de l'oie "Non au harcèlement" | Mallette physique ou version dématérialisée (TBI). | Utiliser le jeu comme prétexte au débat et à l'échange collectif. |

Pour les 11 - 18 ans (Collège et Lycée)

• Jeu de l'oie spécialisé : Orienté vers le harcèlement sexuel, sexiste et homophobe (Cycle 4).

• BD "La Jungle" : Récit d'une rentrée en collège basée sur des témoignages réels, incluant une trousse à outils et des liens utiles.

• "Le Labyrinthe de Nina" (Serious Game) :

◦ Concept : Jeu immersif où le joueur explore le smartphone d'une lycéenne disparue pour comprendre les mécanismes du cyber-harcèlement.  

◦ Partenariat : Développé avec l'association e-Enfance (gestionnaire du 3018).  

◦ Versions : Une version grand public (60 min) et une version "Express" (30 min) pour les ateliers scolaires, facilitant la médiation par l'enseignant.

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4. Supports multimédias et prévention numérique

La MAE développe des formats variés pour s'adapter aux nouveaux usages des familles :

• Podcasts :

◦ Au-delà du miroir : Témoignages de jeunes sur la différence, la discrimination et la résilience.  

◦ Nos enfants, les écrans et Internet : Épisodes dédiés à la pornographie en ligne, aux réseaux sociaux et aux jeux vidéo.   

◦ Parentalité accompagnée : Focus sur la santé mentale et l'égalité filles-garçons.

• Vidéos "3 minutes pour comprendre" : Décryptage par Natacha Waro, psychologue clinicienne, pour identifier les signaux d'alerte et savoir comment agir.

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5. Modalités de déploiement et collaboration territoriale

Accès aux outils

• Numérique : Téléchargement gratuit sur les sites mae.fr ou labyrinthedenina.fr, et sur les stores d'applications mobiles (Android/iOS).

• Physique : Les mallettes et albums sont distribués via les réseaux de délégués départementaux de la MAE. Les parents peuvent solliciter ces délégués via un formulaire sur le site national.

Rôle des parents et coéducation

• Ambassadeurs : Les parents d'élèves sont encouragés à informer les directions d'école de l'existence de ces outils agréés.

• Actions locales : Collaboration possible pour organiser des "Cafés parents", des tables rondes ou des animations lors des assemblées générales de la FCPE.

• Obligations légales : Il est rappelé que depuis 2022, les enseignants ont l'obligation de se former à la lutte contre le harcèlement scolaire.

Vigilance sur les intervenants

Il est crucial de vérifier l'agrément des intervenants extérieurs.

Le Ministère de l'Éducation nationale publie une liste officielle des associations autorisées à intervenir en milieu scolaire afin d'éviter les dérives ou les discours non conformes aux valeurs de la République.

The goal keeper is the only person who can touch it with their hands or when someone is doing a throw in

I had no idea that the tradition of Football on Thanksgiving dated all the way back to the 19th century. I would've assumed it was a more modern thing meant to sell advertisements on television and radio.

this is the WORST!

Quantify and I will keep stressing this. QUANTIFY! Like how much time did you save (estimate if you have to) when entering game results vs manual calculation. It's all metrics!

Then same with WEb-based front end using what and modern design etc something to stand out.

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anecdotal lead

Reviewer #3 (Public review):

This paper applies a computational model to behavior in a probabilistic operant reward learning task (a 3-armed bandit) to uncover differences between individuals with temporomandibular disorder (TMD) compared with healthy controls. Integrating computational principles and models into pain research is an important direction, and the findings here suggest that TMD is associated with subtle changes in how uncertainty is represented over time as individuals learn to make choices that maximize reward. There are a number of strengths, including the comparison of a volatile Kalman filter (vKF) model to some standard base models (Rescorla Wagner with 1 or 2 learning rates) and parameter recovery analyses suggesting that the combination of task and vKF model may be able to capture some properties of learning and decision-making under uncertainty that may be altered in those suffering from chronic pain-related conditions.

I've focused my comments in four areas: (1) Questions about the patient population, (2) Questions about what the findings here mean in terms of underlying cognitive/motivational processes, (3) Questions about the broader implications for understanding individuals with TMD and other chronic pain-related disorders, and (4) Technical questions about the models and results.

(1) Patient population

This is a computational modelling study, so it is light on characterization of the population, but the patient characteristics could matter. The paper suggests they were hospitalized, but this is not a condition that requires hospitalization per se. It would be helpful to connect and compare the patient characteristics with large-scale studies of TMD, such as the OPPERA study led by Maixner, Fillingim, and Slade.

(2) What cognitive/motivational processes are altered in TMD

The study finds a pattern of alterations in TMD patients that seems clear in Figure 2. Healthy controls (HC) start the task with high estimates of volatility, uncertainty, and learning rate, which drop over the course of the task session. This is consistent with a learner that is initially uncertain about the structure of the environment (i.e., which options are rewarded and how the contingencies change over time) but learns that there is a fixed or slowly changing mean and stationary variance. The TMD patients start off with much lower volatility, uncertainty, and learning rate - which are actually all near 0 - and they remain stable over the course of learning. This is consistent with a learner who believes they know the structure of the environment and ignores new information.

What is surprising is that this pattern of changes over time was found in spite of null group differences in a number of aspects of performance: (1) stay rate, (2) switch rate, (3) win-stay/lose-switch behaviors, (4) overall performance (corrected for chance level), (5) response times, (6) autocorrelation, (7) correlations between participants' choice probability and each option's average reward rate, (7) choice consistency (though how operationalized is not described?), (8) win-stay-lose-shift patterns over time. I'm curious about how the patterns in Figure 2 would emerge if standard aspects of performance are essentially similar across groups (though the study cannot provide evidence in favor of the null). It will be important to replicate these patterns in larger, independent samples with preregistered analyses.

The authors believe that this pattern of findings reveals that TMD patients "maintain a chronically heightened sensitivity to environmental changes" and relate the findings to predictive processing, a hallmark of which (in its simplest form) is precision-weighted updating of priors. They also state that the findings are not related to reduced overall attentiveness or failure to understand the task, but describe them as deficits or impairments in calibrating uncertainty.

The pattern of differences could, in fact, result from differences in prior beliefs, conceptualization of the task, or learning. Unpacking these will be important steps for future work, along with direct measures of priors, cognitive processes during learning, and precision-weighted updating.

(3) Implications for understanding chronic pain

If the findings and conclusions of the paper are correct, individuals with TMD and perhaps other pain-related disorders may have fundamental alterations in the ways in which they make decisions about even simple monetary rewards. The broader questions for the field concern (1) how generalizable such alterations are across tasks, (2) how generalizable they are across patient groups and, conversely, how specific they are to TMD or chronic pain, (3) whether they are the result of neurological dysfunction, as opposed to (e.g.) adaptive strategies or assumptions about the environment/task structure.

It will be important to understand which features of patients' and/or controls' cognition are driving the changes. For example, could the performance differences observed here be attributable to a reduced or altered understanding of the task instructions, more uncertainty about the rules of the game, different assumptions about environments (i.e., that they are more volatile/uncertain or less so), or reduced attention or interest in optimizing performance? Are the controls OVERconfident in their understanding of the environment?

This set of questions will not be easy to answer and will be the work of many groups for many years to come. It is a judgment call how far any one paper must go to address them, but my view is that it is a collaborative effort. Start with a finding, replicate it across labs, take the replicable phenomena and work to unpack the underlying questions. The field must determine whether it is this particular task with this model that produces case-control differences (and why), or whether the findings generalize broadly. Would we see the same findings for monetary losses, sounds, and social rewards? Tasks with painful stimuli instead of rewards?

Another set of questions concerns the space of computational models tested, and whether their parameters are identifiable. An alteration in estimated volatility or learning rate, for example, can come from multiple sources. In one model, it might appear as a learning rate change and in another as a confirmation bias. It would be interesting in this regard to compare the "mechanisms" (parameters) of other models used in pain neuroscience, e.g., models by Seymour, Mancini, Jepma, Petzschner, Smith, Chen, and others (just to name a few).

One immediate next step here could be to formally compare the performance of both patients and controls to normatively optimal models of performance (e.g., Bayes optimal models under different assumptions). This could also help us understand whether the differences in patients reflect deficits and what further experiments we would need to pin that down.<br /> In addition, the volatility parameter in the computational model correlated with apathy. This is interesting. Is there a way to distinguish apathy as a particular clinical characteristic and feature of TMD from apathy in the sense of general disinterest in optimal performance that may characterize many groups?

If we know this, what actionable steps does it lead us to take? Could we take steps to reduce apathy and thus help TMD patients better calibrate to environmental uncertainty in their lives? Or take steps to recalibrate uncertainty (i.e., increase uncertainty adaptation), with benefits on apathy? A hallmark of a finding that the field can build off of is the questions it raises.

(4) Technical questions about the models and results

Clarification of some technical points would help interpret the paper and findings further:

(a) Was the reward probability truly random? Was the random walk different for each person, or constrained?

(b) When were self-report measures administered, and how?

