{"id":"69cdbb7a-f98f-4e84-86e4-fa92e4a964cc","arxiv_id":"2509.09314","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"Team performance in a silent search-and-rescue task is best predicted by spatial movement specialization: different roles exploring distinct areas with similar thoroughness.","lead":"This paper introduces three new measures of how team members coordinate their movements in space, and tests them in an online search-and-rescue game where talking is not allowed. The best predictor of team success was whether players with different roles searched separate areas while still covering similar amounts of ground.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Team performance weights conflict between Sections 3.2.2 and 3.3.3; central SMS and mediation results may depend on an undefined scoring scheme.","rationale":"I reviewed the paper in good faith and identified the most concrete, load-bearing threat to the central claim. The reader's stated weakest assumption is construct validity of SMS as coordination rather than task partitioning. That is a legitimate theoretical concern, but it is harder to settle with a single check and is partially acknowledged by the authors as a limitation. The performance scoring inconsistency is a direct internal contradiction that can be checked computationally. If the weights differ, the dependent variable is ambiguous, and all performance regressions and the mediation analysis may be affected. The reader's rationale does mention this inconsistency, but did not select it as the weakest assumption. My verdict remains CONDITIONAL (UNCHANGED) because the issue may be resolvable by clarifying which weights were used and verifying that results survive under both definitions. The paper's small sample (N=34) and multiple comparisons further increase fragility, but the scoring ambiguity is the most pressing technical issue. I would not move to REJECT without first seeing the re-analysis, since the error may be typographical and the substantive conclusion may hold.","tokens_in":13864,"tokens_out":3133,"duration_ms":34409,"concrete_test":"Recompute all performance-based analyses (Tables 2-4, Section 4.1-4.3) using the Section 3.2.2 scoring (green=10, yellow=20, red=30) and separately using the Section 3.3.3 scoring (green=10, yellow=30, red=60). Compare the SMS beta coefficients, model F/p-values, and the mediation indirect effect and confidence interval. If SMS remains a significant predictor and the mediation CI excludes zero under both scoring schemes, the inconsistency is not load-bearing; if significance or effect direction changes, the central claim is not robust to the documented scoring ambiguity.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim that SMS predicts team performance and that CI mediates this relationship rests on a performance variable whose definition is inconsistent in the manuscript. Section 3.2.2 states that rescuing green, yellow, and red victims yields 10, 20, and 30 points, and that the total earned in the two rounds determines the team bonus. Section 3.3.3, however, states that team performance is computed with red=60, yellow=30, green=10. These two schemes disagree for both yellow and red victims. All regression and mediation analyses (Tables 2 and 3, Section 4.1-4.3) use a single performance score, but it is not specified which weighting was entered. If the Section 3.3.3 weights were used, team scores and rankings may differ substantially from the task-incentive weights participants actually experienced. This could change the SMS coefficient (β=108.84), the model F (2.45, p=.08), and the mediation indirect effect (540.64, 95% CI [55.19, 1268.06]). Even if one section is a typo, the manuscript as written does not allow an independent researcher to reproduce the performance variable, undermining the empirical basis of every performance-related result. The reader's construct-validity concern about SMS is important, but the scoring inconsistency is more immediately load-bearing because it threatens the dependent variable itself.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"Using data from 34 four-person teams in a restricted-communication search-and-rescue task, the paper proposes three spatial coordination metrics (SED, SMS, SPA) and tests their associations with collective intelligence and team performance. The authors report that SMS significantly predicts both CI and performance, that CI partially mediates the SMS-performance link, that SPA has a marginal inverted-U relation with performance, and that temporal dynamics differentiate high- from low-performing teams. The central contribution is a set of quantitative, outcome-independent process metrics for implicit spatial coordination.","tokens_in":1726,"tokens_out":2524,"duration_ms":69464,"significance":"The contribution is timely: if the measures hold, they provide objective, low-cost process indicators for distributed teams. The metric definitions are clear and computed independently of outcomes, and mediation uses bootstrap confidence intervals, which are strengths. However, the dependent-variable scoring is inconsistent across sections, the overall performance model is non-significant, and the inverted-U relies on marginal effects. These issues limit the strength of the conclusions and demand revision before the claims can be accepted.","major_comments":[{"comment":"Team performance is defined inconsistently. Section 3.2.2 states green/yellow/red rescue points are 10/20/30; Section 3.3.3 states red=60, yellow=30, green=10. All regression and mediation analyses (Tables 2–4) use a single score, but the manuscript does not say which weights were entered. Please state which weighting was used, justify it relative to the task instructions, and check whether the reported correlations, betas, F-statistics, and indirect effects are robust to the alternative weighting. Without this, the performance-related results are not reproducible.","section":"§3.2.2 vs §3.3.3"},{"comment":"The model predicting team performance is not significant as a whole (F=2.45, p=.08), yet the SMS coefficient is reported as p<.05. This tension deserves explicit discussion. Please report the SMS coefficient's confidence interval, test the regression with CI included (as implied by the mediation model), and