{"id":"fd0b0d24-6029-450d-a714-47dac777dfea","arxiv_id":"2607.28601","paper_version":1,"verdict":"ACCEPT","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"low","formal_verification":"none","parameter_count":5,"one_line_summary":"Selective social learning is captured by a Rational Mentalizing model that uses Theory of Mind to estimate observation utility and arbitrates against non-social exploration cost.","lead":"Humans decide whether to watch others or explore alone by mentalizing about others’ goals and weighing expected information against cost. A Bayesian Theory-of-Mind plus utility model matches those choices across four multi-agent grid experiments better than ablations that drop either component.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.5","headline":"No verdict change: residual under-prediction reflects hard greedy C_self/argmin, but relative ToM+utility evidence still carries the claim.","rationale":"The reader correctly isolates the weakest link: greedy C_self plus hard threshold, flagged by the paper’s under-prediction of how much people observe/move. That does not overturn the strongest supported claim—relative observation counts and whom-to-watch across four escalating designs, with a priori parameters and ablations that fail in opposite directions when mentalizing or utility comparison is removed. Total cost is non-diagnostic (all models fit it); observation CCC near split-half reliability is the right primary DV. I find no stronger internal inconsistency (e.g., no circular fitting, no contradiction between Eq. 1–3 and the reported qualitative reversals). External generality to communication/pedagogy/repeated trust is untested but outside the paper’s stated scope. Stress-test therefore leaves ACCEPT in place: refine the process model later; do not downgrade the present behavioral-modeling result.","tokens_in":17140,"tokens_out":625,"duration_ms":45171,"concrete_test":"On the OSF model: (i) replace hard argmin with softmax P(observe j)∝exp(−Q^j_obs/τ) vs exp(−C_self/τ), and/or scale C_self by a global factor α; refit only τ or α to absolute observation totals; (ii) re-score Exp 3–4 whom-to-watch correlations and expert/novice reversals with those parameters frozen. If α or τ that fix absolute under-prediction collapse agent-choice r/CCC or reverse D.1–D.2 patterns, the utility-arbitration formalization is misspecified; if patterns hold, the residual is harmless calibration.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim needs the step-wise comparison C_self(b_t) vs Q^j_obs(t)=Σ_i p_i[c_obs T_i+C_plan(b'_t)] to be the right latent trade-off. Methods §6.4 fixes C_self as a greedy nearest-wizard tour and Algorithm 1 uses a hard one-way commit (once C_self≤ all Q_obs, stop observing and execute the planner). The paper’s own pooled results show systematic under-prediction of absolute observation and total steps, with residual watching of the non-chosen agent—exactly what a misspecified C_self magnitude, missing interleaving, or softer-than-argmin rule would produce. Ablations still show both mentalizing and cost comparison are needed for relative counts and whom-to-watch (CCC=0.89 near r_SB ceilings), so the concern limits process completeness more than the directional claim. It is load-bearing for “utility maximization” as implemented, not for “ToM informs selective observation” as a qualitative result.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.5","summary":"The paper proposes a Rational Mentalizing model of selective social learning: at each step the observer compares the non-social exploration cost C_self(b_t) to a posterior-weighted expected cost of watching agent j, Q^j_obs(t)=Σ_i p_i[c_obs T_i + C_plan(b'_t)], obtained by Bayesian Theory of Mind (inverse planning over goals and beliefs) plus forward simulation of how that agent’s trajectory would shrink the observer’s candidate set. Observation occurs only when watching is cheaper; with multiple agents the argmin selects whom to watch. A novel multi-agent grid game (observe-or-act turns; wizards/amulets; expert/novice NPCs) creates parametric trade-offs across four experiments of increasing complexity (goal uncertainty, whom-to-watch, expertise). With parameters fixed a priori and shared across experiments, the full model matches human observation counts (pooled CCC=0.89, near split-half reliability ceilings), while three ablations that remove mentalizing, utility comparison, or both fit substantially worse and fail in opposite directions (over- vs under-observe).","tokens_in":17445,"tokens_out":1508,"duration_ms":37835,"significance":"If the result holds, the paper supplies a concrete, testable account of how Theory of Mind is used not only to interpret others but to decide whether social observation is worth its cost relative to individual exploration—bridging Bayesian ToM, active information seeking, and social-learning-strategy literatures that have largely treated observed behavior as a reliability-weighted signal rather than as goal-directed evidence. Strengths that raise the contribution above a pure modeling exercise include: a novel paradigm with controllable goal/expertise structure; a priori shared parameters never fit to human observation; high split-half reliability of the behavioral target; ablations that dissociate components; and released code, stimuli, and anonymized data (OSF). The whom-to-watch and expert/novice reversals are especially diagnostic relative to heuristic or pure RL social-learning accounts.","major_comments":[{"comment":"Methods §6.4 and Algorithm 1 implement utility maximization as a hard myopic rule: observe j* iff min_j Q^j_obs(t) < C_self(b_t), else permanently commit to a greedy nearest-wizard self-exploration planner C_self(b)=Σ_k[d(x_{k-1},w^{(k)})+c_int]+d(x_K,x_g). Discussion correctly notes systematic under-prediction of absolute observation