(c) Pain assessments: What types of pain? Was a body map assessed? Widespreadness? Pain at the time of the test, or pain in general?

(d) Parameter recovery: As you point out, r = 0.47 seems very low for recovery of the true quantity, but this depends on noise levels and on how the parameter space is sampled. Is this noise-free recovery, and is it robust to noise? Are the examples of true parameters drawn from the space of participants, or do they otherwise systematically sample the space of true parameters?

(e) What are the covariances across parameter estimates and resultant confusability of parameter estimates (e.g., confusion matrix)?

(f) It would be helpful to have a direct statistical comparison of controls and TMD on model parameter estimates.

(g) Null statistical findings on differences in correlations should not be interpreted as a lack of a true effect. Bayes Factors could help, but an analysis of them will show that hundreds of people are needed before it is possible to say there are no differences with reasonable certainty. Some journals enforce rules around the kinds of language used to describe null statistical findings, and I think it would be helpful to adopt them more broadly.

(h) What is normatively optimal in this task? Are TMD patients less so, or not? The paper states "aberrant precision (uncertainty) weighting and misestimation of environmental volatility". But: are they misestimates?

(i) It's not clear how well the choice of prior variance for all parameters (6.25) is informed by previous research, as sensible values may be task- and context-dependent. Are the main findings robust to how priors are specified in the HBI model?

This passage uses three levels to remind us that "robots" themselves do not equate to intelligence or objectivity. Tay's "contamination" illustrates that machine learning-based conversational robots absorb biases from the platform as "language norms"—when training data comes from an environment full of provocation and racism, the system becomes an amplifier of prejudice; the problem is not just a technical failure, but a governance failure of treating a "public platform" as a safe training ground. Next, the "registered vs. unregistered bots" reveal the cat-and-mouse game of platform regulation and countermeasures: API restrictions act as rules and guardrails, while simulated clicks bypassing APIs disguise automation as "human," making it harder for platforms to track, demonstrating that visibility and controllability are themselves forms of power. Finally, the "fake bots" point to another form of deception: humans pretending to be AI to gain traffic, a sense of mystery, or immunity from responsibility—this blurs the line of "authenticity" and reminds us that in the attention economy, technological identity can also be used for performance and marketing.

I once would have said this was disgusting, but I have grown beyond that

"Summing up the comparative observations on these three dimensions, these cases show the strengths and weaknesses of EU bicameralism, as it enters into new, sensitive areas of market regulation. Member States do not yet consider EU bicameralism as the only game in town; in this respect, agriculture is no different from financial regulation. However, MEPs still have a considerable influence on the daily process of bicameral law-making by instituting democratic control and pluralistic representation. The analysis shows that faced with pressure, MEPs reacted very differently in agriculture and in financial affairs, resulting in a lower degree of EP autonomy from the Council in agriculture than in financial affairs. These different outcomes probably reflect a range of circumstances and conditions, which researchers now need to disentangle. They likely include the extent to which EP committees are used to working with co-decision, the intensity of public pressure, and the configuration of policy networks."

I can see various characterizations, themes and stylistic devices, which I will discuss below

Changer photo Escape Game

I found this very interesting. At my high school, a long-time teacher who's been there for 25+ years, who's also the head football coach, Steve Valach, emphasizes the word "Ubuntu" at the kickoff assembly each year during the first week of school. When I heard about it for the first time nearly 4.5 years ago, he made it so memorable because it means "I am because we are." That is the key part about ubuntu, the connectedness it creates, the team aspect of it, it's harmonious as it says in the text because it's a unity feeling. In football when the game is really close, sometimes, I see a huddle of how a team is going to win a nail biting game, if the game is 28-27, "ubuntu" comes into mind because the offense needs to have receivers catching, good routing, special teams making it harder for the other team to score, defense stepping up to the plate, and when the clock goes to 0, it really has that feeling of "We won the game, everyone contributed." Which is, in my mind, that feeling of "Ubuntu." everyone pitched in, nobody did something where it hindered someone's capabilities, everyone was capable. This idea also connects to virtue ethics, because it emphasizes developing good character through cooperation, respect, and helping others succeed.

Ouch! Colonialism is compatible with black supremacy, by the way. Not saying this is what the collection purports, as Lindsay is clearly indigenous-conscious, but this is a potentially dangerous blind spot that equalises black games to mainland US black games...

Blackness is not a single entity. It gradually emerged as a label after the trading routes of slavic people (slaves) ended with the Ottoman conquest of Constantinople in 1453 that then changed slave routes from north Europe and Asia to more of Sub-Saharan Africa (and then came the colonisation age, and the Christian vs. non-Christian ideology of Columbus and the Catholic Kings). The label was met with sympathy by European aristocrats and traders seeing in it an easier gateway into differentiation, and was later legitimised by historians in race taxonomies, and phrenology.

SV techies plan monthly trips to China, as indicator for how China is doing and how US tech sees it

Luhmann indicates that pretty much anything is game for putting into a zettelkasten.

5:44 "US intelligence agencies ... are now saying that, based on intelligence they have, that they do believe that Russia and Putin are not going to stop with just Ukraine., they believe that they are going to continue on, and try to take at least some parts of Europe."

plausible. europe will drown in blackout, hunger, cold, civil war, hyperinflation, ... so russia will have an easy game to take whole europe, assuming russia does not collapse first under the pressure of sanctions. but when russia collapses, there is china... europe has no resources for industry, but maybe europe would be a nice place to live.

What is cyberbullying and how does it differ from traditional bullying?

security Online harassment

It's actually acceptable to tweak as the game matures and people find new ways to play it. Don't think you have to get it perfect first time.

In half a year! Some fundraisers and charity streams get that in a day. Some companies extract this from users every minute.

Mainly through "white mansplaining", which in this case is not done by white people who pursue activism to understand others and de-privilege themselves, but rather as a throwable weapon dismissive argument of "I already know your perspective" which mainly seeks to perpetuate hierachies of oppression and silence.

Reminds me of Ontario's Access to Justice game (https://ojen.ca/en/resource/access-to-justice-game/), except this one is more like A Normal Lost phone, or like Wednesdays (https://artefrance.itch.io/wednesdays).

Note that male-to-male violence also exists! I have been threatened too, yet this is much less usually talked about. It is normalised, invisibilised, naturalised.

Rocket League told you when action had been taken!

But most players won't. Considering most games have a finish rate of ~25%, this type of reading and interaction... is at most relegated to 10% of the playerbase, being optimistic. That can be a lot, but it's not "the mainstream" audience.

Developed on the book Playthrough Poetics, and on the Conclusion of a posterior Jennings' article (https://www.gamejournal.it/a-meta-synthesis-of-agency-in-game-studies-trends-troubles-trajectories-s-c-jennings/).

As in mutual respect: I tend for you, and you tend for me. An issue is that people may deny their vulnerability. They may dismiss it as any other attribute or sentiment. They may be infatuated with riches, and think they are immune behind the screen, relying on the trope that only girls cry, and that psych harm is separate from physical one. Further, practicing ethics of care requires a lot of time and continuous effort to minimise oneself, to revise and make one obsolete. To stop creating and start listening, to not pursue firstness, but secondariness, to be invisible, not acknowledged, not praised. It is tough, and can lead to burnout.

Two arguments are being made here: First, player events impact non-players. Second: Players, even if thought invisible, leave bodily tracks.

Player events can be festivals, performances, but also cultural shenanigans and terms like inting, or gg ez, which can convey a competitive way of narrating out of the gaming sphere. Players leave tracks the moment they download a game, in the form of cookies, if the game requires Internet connection or has an anti-cheat tool (Riot Vanguard), if it has a log that gets mixed with OS files, or if it has a public profile linkage like a Rocket League ranking tracker, modding store, Mario Maker level, Steam user, or Animal Crossing island. These are our creations, extensions —limbs— of ourselves, of our image, and ideas.

eLife Assessment

This important work investigates cooperative behaviors in adolescents using a repeated Prisoner's Dilemma game. The computational modeling approach used in the study is solid and rigorous. The work could be further strengthened with the consideration of modeling higher-order social inferences and non-linear relationships between age and observed behavior. Findings from this study will be of interest to developmental psychologists, economists, and social psychologists.

Reviewer #1 (Public review):

Summary:

Wu and colleagues aimed to explain previous findings that adolescents, compared to adults, show reduced cooperation following cooperative behaviour from a partner in several social scenarios. The authors analysed behavioural data from adolescents and adults performing a zero-sum Prisoner's Dilemma task and compared a range of social and non-social reinforcement learning models to identify potential algorithmic differences. Their findings suggest that adolescents' lower cooperation is best explained by a reduced learning rate for cooperative outcomes, rather than differences in prior expectations about the cooperativeness of a partner. The authors situate their results within the broader literature, proposing that adolescents' behaviour reflects a stronger preference for self-interest rather than a deficit in mentalising.

Strengths:

The work as a whole suggests that, in line with past work, adolescents prioritise value accumulation, and this can be, in part, explained by algorithmic differences in wegithed value learning. The authors situate their work very clearly in past literature, and make it obvious the gap they are testing and trying to explain. The work also includes social contexts which move the field beyond non-social value accumulation in adolescents. The authors compare a series of formal approaches that might explain the results and establish generative and model-comparison procedures to demonstrate the validity of their winning model and individual parameters. The writing was clear, and the presentation of the results was logical and well-structured.