conduct sensitivity analyses (e.g., bootstrap). The Discussion should not present SMS as a clear predictor of performance without acknowledging the marginal omnibus test.","section":"§4.1, Table 2"},{"comment":"The inverted-U claim for SPA is based on a quadratic model that is not significant (F(2,31)=2.31, p=.116), a quadratic term at p=.06, and a post-hoc ANOVA on the same data. Please frame this as exploratory, provide an explicit test of the quadratic effect (e.g., a planned contrast or a direct comparison of nested models), report the standard error of the estimated optimum, and soften causal language such as 'too much adaptation started to reduce effectiveness.'","section":"§4.3.1, Table 4"},{"comment":"The construct validity of SMS as measuring 'effective implicit coordination' is not established. SMS=Es*(1-O) can be high merely because roles are assigned to independent spatial zones, with little anticipation or dynamic adjustment. Section 3.3.1 itself defers analysis of component interactions. Please provide validation evidence, such as relation to timing of joint rescues, comparison with a random-movement baseline, or a manipulation that separates task-determined partitioning from adaptive coordination. Otherwise, interpret SMS as a measure of spatial role configuration rather than implicit coordination.","section":"§3.3.1, Eq. (2)"},{"comment":"The analysis involves many correlated tests—three metrics, two outcomes, quadratic models, group comparisons, and temporal plots—on a sample of 34 teams, with no correction for multiple comparisons. Several key results are near p=.05. Please report false-discovery-rate adjusted p-values or a pre-specified distinction between confirmatory and exploratory analyses. This would materially increase confidence in the robustness of the SMS and mediation results.","section":"§4 (overall)"}],"minor_comments":[{"comment":"The abstract states that temporal dynamics 'clearly differentiate' high- from low-performing teams, but Figure 7 is descriptive; no statistical test is reported for the temporal curves. Please add a suitable statistical comparison or soften the claim.","section":"§5.2 / Abstract"},{"comment":"SPA is defined as |D2-D1|/max(D1,D2), which discards the direction of change. The discussion in §4.3.2 refers to teams increasing or decreasing distance, but that direction is not recoverable from the metric as defined. Clarify whether a signed version was used for the temporal analysis or whether the text is an interpretation of separate phase-specific distances.","section":"§3.3.1, Eq. (5)"},{"comment":"The red-victim deadline is described as 'the first three minutes' of a 5-minute mission, and Figure 7 labels the threshold as '60% mission.' Please make the time convention consistent throughout (e.g., 3 minutes = 60%, not 50%).","section":"§3.1 / Figure 7"},{"comment":"The manuscript would benefit from a data and code availability statement, or a supplementary appendix reporting the exact grid size and time-bin choices, since these parameters directly affect the metric values.","section":"General"}],"recommendation":"major_revision","confidential_remarks":"The core idea is promising and the metrics are transparent, but the unresolved scoring inconsistency in the dependent variable is a serious reproducibility issue that affects every performance-related result. Given the small sample and the number of marginal p-values, I advise the editor to request the analysis data or detailed scoring scripts as part of the revision. If the performance definition is clarified and results are shown to be robust, I would reconsider; otherwise the performance and mediation claims need to be substantially weakened or re-analyzed."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nThe paper is worth reading for the metrics. SMS and its companions are a sensible way to quantify implicit spatial coordination from movement traces, and the authors ground them in the right literature. The main finding—SMS correlates with CI (r=.46) and performance (r=.38), and CI partially mediates the SMS-performance link—is plausible and mostly survives basic checks. I agree with the reader that this is a legitimate candidate for conditional acceptance, not a desk reject.\n\nThat said, the scoring inconsistency is real and load-bearing. Section 3.2.2 says green/yellow/red are worth 10/20/30 points; Section 3.3.3 says 10/30/60. The paper never says which weighting entered the regressions and mediation. Since performance is the outcome variable, an unspecified dependent variable means the regression coefficients, the mediation test, and the group splits are not reproducible. This needs to be resolved before the results can be taken at face value. The stress-test note is correct; this trumps the construct-validity worry.\n\nOther soft spots: the full regression model for performance is not significant (F=2.45, p=.08); the inverted-U for SPA rides on a quadratic term at p=.06 and a model at p=.116, then a post-hoc group ANOVA. That is thin, and the paper should label it as exploratory. The SMS construct itself—entropy similarity times one minus overlap—could be capturing task partitioning rather than active coordination, and the authors themselves defer interaction analysis to future work. That's a fair concern but not fatal to the paper; it's a limitation, not an error.\n\nThe temporal analysis is suggestive but qualitative; I'd want the authors to either provide quantitative tests or present it as illustration.