and total steps, plus residual watching of the non-chosen agent. That pattern is exactly what a misspecified C_self magnitude, missing interleaving of observe/act, or softer-than-argmin choice would produce. Because the central claim is that selective social learning is guided by ToM “in the service of utility maximization,” the paper should either (i) report a sensitivity analysis (soft-max over {act, observe-j}, alternative residual planners, or a small exploration bonus) showing that relative CCC/whom-to-watch results are robust, or (ii) explicitly downg","section":"Methods §6.4, Algorithm 1, Discussion"},{"comment":"The Rational Observer ablation (Methods §6.5) decides once at trial start using a fixed horizon to the other agent’s first wizard interaction, with no online re-evaluation and no goal inference. That is a weak foil for “cost-benefit without mentalizing”: a stronger non-mentalizing baseline would recompute expected information value online from observed trajectories treated as exogenous signals (e.g., reliability-weighted or volatility-based arbitration in the spirit of Behrens et al. / Diaconescu et al., which the Introduction cites). If such a baseline still fails on Experiments 2–4 (goal divergence, whom, novice), the ToM necessity claim is stronger; if it closes much of the gap on observation counts, the unique contribution of generative mentalizing needs tighter bounds. At minimum, justify why the once-and-done Rational Observer is the right contrast rather than an online non-ToM act","section":"§4, Methods §6.5, Introduction"}],"minor_comments":[{"comment":"Figure 3 reports pooled CCC and r, but per-experiment observation scatter with identity lines (partially in SI Figs. A12–A13) should be promoted or at least cross-referenced more clearly in the main Results so readers can see absolute calibration, not only concordance.","section":"§4.1, Figure 3"},{"comment":"Eq. (1) and Eq. (4) are identical; renumber or drop the duplicate and keep a single definition of Q^j_obs(t).","section":"§3, Methods §6.4"},{"comment":"The Mentalizing Observer’s divergence threshold ε=0.1 (Table A2) is listed among free parameters; briefly state that it was set a priori like the others and, if any informal tuning occurred, disclose it.","section":"Appendix A, Table A2"},{"comment":"Typographical inconsistencies: “Theoryof Mind”, “Muthukrishnaetal.”, “VélezandHyowonGweon”, missing spaces in keywords and some references. A pass for spacing and en-dashes would help.","section":"Throughout / References"},{"comment":"Figure 5’s “3×move + 5×interact” execution-cost definition is clear in the caption but should be stated once in Methods when point costs are introduced so the observation–execution coupling analysis is self-contained.","section":"Figure 5, §6.3"},{"comment":"SI notes total cost is not model-diagnostic (Fig. A14) while observation cost is—this is an important design lesson; one sentence in main §4 would help readers who only skim the SI.","section":"§4, Appendix A.3"}],"recommendation":"minor_revision","confidential_remarks":"I agree with the reader’s high-confidence accept lean on the directional ToM+utility claim; I am recommending minor_revision rather than accept only so the authors tighten the utility-maximization wording against their own absolute under-prediction and strengthen (or better justify) the non-mentalizing baseline. Neither issue requires new experiments if sensitivity analyses and clearer claim scoping are feasible from existing code. Fit for a computational cognitive science / multi-agent learning venue is strong; novelty of the paradigm and a priori parameters are real assets."},"author_rebuttal":null,"desk_editor":{"model":"grok-4.5","letter":"Punchline: this is a solid process model of selective social learning when goals diverge. They take inverse planning seriously not just to label another agent’s goal, but to forecast how many watch steps would shrink residual plan cost, then compare that to self-exploration. That arbitration, plus a purpose-built observe-or-act grid game, is the real addition over copy-successful heuristics and reliability-weighted social RL.\n\nWhat works: four experiments that ratchet complexity (goal uncertainty, two experts, expert vs novice), parameters fixed a priori and shared, ablations that fail in opposite directions (mentalizing-only over-observes; cost-only cannot choose whom). Observation-count fit is near the split-half ceiling (pooled CCC 0.89). The expert/novice reversal by map structure is the nicest qualitative result—trust falls out of expected informativeness, not a static expertise tag. OSF code and data are there. Citations are in the right neighborhood (Baker, SIPS/Zhi-Xuan, Gweon/Vélez, Kendal, active learning).\n\nSoft spots, in proportion: the utility comparison is implemented as greedy nearest-wizard C_self plus a hard stop-once-cheaper rule. Humans systematically observe and move more than the model, and keep a trickle of watch on the non-chosen agent. The discussion owns this (hedging, curiosity, softer choice). That means “utility maximization” as coded is incomplete as a full process account; it does not erase the evidence that both mentalizing and cost comparison are needed for relative counts and whom-to-watch. Total cost is rightly weak evidence—all models get it. Generality to communication, pedagogy, or multi-trial trust is untested, which is fine if they do not overclaim it.