Weaknesses:

I had some concerns about the methods used to fit and approximate parameters of interest. Namely, the use of maximum likelihood versus hierarchical methods to fit models on an individual level, which may reduce some of the outliers noted in the supplement, and also may improve model identifiability.

There was also little discussion given the structure of the Prisoner's Dilemma, and the strategy of the game (that defection is always dominant), meaning that the preferences of the adolescents cannot necessarily be distinguished from the incentives of the game, i.e. they may seem less cooperative simply because they want to play the dominant strategy, rather than a lower preferences for cooperation if all else was the same.

The authors have now addressed my comments and concerns in their revised version.

Appraisal & Discussion:

Overall, I believe this work has the potential to make a meaningful contribution to the field. Its impact would be strengthened by more rigorous modelling checks and fitting procedures, as well as by framing the findings in terms of the specific game-theoretic context, rather than general cooperation.

Comments on revisions:

Thank you to the authors for addressing my comments and concerns.

Reviewer #2 (Public review):

Summary:

This manuscript investigates age-related differences in cooperative behavior by comparing adolescents and adults in a repeated Prisoner's Dilemma Game (rPDG). The authors find that adolescents exhibit lower levels of cooperation than adults. Specifically, adolescents reciprocate partners' cooperation to a lesser degree than adults do. Through computational modeling, they show that this relatively low cooperation rate is not due to impaired expectations or mentalizing deficits, but rather a diminished intrinsic reward for reciprocity. A social reinforcement learning model with asymmetric learning rate best captured these dynamics, revealing age-related differences in how positive and negative outcomes drive behavioral updates. These findings contribute to understanding the developmental trajectory of cooperation and highlight adolescence as a period marked by heightened sensitivity to immediate rewards at the expense of long-term prosocial gains.

Strengths:

(1) Rigid model comparison and parameter recovery procedure.

(2) Conceptually comprehensive model space.

(3) Well-powered samples.

Weaknesses:

A key conceptual distinction between learning from non-human agents (e.g., bandit machines) and human partners is that the latter are typically assumed to possess stable behavioral dispositions or moral traits. When a non-human source abruptly shifts behavior (e.g., from 80% to 20% reward), learners may simply update their expectations. In contrast, a sudden behavioral shift by a previously cooperative human partner can prompt higher-order inferences about the partner's trustworthiness or the integrity of the experimental setup (e.g., whether the partner is truly interactive or human). The authors may consider whether their modeling framework captures such higher-order social inferences. Specifically, trait-based models-such as those explored in Hackel et al. (2015, Nature Neuroscience)-suggest that learners form enduring beliefs about others' moral dispositions, which then modulate trial-by-trial learning. A learner who believes their partner is inherently cooperative may update less in response to a surprising defection, effectively showing a trait-based dampening of learning rate.

This asymmetry in belief updating has been observed in prior work (e.g., Siegel et al., 2018, Nature Human Behaviour) and could be captured using a dynamic or belief-weighted learning rate. Models incorporating such mechanisms (e.g., dynamic learning rate models as in Jian Li et al., 2011, Nature Neuroscience) could better account for flexible adjustments in response to surprising behavior, particularly in the social domain.

Second, the developmental interpretation of the observed effects would be strengthened by considering possible non-linear relationships between age and model parameters. For instance, certain cognitive or affective traits relevant to social learning-such as sensitivity to reciprocity or reward updating-may follow non-monotonic trajectories, peaking in late adolescence or early adulthood. Fitting age as a continuous variable, possibly with quadratic or spline terms, may yield more nuanced developmental insights.

Finally, the two age groups compared-adolescents (high school students) and adults (university students)-differ not only in age but also in sociocultural and economic backgrounds. High school students are likely more homogenous in regional background (e.g., Beijing locals), while university students may be drawn from a broader geographic and socioeconomic pool. Additionally, differences in financial independence, family structure (e.g., single-child status), and social network complexity may systematically affect cooperative behavior and valuation of rewards. Although these factors are difficult to control fully, the authors should more explicitly address the extent to which their findings reflect biological development versus social and contextual influences.

Comments on revisions:

The authors have adequately addressed my previous comments.

Author response:

The following is the authors’ response to the original reviews.

Public reviews:

Reviewer #1 (Public review):

Summary:

Wu and colleagues aimed to explain previous findings that adolescents, compared to adults, show reduced cooperation following cooperative behaviour from a partner in several social scenarios. The authors analysed behavioural data from adolescents and adults performing a zero-sum Prisoner's Dilemma task and compared a range of social and non-social reinforcement learning models to identify potential algorithmic differences. Their findings suggest that adolescents' lower cooperation is best explained by a reduced learning rate for cooperative outcomes, rather than differences in prior expectations about the cooperativeness of a partner. The authors situate their results within the broader literature, proposing that adolescents' behaviour reflects a stronger preference for self-interest rather than a deficit in mentalising.

Strengths:

The work as a whole suggests that, in line with past work, adolescents prioritise value accumulation, and this can be, in part, explained by algorithmic differences in weighted value learning. The authors situate their work very clearly in past literature, and make it obvious the gap they are testing and trying to explain. The work also includes social contexts that move the field beyond non-social value accumulation in adolescents. The authors compare a series of formal approaches that might explain the results and establish generative and modelcomparison procedures to demonstrate the validity of their winning model and individual parameters. The writing was clear, and the presentation of the results was logical and well-structured.

We thank the reviewer for recognizing the strengths of our work.

Weaknesses:

(1) I also have some concerns about the methods used to fit and approximate parameters of interest. Namely, the use of maximum likelihood versus hierarchical methods to fit models on an individual level, which may reduce some of the outliers noted in the supplement, and also may improve model identifiability.

We thank the reviewer for this suggestion. Following the comment, we added a hierarchical Bayesian estimation. We built a hierarchical model with both group-level (adolescent group and adult group) and individual-level structures for the best-fitting model. Four Markov chains with 4,000 samples each were run, and the model converged well (see Figure supplement 7).

We then analyzed the posterior parameters for adolescents and adults separately. The results were consistent with those from the MLE analysis. These additional results have been included in the Appendix Analysis section (also see Figure supplement 5 and 7). In addition, we have updated the code and provided the link for reference. We appreciate the reviewer’s suggestion, which improved our analysis.

(2) There was also little discussion given the structure of the Prisoner's Dilemma, and the strategy of the game (that defection is always dominant), meaning that the preferences of the adolescents cannot necessarily be distinguished from the incentives of the game, i.e. they may seem less cooperative simply because they want to play the dominant strategy, rather than a lower preferences for cooperation if all else was the same.

We thank the reviewer for this comment and agree that adolescents’ lower cooperation may partly reflect a rational response to the incentive structure of the Prisoner’s Dilemma. 

However, our computational modeling explicitly addressed this possibility. Model 4 (inequality aversion) captures decisions that are driven purely by self-interest or aversion to unequal outcomes, including a parameter reflecting disutility from advantageous inequality, which represents self-oriented motives. If participants’ behavior were solely guided by the payoff-dominant strategy, this model should have provided the best fit. However, our model comparison showed that Model 5 (social reward) performed better in both adolescents and adults, suggesting that cooperative behavior is better explained by valuing social outcomes beyond payoff structures.

Besides, if adolescents’ lower cooperation is that they strategically respond to the payoff structure by adopting defection as the more rewarding option. Then, adolescents should show reduced cooperation across all rounds. Instead, adolescents and adults behaved similarly when partners defected, but adolescents cooperated less when partners cooperated and showed little increase in cooperation even after consecutive cooperative responses. This pattern suggests that adolescents’ lower cooperation cannot be explained solely by strategic responses to payoff structures but rather reflects a reduced sensitivity to others’ cooperative behavior or weaker social reciprocity motives. We have expanded our Discussion to acknowledge this important point and to clarify how the behavioral and modeling results address the reviewer’s concern.

“Overall, these findings indicate that adolescents’ lower cooperation is unlikely to be driven solely by strategic considerations, but may instead reflect differences in the valuation of others’ cooperation or reduced motivation to reciprocate. Although defection is the payoff-dominant strategy in the Prisoner’s Dilemma, the selective pattern of adolescents’ cooperation and the model comparison results indicate that their reduced cooperation cannot be fully explained by strategic incentives, but rather reflects weaker valuation of social reciprocity.”

Appraisal & Discussion:

(3) The authors have partially achieved their aims, but I believe the manuscript would benefit from additional methodological clarification, specifically regarding the use of hierarchical model fitting and the inclusion of Bayes Factors, to more robustly support their conclusions. It would also be important to investigate the source of the model confusion observed in two of their models.

We thank the reviewer for this comment. In the revised manuscript, we have clarified the hierarchical Bayesian modeling procedure for the best-fitting model, including the group- and individual-level structure and convergence diagnostics. The hierarchical approach produced results that fully replicated those obtained from the original maximumlikelihood estimation, confirming the robustness of our findings. Please also see the response to (1).