\n\nBottom line: send to peer review. A careful referee can push for clarification of the scoring scheme, effect sizes, and data sharing. If the scoring discrepancy turns out to be a typo and the analyses use the task-incentive weights, the central claim likely holds. If not, the empirical core is compromised. Either way, the paper deserves referee time.","headline":"Worth reading for the new spatial coordination metrics, but the inconsistent performance scoring rule is load-bearing and needs to be resolved before the headline claims can be trusted.","tokens_in":14648,"tokens_out":1683,"would_cite":false,"duration_ms":18437,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper establishes that a single spatial metric—role-based movement specialization—predicts both collective intelligence and team performance in communication-restricted search-and-rescue teams, with collective intelligence carrying roug","keywords":["implicit coordination","collective intelligence","spatial coordination","movement specialization","search and rescue","team performance","mediation analysis","temporal dynamics"],"falsifier":"Re-run the same task with each role assigned a fixed, non-overlapping territory from the start; if those teams have high specialization scores but no performance gain over free-moving teams, the metric tracks task design, not coordination quality.","tokens_in":13736,"feed_emoji":"🗺️","tokens_out":5561,"duration_ms":65758,"temperature":0.7,"pith_summary":"The paper tries to show that teams coordinating without talking leave measurable traces in their movement, and that those traces predict how well the team does. It defines three movement-based metrics and tests them on 34 four-person teams playing a search-and-rescue game with no communication. The central finding is that spatial movement specialization—roles covering similarly thorough but minimally overlapping territory—predicts both collective intelligence and team performance. Collective intelligence mediates about 48% of the specialization-performance link. The other two metrics show weaker or curvilinear effects, but their time trajectories still separate high- from low-performing teams.","feed_headline":"Balanced space-splitting predicts team performance","feed_subtitle":"In a no-talk rescue game, teams that divide territory while matching effort outperform—an effect routed through collective intelligence.","key_machinery":"Spatial Movement Specialization (SMS) is defined as SMS = Es × (1 − O), where Es is the entropy similarity between the movement distributions of the two roles and O is the Jaccard overlap of the grid cells they visit. It is designed to reward a balanced divide-and-conquer strategy: roles should explore with similar thoroughness while minimizing redundant territory. Supporting metrics are Spatial Exploration Diversity (SED), the average Jensen-Shannon divergence between all pairs of players' movement distributions, and Spatial Proximity Adaptation (SPA), the normalized change in inter-role distance between the first and second halves of the mission. SMS is the load-bearing metric because it i","core_discovery":"The paper's central claim is that effective implicit spatial coordination can be captured by a single product: entropy similarity between the two roles' movement distributions multiplied by one minus their spatial overlap. Teams with high specialization scores—both roles exploring with comparable thoroughness while covering different ground—earn higher collective intelligence scores and higher mission scores. Bootstrapped mediation analysis shows that collective intelligence carries a significant indirect effect, accounting for 47.6% of the total effect of specialization on performance, while the direct effect becomes non-significant when the mediator is included. The paper also reports a ma","pith_inferences":["The authors defer separating the two components of SMS; a decomposition may show that one component does most of the predictive work, which would change the functional form of the metric.","A testable extension is to give an AI teammate the same SMS-derived spatial signal and see whether human teams improve; that would test whether the metric has actionable value beyond prediction.","With only 34 teams, the null results for exploration diversity and proximity adaptation are weak evidence; larger samples or finer time windows could reveal effects the current design misses."],"forward_implications":["Teams that establish high spatial specialization early in a mission tend to keep it and outperform; early spatial role differentiation is a plausible training target.","Because specialization predicts collective intelligence, movement traces can feed real-time diagnostics for coordination breakdowns in communication-limited teams.","The inverted-U pattern for proximity adaptation means both rigid spacing and excessive re-spacing hurt performance, so support tools should aim for moderate adaptation.","The mediation result implies spatial coordination builds a general team capability rather than only task-specific skill, so gains may carry over to other collaborative tasks.","The three metrics, computed at 3-second intervals, offer process-level measures usable for AI-assisted team monitoring in navigation-based work."],"fun_headline_variants":["Spatial specialization predicts team performance in silent missions","Balanced territory division boosts performance via collective intelligence","No-talk coordination: spatial specialization is key to team success","Implicit spatial coordination drives performance through shared intelligence","Team success in silent tasks depends on spatial specialization"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The load-bearing premise is that the specialization score measures real coordination—mutual anticipation and adjustment—rather than just the fact that the two roles were given different jobs in different areas.","fun_headline_variants_meta":{"raw":{"variants":["Spatial specialization predicts team performance in silent missions","Balanced territory division boosts performance via collective intelligence","No-talk coordination: spatial specialization is key to team success","Implicit spatial coordination drives performance through shared intelligence","Team success in silent tasks depends on spatial specialization"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000187,"raw_usage":{"total_tokens":1171,"prompt_tokens":756,"completion_tokens":415,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":500,"completion_tokens_details":{"reasoning_tokens":341}},"tokens_in":500,"tokens_out":415,"duration_ms":4838,"temperature":1.0,"reasoning_tokens":341,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-04T19:17:06.958193+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-run the same task with each role assigned a fixed, non-overlapping territory from the start; if those teams have high specialization scores but no performance gain over free-moving teams, the metric tracks task design, not coordination quality.","supporting_citations":[],"review_version":1}