\n\nWho it is for: people who model ToM, social learning strategies, or multi-agent active sensing. Worth a serious referee. I would bring it to reading group and cite the paradigm and the arbitration idea. Send it out.","headline":"Clean integration of BToM with observe-vs-act utility that actually fits human whom-to-watch data; absolute under-prediction is real but does not sink the relative claim.","tokens_in":18112,"tokens_out":516,"would_cite":true,"duration_ms":11808,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.5","headline":"People decide whether to watch others or explore alone by mentalizing about goals and weighing expected costs.","keywords":["social learning","Theory of Mind","Bayesian inference","decision making","computational modeling","active learning","multi-agent"],"falsifier":"In the same game, if human observation counts reverse or decouple from the model's Q_obs versus C_self ranking—especially when the greedy residual planner and human exploration paths diverge, or when non-instrumental monitoring of the less-informative agent stays high—the utility-arbitration claim fails.","tokens_in":17931,"feed_emoji":"🧠","tokens_out":873,"duration_ms":25112,"temperature":0.7,"pith_summary":"Humans do not copy others indiscriminately, nor do they always explore alone. This paper argues that the choice is a step-by-step utility comparison: the observer uses Theory of Mind to infer another agent's goals and beliefs, simulates how that agent's future path would shrink its own uncertainty, and watches only when the expected cost of watching is lower than the cost of exploring now. A novel multi-agent grid game forces participants to choose, on every step, whether to observe a selected agent or move themselves, while maps vary goal uncertainty, number of agents, and expertise. Across four experiments the full model matches human observation counts closely (pooled concordance 0.89), whereas ablations that drop mentalizing or drop the cost comparison systematically over- or under-observe and fail to pick the right agent. The result matters because it unifies two literatures—Bayesian mentalizing and active information search—into a single account of when social information is worth the price.","feed_headline":"ToM plus cost comparison decides when to watch others","feed_subtitle":"A utility model that mentalizes about goals matches human observe-or-explore choices across four games","key_machinery":"The Rational Mentalizing model: Bayesian inverse planning over another agent's goal and belief, forward simulation of each hypothesis's trajectory to obtain a residual plan cost, and a hard comparison of that expected watching cost Q_obs against self-exploration cost C_self.","core_discovery":"Selective social learning is guided by Theory of Mind in the service of utility maximization. At each timestep the observer compares the non-social exploration cost under its current beliefs against the posterior-weighted expected cost of watching each available agent (observation steps until the candidate set narrows, plus residual planning cost afterward) and observes only the cheapest informative source when watching is cheaper than acting.","pith_inferences":["The same arbitration could be ported to advice-taking and pedagogical settings if the generative model is extended so the observed agent sometimes optimizes for the learner rather than its own goal.","The systematic under-prediction of absolute observation suggests a measurable curiosity or hedging term that could be added without discarding the instrumental core.","Cross-trial accumulation of agent-specific priors—histories of who tended to be informative—would turn the per-trial posterior into a longer-horizon trust model testable in repeated play."],"forward_implications":["When agents pursue different goals, the value of watching cannot be read off outcomes alone; it must be computed from inferred goals and beliefs.","Whom-to-watch choices fall out of the same cost comparison: the agent whose simulated trajectory is expected to shrink the observer's candidate set fastest per observation step.","Selective trust can emerge as a byproduct of mentalizing rather than a separate accuracy-tracking module: expertise matters only insofar as it changes expected informativeness in the current map.","Ablations that keep only mentalizing over-observe; ablations that keep only cost comparison cannot choose the right source once goals diverge."],"fun_headline_variants":["Theory of Mind weighs watch-vs-explore utilities","Mentalizing goals decides when social learning wins","ToM cost model matches human observe-or-act choices","Rational mentalizing arbitrates social vs solo learning","Observers pick cheapest informative source via ToM"],"cache_read_input_tokens":128,"weakest_assumption_plain":"The leftover cost after watching is well captured by a fixed greedy nearest-wizard planner, and the decision is a hard threshold comparison of that scalar to the expected watching cost.","fun_headline_variants_meta":{"raw":{"variants":["Theory of Mind weighs watch-vs-explore utilities","Mentalizing goals decides when social learning wins","ToM cost model matches human observe-or-act choices","Rational mentalizing arbitrates social vs solo learning","Observers pick cheapest informative source via ToM"]},"model":"grok-4.5","effort":"low","cost_usd":0.004534,"raw_usage":{"total_tokens":1246,"prompt_tokens":681,"num_sources_used":0,"completion_tokens":75,"cost_in_usd_ticks":45344000,"prompt_tokens_details":{"text_tokens":681,"audio_tokens":0,"image_tokens":0,"cached_tokens":128},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":490,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":681,"tokens_out":75,"duration_ms":7707,"temperature":1.0,"reasoning_tokens":490,"cache_read_input_tokens":128,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-31T02:33:32.213643+00:00","model_set":{"reader":"grok-4.5"},"falsifier":"In the same game, if human observation counts reverse or decouple from the model's Q_obs versus C_self ranking—especially when the greedy residual planner and human exploration paths diverge, or when non-instrumental monitoring of the less-informative agent stays high—the utility-arbitration claim fails.","supporting_citations":[],"review_version":1}