Regarding the model confusion between the inequality aversion (Model 4) and social reward (Model 5) models in the model recovery analysis, both models’ simulated behaviors were best captured by the baseline model. This pattern arises because neither model includes learning or updating processes. Given that our task involves dynamic, multi-round interactions, models lacking a learning mechanism cannot adequately capture participants’ trial-by-trial adjustments, resulting in similar behavioral patterns that are better explained by the baseline model during model recovery. We have added a clarification of this point to the Results:

“The overlap between Models 4 and 5 likely arises because neither model incorporates a learning mechanism, making them less able to account for trial-by-trial adjustments in this dynamic task.”

(4) I am unconvinced by the claim that failures in mentalising have been empirically ruled out, even though I am theoretically inclined to believe that adolescents can mentalise using the same procedures as adults. While reinforcement learning models are useful for identifying biases in learning weights, they do not directly capture formal representations of others' mental states. Greater clarity on this point is needed in the discussion, or a toning down of this language.

We sincerely thank the reviewer for this professional comment. We agree that our prior wording regarding adolescents’ capacity to mentalise was somewhat overgeneralized. Accordingly, we have toned down the language in both the Abstract and the Discussion to better align our statements with what the present study directly tests. Specifically, our revisions focus on adolescents’ and adults’ ability to predict others’ cooperation in social learning. This is consistent with the evidence from our analyses examining adolescents’ and adults’ model-based expectations and self-reported scores on partner cooperativeness (see Figure 4). In the revised Discussion, we state:

“Our results suggest that the lower levels of cooperation observed in adolescents stem from a stronger motive to prioritize self-interest rather than a deficiency in predicting others’ cooperation in social learning”.

(5) Additionally, a more detailed discussion of the incentives embedded in the Prisoner's Dilemma task would be valuable. In particular, the authors' interpretation of reduced adolescent cooperativeness might be reconsidered in light of the zero-sum nature of the game, which differs from broader conceptualisations of cooperation in contexts where defection is not structurally incentivised.

We thank the reviewer for this comment and agree that adolescents’ lower cooperation may partly reflect a rational response to the incentive structure of the Prisoner’s Dilemma. However, our behavioral and computational evidence suggests that this pattern cannot be explained solely by strategic responses to payoff structures, but rather reflects a reduced sensitivity to others’ cooperative behavior or weaker social reciprocity motives. We have expanded the Discussion to acknowledge this point and to clarify how both behavioral and modeling results address the reviewer’s concern (see also our response to 2).

(6) Overall, I believe this work has the potential to make a meaningful contribution to the field. Its impact would be strengthened by more rigorous modelling checks and fitting procedures, as well as by framing the findings in terms of the specific game-theoretic context, rather than general cooperation.

We thank the reviewer for the professional comments, which have helped us improve our work.

Reviewer #2 (Public review):

Summary:

This manuscript investigates age-related differences in cooperative behavior by comparing adolescents and adults in a repeated Prisoner's Dilemma Game (rPDG). The authors find that adolescents exhibit lower levels of cooperation than adults. Specifically, adolescents reciprocate partners' cooperation to a lesser degree than adults do. Through computational modeling, they show that this relatively low cooperation rate is not due to impaired expectations or mentalizing deficits, but rather a diminished intrinsic reward for reciprocity. A social reinforcement learning model with asymmetric learning rate best captured these dynamics, revealing age-related differences in how positive and negative outcomes drive behavioral updates. These findings contribute to understanding the developmental trajectory of cooperation and highlight adolescence as a period marked by heightened sensitivity to immediate rewards at the expense of long-term prosocial gains.

Strengths:

(1) Rigid model comparison and parameter recovery procedure.

(2) Conceptually comprehensive model space.

(3) Well-powered samples.

We thank the reviewer for highlighting the strengths of our work.

Weaknesses:

A key conceptual distinction between learning from non-human agents (e.g., bandit machines) and human partners is that the latter are typically assumed to possess stable behavioral dispositions or moral traits. When a non-human source abruptly shifts behavior (e.g., from 80% to 20% reward), learners may simply update their expectations. In contrast, a sudden behavioral shift by a previously cooperative human partner can prompt higher-order inferences about the partner's trustworthiness or the integrity of the experimental setup (e.g., whether the partner is truly interactive or human). The authors may consider whether their modeling framework captures such higher-order social inferences. Specifically, trait-based models-such as those explored in Hackel et al. (2015, Nature Neuroscience)-suggest that learners form enduring beliefs about others' moral dispositions, which then modulate trial-bytrial learning. A learner who believes their partner is inherently cooperative may update less in response to a surprising defection, effectively showing a trait-based dampening of learning rate.

We thank the reviewer for this thoughtful comment. We agree that social learning from human partners may involve higher-order inferences beyond simple reinforcement learning from non-human sources. To address this, we had previously included such mechanisms in our behavioral modeling. In Model 7 (Social Reward Model with Influence), we tested a higher-order belief-updating process in which participants’ expectations about their partner’s cooperation were shaped not only by the partner’s previous choices but also by the inferred influence of their own past actions on the partner’s subsequent behavior. In other words, participants could adjust their belief about the partner’s cooperation by considering how their partner’s belief about them might change. Model comparison showed that Model 7 did not outperform the best-fitting model, suggesting that incorporating higher-order influence updates added limited explanatory value in this context. As suggested by the reviewer, we have further clarified this point in the revised manuscript.

Regarding trait-based frameworks, we appreciate the reviewer’s reference to Hackel et al. (2015). That study elegantly demonstrated that learners form relatively stable beliefs about others’ social dispositions, such as generosity, especially when the task structure provides explicit cues for trait inference (e.g., resource allocations and giving proportions). By contrast, our study was not designed to isolate trait learning, but rather to capture how participants update their expectations about a partner’s cooperation over repeated interactions. In this sense, cooperativeness in our framework can be viewed as a trait-like latent belief that evolves as evidence accumulates. Thus, while our model does not include a dedicated trait module that directly modulates learning rates, the belief-updating component of our best-fitting model effectively tracks a dynamic, partner-specific cooperativeness, potentially reflecting a prosocial tendency.

This asymmetry in belief updating has been observed in prior work (e.g., Siegel et al., 2018, Nature Human Behaviour) and could be captured using a dynamic or belief-weighted learning rate. Models incorporating such mechanisms (e.g., dynamic learning rate models as in Jian Li et al., 2011, Nature Neuroscience) could better account for flexible adjustments in response to surprising behavior, particularly in the social domain.

We thank the reviewer for the suggestion. Following the comment, we implemented an additional model incorporating a dynamic learning rate based on the magnitude of prediction errors. Specifically, we developed Model 9:  Social reward model with Pearce–Hall learning algorithm (dynamic learning rate), in which participants’ beliefs about their partner’s cooperation probability are updated using a Rescorla–Wagner rule with a learning rate dynamically modulated by the Pearce–Hall (PH) Error Learning mechanism. In this framework, the learning rate increases following surprising outcomes (larger prediction errors) and decreases as expectations become more stable (see Appendix Analysis section for details).

The results showed that this dynamic learning rate model did not outperform our bestfitting model in either adolescents or adults (see Figure supplement 6). We greatly appreciate the reviewer’s suggestion, which has strengthened the scope of our analysis. We now have added these analyses to the Appendix Analysis section (see Figure Supplement 6) and expanded the Discussion to acknowledge this modeling extension and further discuss its implications.

Second, the developmental interpretation of the observed effects would be strengthened by considering possible non-linear relationships between age and model parameters. For instance, certain cognitive or affective traits relevant to social learning-such as sensitivity to reciprocity or reward updating-may follow non-monotonic trajectories, peaking in late adolescence or early adulthood. Fitting age as a continuous variable, possibly with quadratic or spline terms, may yield more nuanced developmental insights.

We thank the reviewer for this professional comment. In addition to the linear analyses, we further conducted exploratory analyses to examine potential non-linear relationships between age and the model parameters. Specifically, we fit LMMs for each of the four parameters as outcomes (α+, α-, β, and ω). The fixed effects included age, a quadratic age term, and gender, and the random effects included subject-specific random intercepts and random slopes for age and gender. Model comparison using BIC did not indicate improvement for the quadratic models over the linear models for α⁺ (ΔBIC_{quadratic-linear} = 5.09), α− (ΔBICquadratic-linear = 3.04), β (ΔBICquadratic-linear = 3.9), or ω (ΔBICquadratic-linear = 0). Moreover, the quadratic age term was not significant for α⁺, α⁻, or β (all ps > 0.10). For ω, we observed a significant linear age effect (b = 1.41, t = 2.65, p = 0.009) and a significant quadratic age effect (b = −0.03, t = −2.39, p = 0.018; see Author response image 1). This pattern is broadly consistent with the group effect reported in the main text. The shaded area in the figure represents the 95% confidence interval. As shown, the interval widens at older ages (≥ 26 years) due to fewer participants in that range, which limits the robustness of the inferred quadratic effect. In consideration of the limited precision at older ages and the lack of BIC improvement, we did not emphasize the quadratic effect in the revised manuscript and present these results here as exploratory.

Author response image 1.

Linear and quadratic model fits showing the relationship between age and the ω parameter, with 95% confidence intervals.<br />

Finally, the two age groups compared - adolescents (high school students) and adults (university students) - differ not only in age but also in sociocultural and economic backgrounds. High school students are likely more homogenous in regional background (e.g., Beijing locals), while university students may be drawn from a broader geographic and socioeconomic pool. Additionally, differences in financial independence, family structure (e.g., single-child status), and social network complexity may systematically affect cooperative behavior and valuation of rewards. Although these factors are difficult to control fully, the authors should more explicitly address the extent to which their findings reflect biological development versus social and contextual influences.

We appreciate this comment. Indeed, adolescents (high school students) and adults (university students) differ not only in age but also in sociocultural and socioeconomic backgrounds. In our study, all participants were recruited from Beijing and surrounding regions, which helps minimize large regional and cultural variability. Moreover, we accounted for individual-level random effects and included participants’ social value orientation (SVO) as an individual difference measure. 

Nonetheless, we acknowledge that other contextual factors, such as differences in financial independence, socioeconomic status, and social experience—may also contribute to group differences in cooperative behavior and reward valuation. Although our results are broadly consistent with developmental theories of reward sensitivity and social decisionmaking, sociocultural influences cannot be entirely ruled out. Future work with more demographically matched samples or with socioeconomic and regional variables explicitly controlled will help clarify the relative contributions of biological and contextual factors. Accordingly, we have revised the Discussion to include the following statement:  “Third, although both age groups were recruited from Beijing and nearby regions, minimizing major regional and cultural variation, adolescents and adults may still differ in socioeconomic status, financial independence, and social experience. Such contextual differences could interact with developmental processes in shaping cooperative behavior and reward valuation. Future research with demographically matched samples or explicit measures of socioeconomic background will help disentangle biological from sociocultural influences.”

Reviewer #3 (Public review):

Summary:

Wu and colleagues find that in a repeated Prisoner's Dilemma, adolescents, compared to adults, are less likely to increase their cooperation behavior in response to repeated cooperation from a simulated partner. In contrast, after repeated defection by the partner, both age groups show comparable behavior.

To uncover the mechanisms underlying these patterns, the authors compare eight different models. They report that a social reward learning model, which includes separate learning rates for positive and negative prediction errors, best fits the behavior of both groups. Key parameters in this winning model vary with age: notably, the intrinsic value of cooperating is lower in adolescents. Adults and adolescents also differ in learning rates for positive and negative prediction errors, as well as in the inverse temperature parameter.

Strengths: 

The modeling results are compelling in their ability to distinguish between learned expectations and the intrinsic value of cooperation. The authors skillfully compare relevant models to demonstrate which mechanisms drive cooperation behavior in the two age groups.

We thank the reviewer’s recognition of our work’s strengths.

Weaknesses:

Some of the claims made are not fully supported by the data:

The central parameter reflecting preference for cooperation is positive in both groups. Thus, framing the results as self-interest versus other-interest may be misleading.

We thank the reviewer for this insightful comment. In the social reward model, the cooperation preference parameter is positive by definition, as defection in the repeated rPDG always yields a +2 monetary advantage regardless of the partner’s action. This positive value represents the additional subjective reward assigned to mutual cooperation (e.g., reciprocity value) that counterbalances the monetary gain from defection. Although the estimated social reward parameter ω was positive, the effective advantage of cooperation is Δ=p×ω−2. Given participants’ inferred beliefs p, Δ was negative for most trials (p×ω<2), indicating that the social reward was insufficient to offset the +2 advantage of defection. Thus, both adolescents and adults valued cooperation positively, but adolescents’ smaller ω and weaker responsiveness to sustained partner cooperation suggest a stronger weighting on immediate monetary payoffs. 

In this light, our framing of adolescents as more self-interested derives from their behavioral pattern: even when they recognized sustained partner cooperation and held high expectations of partner cooperation, adolescents showed lower cooperative behavior and reciprocity rewards compared with adults. Whereas adults increased cooperation after two or three consecutive partner cooperations, this pattern was absent among adolescents. We therefore interpret their behavior as relatively more self-interested, reflecting reduced sensitivity to the social reward from mutual cooperation rather than a categorical shift from self-interest to other-interest, as elaborated in the Discussion.

It is unclear why the authors assume adolescents and adults have the same expectations about the partner's cooperation, yet simultaneously demonstrate age-related differences in learning about the partner. To support their claim mechanistically, simulations showing that differences in cooperation preference (i.e., the w parameter), rather than differences in learning, drive behavioral differences would be helpful.

We thank the reviewer for raising this important point. In our model, both adolescents and adults updated their beliefs about partner cooperation using an asymmetric reinforcement learning (RL) rule. Although adolescents exhibited a higher positive and a lower negative learning rate than adults, the two groups did not differ significantly in their overall updating of partner cooperation probability (Fig. 4a-b). We then examined the social reward parameter ω, which was significantly smaller in adolescents and determined the intrinsic value of mutual cooperation (i.e., p×ω). This variable differed significantly between groups and closely matched the behavioral pattern.

Following the reviewer’s suggestion, we conducted additional simulations varying one model parameter at a time while holding the others constant. The difference in mean cooperation probability between adults and adolescents served as the index (positive = higher cooperation in adults). As shown in the Author response image 2, decreases in ω most effectively reproduced the observed group difference (shaded area), indicating that age-related differences in cooperation are primarily driven by variation in the social reward parameter ω rather than by others.

Author response image 2.

Simulation results showing how variations in each model parameter affect the group difference in mean cooperation probability (Adults – Adolescents). Based on the best-fitting Model 8 and parameters estimated from all participants, each line represents one parameter (i.e., α+, α-, ω, β) systematically varied within the tested range (α±:0.1–0.9; ω, β:1–9) while other parameters were held constant. Positive values indicate higher cooperation in adults. Smaller ω values most strongly reproduced the observed group difference, suggesting that reduced social reward weighting primarily drives adolescents’ lower cooperation.

Two different schedules of 120 trials were used: one with stable partner behavior and one with behavior changing after 20 trials. While results for order effects are reported, the results for the stable vs. changing phases within each schedule are not. Since learning is influenced by reward structure, it is important to test whether key findings hold across both phases.

We thank the reviewer for this thoughtful and professional comment. In our GLMM and LMM analyses, we focused on trial order rather than explicitly including the stable vs. changing phase factor, due to concerns about multicollinearity. In our design, phases occur in specific temporal segments, which introduces strong collinearity with trial order. In multi-round interactions, order effects also capture variance related to phase transitions. 

Nonetheless, to directly address this concern, we conducted additional robustness analyses by adding a phase variable (stable vs. changing) to GLMM1, LMM1, and LMM3 alongside the original covariates. Across these specifications, the key findings were replicated (see GLMM_sup2 and LMM_sup4–5; Tables 9-11), and the direction and significance of main effects remained unchanged, indicating that our conclusions are robust to phase differences.

The division of participants at the legal threshold of 18 years should be more explicitly justified. The age distribution appears continuous rather than clearly split. Providing rationale and including continuous analyses would clarify how groupings were determined.

We thank the reviewer for this thoughtful comment. We divided participants at the legal threshold of 18 years for both conceptual and practical reasons grounded in prior literature and policy. In many countries and regions, 18 marks the age of legal majority and is widely used as the boundary between adolescence and adulthood in behavioral and clinical research. Empirically, prior studies indicate that psychosocial maturity and executive functions approach adult levels around this age, with key cognitive capacities stabilizing in late adolescence (Icenogle et al., 2019; Tervo-Clemmens et al., 2023). We have clarified this rationale in the Introduction section of the revised manuscript.

“Based on legal criteria for majority and prior empirical work, we adopt 18 years as the boundary between adolescence and adulthood (Icenogle et al., 2019; Tervo-Clemmens et al., 2023).”

We fully agree that the underlying age distribution is continuous rather than sharply divided. To address this, we conducted additional analyses treating age as a continuous predictor (see GLMM_sup1 and LMM_sup1–3; Tables S1-S4), which generally replicated the patterns observed with the categorical grouping. Nevertheless, given the limited age range of our sample, the generalizability of these findings to fine-grained developmental differences remains constrained. Therefore, our primary analyses continue to focus on the contrast between adolescents and adults, rather than attempting to model a full developmental trajectory.

Claims of null effects (e.g., in the abstract: "adults increased their intrinsic reward for reciprocating... a pattern absent in adolescents") should be supported with appropriate statistics, such as Bayesian regression.

We thank the reviewer for highlighting the importance of rigor when interpreting potential null effects. To address this concern, we conducted Bayes factor analyses of the intrinsic reward for reciprocity and reported the corresponding BF10 for all relevant post hoc comparisons. This approach quantifies the relative evidence for the alternative versus the null hypothesis, thereby providing a more direct assessment of null effects. The analysis procedure is now described in the Methods and Materials section: 

“Post hoc comparisons were conducted using Bayes factor analyses with MATLAB’s bayesFactor Toolbox (version v3.0, Krekelberg, 2024), with a Cauchy prior scale σ = 0.707.”

Once claims are more closely aligned with the data, the study will offer a valuable contribution to the field, given its use of relevant models and a well-established paradigm.

We are grateful for the reviewer’s generous appraisal and insightful comments.

Recommendations for the authors

Reviewer #1 (Recommendations for the authors):

I commend the authors on a well-structured, clear, and interesting piece of work. I have several questions and recommendations that, if addressed, I believe will strengthen the manuscript.

We thank the reviewer for commending the organization of our paper.

Introduction: - Why use a zero-sum (Prisoner's Dilemma; PD) versus a mixed-motive game (e.g. Trust Task) to study cooperation? In a finite set of rounds, the dominant strategy can be to defect in a PD.

We thank the reviewer for this helpful comment. We agree that both the rationale for using the repeated Prisoner’s Dilemma (rPDG) and the limitations of this framework should be clarified. We chose the rPDG to isolate the core motivational conflict between selfinterest and joint welfare, as its symmetric and simultaneous structure avoids the sequential trust and reputation dependencies/accumulation inherent to asymmetric tasks such as the Trust Game (King-Casas et al., 2005; Rilling et al., 2002).

Although a finitely repeated rPDG theoretically favors defection, extensive prior research shows that cooperation can still emerge in long repeated interactions when players rely on learning and reciprocity rather than backward induction (Rilling et al., 2002; Fareri et al., 2015). Our design employed 120 consecutive rounds, allowing participants to update expectations about partner behavior and to establish stable reciprocity patterns over time. We have added the following clarification to the Introduction:

“The rPDG provides a symmetric and simultaneous framework that isolates the motivational conflict between self-interest and joint welfare, avoiding the sequential trust and reputation dynamics characteristic of asymmetric tasks such as the Trust Game (Rilling et al., 2002; King-Casas et al., 2005)”

Methods:

Did the participants know how long the PD would go on for?

Were the participants informed that the partner was real/simulated?

Were the participants informed that the partner was going to be the same for all rounds?

We thank the reviewer for the meticulous review work, which helped us present the experimental design and reporting details more clearly. the following clarifications: I. Participants were not informed of the total number of rounds in the rPDG. This prevented endgame expectations and avoided distraction from counting rounds, which could introduce additional effects. II. Participants were told that their partner was another human participant in the laboratory. However, the partner’s behavior was predetermined by a computer program. This design enabled tighter experimental control and ensured consistent conditions across age groups, supporting valid comparisons. III. Participants were informed that they would interact with the same partner across all rounds, aligning with the essence of a multiround interaction paradigm and stabilizing partner-related expectations. For transparency, we have clarified these points in the Methods and Materials section:

“Participants were told that their partner was another human participant in the laboratory and that they would interact with the same partner across all rounds. However, in reality, the actions of the partner were predetermined by a computer program. This setup allowed for a clear comparison of the behavioral responses between adolescents and adults. Participants were not informed of the total number of rounds in the rPDG.”

The authors mention that an SVO was also recorded to indicate participant prosociality. Where are the results of this? Did this track game play at all? Could cooperativeness be explained broadly as an SVO preference that penetrated into game-play behaviour?

We thank the reviewer for pointing this out. We agree that individual differences in prosociality may shape cooperative behavior, so we conducted additional analyses incorporating SVO. Specifically, we extended GLMM1 and LMM3 by adding the measured SVO as a fixed effect with random slopes, yielding GLMM_sup3 and LMM_sup6 (Tables 12–13). The results showed that higher SVO was associated with greater cooperation, whereas its effect on the reward for reciprocity was not significant. Importantly, the primary findings remained unchanged after controlling for SVO. These results indicate that cooperativeness in our task cannot be explained solely by a broad SVO preference, although a more prosocial orientation was associated with greater cooperation. We have reported these analyses and results in the Appendix Analysis section.

Why was AIC chosen rather an BIC to compare model dominance?

Sorry for the lack of clarification. Both the Akaike Information Criterion (AIC, Akaike, 1974) and Bayesian Information Criterion (BIC, Schwarz, 1978) are informationtheoretic criterions for model comparison, neither of which depends on whether the models to be compared are nested to each other or not (Burnham et al., 2002). We have added the following clarification into the Methods.

“We chose to use the AICc as the metric of goodness-of-fit for model comparison for the following statistical reasons. First, BIC is derived based on the assumption that the “true model” must be one of the models in the limited model set one compares (Burnham et al., 2002; Gelman & Shalizi, 2013), which is unrealistic in our case. In contrast, AIC does not rely on this unrealistic “true model” assumption and instead selects out the model that has the highest predictive power in the model set (Gelman et al., 2014). Second, AIC is also more robust than BIC for finite sample size (Vrieze, 2012).”

I believe the model fitting procedure might benefit from hierarchical estimation, rather than maximum likelihood methods. Adolescents in particular seem to show multiple outliers in a^+ and w^+ at the lower end of the distributions in Figure S2. There are several packages to allow hierarchical estimation and model comparison in MATLAB (which I believe is the language used for this analysis; see https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007043).

We thank the reviewer for this helpful comment and for referring us to relevant methodological work (Piray et al., 2019). We have addressed this point by incorporating hierarchical Bayesian estimation, which effectively mitigates outlier effects and improves model identifiability. The results replicated those obtained with MLE fitting and further revealed group-level differences in key parameters. Please see our detailed response to Reviewer#1 Q1 for the full description of this analysis and results.

Results: Model confusion seems to show that the inequality aversion and social reward models were consistently confused with the baseline model. Is this explained or investigated? I could not find an explanation for this.

The apparent overlap between the inequality aversion (Model 4) and social reward (Model 5) models in the recovery analysis likely arises because neither model includes a learning mechanism, making them unable to capture trial-by-trial adjustments in this dynamic task. Consequently, both were best fit by the baseline model. Please see Response to Reviewer #1 Q3 for related discussion.

Figures 3e and 3f show the correlation between asymmetric learning rates and age. It seems that both a^+ and a^- are around 0.35-0.40 for young adolescents, and this becomes more polarised with age. Could it be that with age comes an increasing discernment of positive and negative outcomes on beliefs, and younger ages compress both positive and negative values together? Given the higher stochasticity in younger ages (\beta), it may also be that these values simply represent higher uncertainty over how to act in any given situation within a social context (assuming the differences in groups are true).

We appreciate this insightful interpretation. Indeed, both α+ and α- cluster around 0.35–0.40 in younger adolescents and become increasingly polarized with age, suggesting that sensitivity to positive versus negative feedback is less differentiated early in development and becomes more distinct over time. This interpretation remains tentative and warrants further validation. Based on this comment, we have revised the Discussion to include this developmental interpretation.

We also clarify that in our model β denotes the inverse temperature parameter; higher β reflects greater choice precision and value sensitivity, not higher stochasticity. Accordingly, adolescents showed higher β values, indicating more value-based and less exploratory choices, whereas adults displayed relatively greater exploratory cooperation. These group differences were also replicated using hierarchical Bayesian estimation (see Response to Reviewer #1 Q1). In response to this comment, we have added a statement in the Discussion highlighting this developmental interpretation.

“Together, these findings suggest that the differentiation between positive and negative learning rates changes with age, reflecting more selective feedback sensitivity in development, while higher β values in adolescents indicate greater value sensitivity. This interpretation remains tentative and requires further validation in future research.”

A parameter partial correlation matrix (off-diagonal) would be helpful to understand the relationship between parameters in both adolescents and adults separately. This may provide a good overview of how the model properties may change with age (e.g. a^+'s relation to \beta).

We thank the reviewer for this helpful comment. We fully agree that a parameter partial correlation matrix can further elucidate the relationships among parameters. Accordingly, we conducted a partial correlation analysis and added the visually presented results to the revised manuscript as Figure 2-figure supplement 4.

It would be helpful to have Bayes Factors reported with each statistical tests given that several p-values fall within the 0.01 and 0.10.

We thank the reviewer for this important recommendation. We have conducted Bayes factor analyses and reported BF10 for all relevant post hoc comparisons. We also clarified our analysis in the Methods and Materials section: 

“Post hoc comparisons were conducted using Bayes factor analyses with MATLAB’s bayesFactor Toolbox (version v3.0, Krekelberg, 2024), with a Cauchy prior scale σ = 0.707.”

Discussion: I believe the language around ruling out failures in mentalising needs to be toned down. RL models do not enable formal representational differences required to assess mentalising, but they can distinguish biases in value learning, which in itself is interesting. If the authors were to show that more complex 'ToM-like' Bayesian models were beaten by RL models across the board, and this did not differ across adults and adolescents, there would be a stronger case to make this claim. I think the authors either need to include Bayesian models in their comparison, or tone down their language on this point, and/or suggest ways in which this point might be more thoroughly investigated (e.g., using structured models on the same task and running comparisons: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0087619).

We thank the reviewer for the comments. Please see our response to Reviewer 1 (Appraisal & Discussion section) for details.

Reviewer #2 (Recommendations for the authors):

The authors may want to show the winning model earlier (perhaps near the beginning of the Results section, when model parameters are first mentioned).

We thank the reviewer for this suggestion. We agree that highlighting the winning model early improves clarity. Currently, we have mentioned the winning model before the beginning of the Results section. Specifically, in the penultimate paragraph of the Introduction we state:

“We identified the asymmetric RL learning model as the winning model that best explained the cooperative decisions of both adolescents and adults.”

Reviewer #3 (Recommendations for the authors):

In addition to the points mentioned above, I suggest the following:

(1) Clarify plots by clearly explaining each variable. In particular, the indices 1 vs. 1,2 vs. 1,2,3 were not immediately understandable.

We thank the reviewer for this suggestion. We agree that the indices were not immediately clear. We have revised the figure captions (Figure 1 and 4) to explicitly define these terms more clearly: 

“The x-axis represents the consistency of the partner’s actions in previous trials (t₋₁: last trial; t_−1,2: last two trials; t_−1,2,3: last three trials).”

It's unclear why the index stops at 3. If this isn't the maximum possible number of consecutive cooperation trials, please consider including all relevant data, as adolescents might show a trend similar to adults over more trials.

We thank the reviewer for raising this point. In our exploratory analyses, we also examined longer streaks of consecutive partner cooperation or defection (up to four or five trials). Two empirical considerations led us to set the cutoff at three in the final analyses. First, the influence of partner behavior diminished sharply with temporal distance. In both GLMMs and LMMs, coefficients for earlier partner choices were small and unstable, and their inclusion substantially increased model complexity and multicollinearity. This recency pattern is consistent with learning and decision models emphasizing stronger weighting of recent evidence (Fudenberg & Levine, 2014; Fudenberg & Peysakhovich, 2016). Second, streaks longer than three were rare, especially among some participants, leading to data sparsity and inflated uncertainty. Including these sparse conditions risked biasing group estimates rather than clarifying them. Balancing informativeness and stability, we therefore restricted the index to three consecutive partner choices in the main analyses, which we believe sufficiently capture individuals’ general tendencies in reciprocal cooperation.

The term "reciprocity" may not be necessary. Since it appears to reflect a general preference for cooperation, it may be clearer to refer to the specific behavior or parameter being measured. This would also avoid confusion, especially since adolescents do show negative reciprocity in response to repeated defection.

We thank you for this comment. In our work, we compute the intrinsic reward for reciprocity as p × ω, where p is the partner cooperation expectation and ω is the cooperation preference. In the rPDG, this value framework manifests as a reciprocity-derived reward: sustained mutual cooperation maximizes joint benefits, and the resulting choice pattern reflects a value for reciprocity, contingent on the expected cooperation of the partner. This quantity enters the trade-off between U_cooperation and U_defectionand captures the participant’s intrinsic reward for reciprocity versus the additional monetary reward payoff of defection. Therefore, we consider the term “reciprocity” an acceptable statement for this construct.

Interpretation of parameters should closely reflect what they specifically measure.

We thank the reviewer for pointing this out. We have refined the relevant interpretations of parameters in the current Results and Discussion sections.

Prior research has shown links between Theory of Mind (ToM) and cooperation (e.g., Martínez-Velázquez et al., 2024). It would be valuable to test whether this also holds in your dataset.

We thank the reviewer for this thoughtful comment. Although we did not directly measure participants’ ToM, our design allowed us to estimate participants’ trial-by-trial inferences (i.e., expectations) about their partner’s cooperation probability. We therefore treat these cooperation expectations as an indirect representation for belief inference, which is related to ToM processes. To test whether this belief-inference component relates to cooperation in our dataset, we further conducted an exploratory analysis (GLMM_sup4) in which participants’ choices were regressed on their cooperation expectations, group, and the group × cooperation-expectation interaction, controlling for trial number and gender, with random effects. Consistent with the ToM–cooperation link in prior research (MartínezVelázquez et al., 2024), participants’ expectations about their partner’s cooperation significantly predicted their cooperative behavior (Table 14), suggesting that decisions were shaped by social learning about others’ inferred actions. Moreover, the interaction between group and cooperation expectation was not significant, indicating that this inference-driven social learning process likely operates similarly in adolescents and adults. This aligns with our primary modeling results showing that both age groups update beliefs via an asymmetric learning process. We have reported these analyses in the Appendix Analysis section.

More informative table captions would help the reader. Please clarify how variables are coded (e.g., is female = 0 or 1? Is adolescent = 0 or 1?), to avoid the need to search across the manuscript for this information.

We thank the reviewer for raising this point. We have added clear and standardized variable coding in the table notes of all tables to make them more informative and avoid the need to search the paper. We have ensured consistent wording and formatting across all tables.

I hope these comments are helpful and support the authors in further strengthening their manuscript.

We thank the three reviewers for their comments, which have been helpful in strengthening this work.

Reference

(1) Fudenberg, D., & Levine, D. K. (2014). Recency, consistent learning, and Nash equilibrium. Proceedings of the National Academy of Sciences of the United States of America, 111(Suppl. 3), 10826–10829. https://doi.org/10.1073/pnas.1400987111

(2) Fudenberg, D., & Peysakhovich, A. (2016). Recency, records, and recaps: Learning and nonequilibrium behavior in a simple decision problem. ACM Transactions on Economics and Computation, 4(4), Article 23, 1–18. https://doi.org/10.1145/2956581

(3) Hackel, L., Doll, B., & Amodio, D. (2015). Instrumental learning of traits versus rewards: Dissociable neural correlates and effects on choice. Nature Neuroscience, 18, 1233– 1235. https://doi.org/10.1038/nn.4080

(4) Icenogle, G., Steinberg, L., Duell, N., Chein, J., Chang, L., Chaudhary, N., Di Giunta, L.,Dodge, K. A., Fanti, K. A., Lansford, J. E., Oburu, P., Pastorelli, C., Skinner, A. T.,Sorbring, E., Tapanya, S., Uribe Tirado, L. M., Alampay, L. P., Al-Hassan, S. M.,Takash, H. M. S., & Bacchini, D. (2019). Adolescents’ cognitive capacity reaches adult levels prior to their psychosocial maturity: Evidence for a “maturity gap” in a multinational, cross-sectional sample. Law and Human Behavior, 43(1), 69–85. https://doi.org/10.1037/lhb0000315

(5) Krekelberg, B. (2024). Matlab Toolbox for Bayes Factor Analysis (v3.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.13744717

(6) Martínez-Velázquez, E. S., Ponce-Juárez, S. P., Díaz Furlong, A., & Sequeira, H. (2024). Cooperative behavior in adolescents: A contribution of empathy and emotional regulation? Frontiers in Psychology, 15, 1342458. https://doi.org/10.3389/fpsyg.2024.1342458

(7) Tervo-Clemmens, B., Calabro, F. J., Parr, A. C., et al. (2023). A canonical trajectory of executive function maturation from adolescence to adulthood. NatureCommunications, 14, 6922. https://doi.org/10.1038/s41467-023-42540-8

(8) King-Casas, B., Tomlin, D., Anen, C., Camerer, C. F., Quartz, S. R., & Montague, P. R. (2005). Getting to know you: reputation and trust in a two-person economic exchange. Science, 308(5718), 78-83. https://doi.org/10.1126/science.1108062

(9) Rilling, J. K., Gutman, D. A., Zeh, T. R., Pagnoni, G., Berns, G. S., & Kilts, C. D. (2002). A neural basis for social cooperation. Neuron, 35(2), 395-405. https://doi.org/10.1016/s0896-6273(02)00755-9

(10) Fareri, D. S., Chang, L. J., & Delgado, M. R. (2015). Computational substrates of social value in interpersonal collaboration. Journal of Neuroscience, 35(21), 8170-8180. https://doi.org/10.1523/JNEUROSCI.4775-14.2015

(11) Akaike, H. (2003). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705

(12) Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 461464. https://doi.org/10.1214/aos/1176344136

(13) Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). Springer.https://doi.org/10.1007/b97636

(14) Gelman, A., & Shalizi, C. R. (2013). Philosophy and the practice of Bayesian statistics. British Journal of Mathematical and Statistical Psychology, 66(1), 8–38. https://doi.org/10.1111/j.2044-8317.2011.02037.x

(15) Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2014). Bayesian data analysis (3rd ed.). Chapman and Hall/CRC. https://doi.org/10.1201/b16018

(16) Vrieze, S. I. (2012). Model selection and psychological theory: A discussion of the differences between the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Psychological Methods, 17(2), 228–243. https://doi.org/10.1037/a0027127.

Chat with Gemini:<br /> Lee:

Here's another block of text in that same lesson. Can you help me visualize what it means?

When both vectors are normalized, the arc cosine (cos⁻¹) of their dot product reveals the angle θ between them: θ=cos⁻¹((A⋅B)/(∥A∥ ∥B∥)) or cos⁻¹(Â⋅B̂), where cos⁻¹ denotes the inverse cosine function, commonly represented as acos() in programming languages.

Gemini:<br /> This block of text is explaining how to "reverse-engineer" an angle using the dot product. In 3D graphics, you often know the coordinates of two objects (like a player and an enemy), but you don't know the angle between them. This formula is how you find it.

The Logic: Working Backwards

Think of the dot product formula we just discussed:

A⋅B = ∥A∥∥B∥cos(θ)

The text is simply rearranging that equation to solve for .

1. Isolate the Cosine: Divide both sides by the magnitudes (). A⋅B cos(θ) = ------ ∥A∥∥B∥
2. Find the Angle: To get θ by itself, you use the inverse cosine (acos). / A⋅B \ θ = arccos( ------ ) \ ∥A∥∥B∥ /

Visualizing the "Normalized" Shortcut

The text mentions: θ=cos(Â⋅B̂). (The "hat" symbol  means it is a normalized unit vector).

This is the "pro-gamer move" in graphics programming. If you normalize both vectors first (meaning you force their lengths to be 1), then ∥A∥ and ∥B∥ both become 1. The denominator in our formula disappears!

• If lengths are 1: The dot product is the cosine of the angle.
• To get the angle: You just plug that dot product into acos().

A Practical Example: "Field of View"

Imagine you are coding a stealth game. You have a Guard and a Player.

• Vector A: The direction the Guard is looking.
• Vector B: The direction from the Guard to the Player.

If you normalize both vectors and calculate (A⋅B), you get the exact angle (e.g., 30°). If the Guard's peripheral vision is only 45°, you now know the Guard can see the player!

Important "Gotcha" for Programmers

When you use acos() in languages like C#, C++, or Python, the result is usually in radians, not degrees.

• Dot Product = 1: Angle is 0 (Vectors point same way).
• Dot Product = 0: Angle is 1.57 radians (90°).
• Dot Product = -1: Angle is 3.14 radians (180°).

Lee:

You said If you normalize both vectors and calculate $acos(A⋅B), you get the exact angle (e.g., 30°). If the Guard's peripheral vision is only 45°, you now know the Guard can see the player! By "only 45°", is that from left to right of the guard's visibility? So, from straight ahead to the far right of his vision would only be 22.5°, thus the player is not visible at a 30° again from the guard? Or is that 45° from straight ahead to the far right of the guard's vision (for a total viewable angle of 90°)?

Gemini:<br /> That is a fantastic catch. In game development and 3D graphics, this distinction is a common source of bugs!

When you use the dot product to check visibility, the angle θ you calculate is the offset from the center.

1. The "Half-Angle" Concept

The dot product measures the angle between two specific lines: where the guard is looking (the "forward vector") and where the player is. It doesn't know about "left" or "right"; it only knows "how far away from my nose is this object?"

• If your calculation acos(A⋅B) returns 30°, it means the player is 30° away from the center line.
• If the guard has a 45° Field of View (FOV), usually that refers to the total horizontal arc (22.5° to the left and 22.5° to the right).

In your specific scenario:

• Total FOV: 45°
• Allowed "Half-Angle": 22.5°
• Player's Angle: 30°
• Result: Not Visible. The player is "outside the cone."

2. How it's usually coded

To avoid doing the math every single frame, programmers usually define the guard's "View Cone" as a threshold value.

If the guard can see 45° to the left and 45° to the right (a 90° total FOV), the threshold is:<br /> cos(45°) ≈ 0.707 The code then looks like this:

```csharp // If dot product is greater than 0.707, the player is within the 90-degree cone if (DotProduct(GuardForward, DirectionToPlayer) > 0.707) { // I see you! }

```

3. Why the Dot Product is "Circular"

One interesting thing to visualize: because the dot product only cares about the angle between two vectors, the "visibility" it creates is actually a cone, not a flat triangle.

If you say "anything within 30° of my forward vector is visible," that includes 30° up, 30° down, 30° left, and 30° right. It creates a circular spotlight of vision.

Summary Table: FOV vs. Dot Product

| Total FOV | Angle from Center | Dot Product Threshold (Approx) | | ------------------ | ----------------- | ------------------------------ | | 180° (Flat wall) | 90° | 0.0 | | 90° (Standard) | 45° | 0.707 | | 60° (Narrow) | 30° | 0.866 | | 2° (Sniper Scope)| 1° | 0.999 |

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Draws parallel between federation / small multiples, and tech sovereignty from silo'd hyperscalers. Vgl my notes on alternatives to Silicon Valley type scaling as only game in town.

The expression Black Ops probably refers to the homonymous Call of Duty shooter game. In this context, the game may function as a metaphor to highlight widespread violence.

The expression Black Ops probably refers to the homonymous Call of Duty shooter game. In this context, the game may function as a metaphor to highlight widespread violence.

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Okay I don't understand this level. Is this some port modern explanation, there is no objective truth or morals idea? Can you go another level deeper, symbols are more real than the world we live in.

Here's an example, Aristocrats deal with a Simulacrum of reality. Their "Reality" is the social pressures via the competing lifestyles of status created by other Elites. The Symbols of status are what is real and valuable to the Aristocrat.

A Coal Miner deal with the very "real" reality of mining actual energy out of the ground. The Physical Mine is what is real to the coal miner.

Both the Coal Miner and Aristocrat deal with Money which is some sort of Simulacrum game theory trust relationship contract thing.

Meaning fanbases. Get together and talk about this gay character, to feel like you belong somewhere, to be socially validated.

Yet, the character is the excuse to meet for the first time. Even if as time goes on this can change, the initial pulse comes from some media exposure, or someone sharing theirs.

I remember when my Disgaea save file got deleted... I cried!

Minecraft Hypixel's Technoblade memorial

Ooooh, but the game was not for them. I wonder how Nintendo does its testing...

Note I skipped a large chunck of fairly unrelated history about sports, war, and measurement that predated videogames. Recently, there has been a large increase in data collection devices which is much more interesting.

The datafication from phones, car GPS, AI, and radar war vehicle detection is much more in-line with game interfaces and game positivist metrics, KPI, retention, etc. Some of these have been instrumentalised for gamification, or speedruns too, like deaths/coins per-level, or playtime/speed.

This is also inherited from inherited from Industrial school tests, IQ, Normality, Matrices, and the tabular accounting quantification of money, born in imperialist countries and urban gangs that tried to keep track of trading stocks.

Arguably, per Kropotkin, this could be a form of rationing and a way to promote worker-guild control, so as to avoid top-down impositions from cartel pricing... which goes to show how far we've branched from games, and how economics the Enlightment science, and quantification aren't the devil, rather they have been repurposed as such, mainly through the industrial education system pipeline, and illusion of explanatory depth specialisation (which is harder to disentangle with ellusive numeric objectivist abstractions).

There, you don't need to read those 15 pages.

As different from football or other popular sports this is meant as a "private" and "individual" passtime, initially only for the burgeoise (then gun culture spread to all America).

The point being, there is exogenous, or what is forced from outside, and endogenous, or what is forced from inside. UK, France, Spain, etc. were and are colonialists countries that forced upon others their empire, and they still do, but in a more palatable less visibly violent fashion.

They opress, homogenise, silence, displace, much like the US, Mainland China, India, or Mexico. Their internal dissidents, varied ideologies, immigrants, subcultures, languages, natives, are squished, minoritised, colonised.

Okay, I get it, snake oil vendors are the people who get popular and get government grants to do next to nothing, because the instruction is only a part of the process. Beyond unlearning and learning there must be a change of habits, and no single play session or workshop can achieve that.

Yes, they can nudge toward visibility, but do they re-distribute? They can pinpoint and landmarks to look, and provide ways to not missbehave, but if they then need to be applied, and moral courage is not at its peak, as violence looms on the other side of the spectrum, these products can be almost a self-cleansing sterilisation tool, to merely perform predisposition to change, and to alleviate the cognitive dissonance of not doing so.

The example is pretty shitty, but it's true that when you work in a commercial game company and you can flip assets and code, Unreal becomes very easy to use with first person shooting and enemies: It's purposelly built, like Fortnite UEFN.

No NFL Game Has Ever Ended in a Score of 36–23 - The Atlantic by [[Josh Levin]] accessed on 2025-12-17T23:05:52

Men are more dominant than women physically, since they have to hunt, defend, and fought. The women are strong too, but they have less physical things to do, like grow and gather stuff.

Wait, the first world war would not have been fundable without the Federal Reserve.

In fact since it's easy for a central bank to repay money people that loan money out are incentived to start wars for the government to spend money that has to be loaned to the government.

Ah that's the "Military Industrial Complex" Eisenhower was talking about in his speach

Do any "Game B" people have any solutions to this inventive problem that creates perpetual war?

Robert Kiyosaki knows this game quite well and has leveraged himself to 1.2 Billion Dollars in debt.

He likes to say, if you have a million dollar loan and can't pay it back, you have a problem, but if you have a billion dollar loan and can't pay it back, the bank has a problem.

What is a "coalitional game"?

Coalitional game theory deals with multi agent dynamics requiring cooperation rather than just raw self interested agents.

Source: Coalitional Game theory - GeeksforGeeks

What is a "coalitional game"?

Coalitional game theory deals with multi agent dynamics requiring cooperation rather than just raw self interested agents.

Source: Coalitional Game theory - GeeksforGeeks