NoSeMaze setup, cohort structure, and accelerated longitudinal reshuffling design.

A, The NoSeMaze (see Supplementary Fig. S1) tracks social rank, chasing, and reinforcement learning in mouse societies (n = 9-10 per group) over multiple weeks without human intervention. RFID-tagged animals are automatically identified at the tube entrances and during olfactory stimulus-outcome learning tasks at the water lickport. B, Overview of the two adult cohorts studied in separate experimental series. Cohort 1 (“younger adults”) comprised 41 male C57BL/6J-background mice (16 OXTRΔAON, 25 controls), aged 16–30 weeks during NoSeMaze testing, and contributed 11 NoSeMaze group rounds in total. Cohort 2 (“older adults”) comprised 38 male mice (10 OXTRΔAON, 28 controls), aged 55–97 weeks during NoSeMaze testing, and contributed 10 NoSeMaze group rounds in total. C, Experimental timeline. Before entry into the NoSeMaze, home-cage compositions were repeatedly reshuffled every 3–4 days (3–5 mice per cage) to promote familiarity among cohort members. Mice were then housed in the NoSeMaze for approximately 3 weeks per round in groups of 9–10, with 2–4 groups run in parallel. Between consecutive NoSeMaze rounds, mice returned to reshuffled home cages, where they were again reshuffled in the last week before the next NoSeMaze round. Reshuffling occurred within cohorts only, not across cohorts. D, Example of the reshuffling design across two consecutive NoSeMaze rounds. Colored dots represent individual mice and gray ribbons indicate reassignment from pre-shuffling home cages to NoSeMaze round 1, then to reshuffled home cages, and finally to NoSeMaze round 2. This accelerated longitudinal design systematically altered social group composition across rounds while maintaining animals within their age-defined cohort. NoSeMaze, Non-invasive Sensor-rich Maze; RFID, radio frequency identification

Inter-individual differences in reinforcement learning in the NoSeMaze.

A, Mice performed stimulus-outcome learning trials for daily water intake, initiated by head insertion into the odor port. In CS+ trials, licking twice during odor presentation released water, while in CS− trials, it triggered a 6-second timeout. B, Reward contingencies switched every three days. C, Trial activity peaked during the dark phase. D, Number of trials required to reach 80% performance (hits or correct rejections, mean ± SEM) in each phase (cf. Fig. 2B). Performance decreased after the first reversal and improved in later phases, consistent with learning the task structure. Only data from the first round in the NoSeMaze was considered. E-G, Example lick patterns from three mice show variability in pre-CS licking rates and modulations during CS presentation in the stable phases of the last 150 trials before reversals. H, Pre-CS licking rates varied widely across animals. I, No correlation was observed between pre-CS licking rates and CS+ modulation peaks (see Supplementary Fig. S2B). J, The fraction of correct rejections was more broadly distributed across mice than hits. K, Pre-CS licking rates negatively correlated with correct rejection rates (see Supplementary Fig. S2C). L, Spearman’s correlation matrix (data from 17 groups) revealed significant relationships between reinforcement-learning metrics, including pre-CS licking rate, correct hit and rejection rates, CS+ modulation peak, and latency to switch after contingency reversals. Only statistically significant correlation coefficients are shown (Bonferroni-corrected for multiple comparisons). M, K-means clustering based on reward-seeking features identified three distinct learning strategies: impulsive go learners (purple), cautious no-go learners (green), and flexible learners (orange). N, The three learning strategies (color labeling as in M) differed across reward-seeking features including hit and rejection rates, pre-CS licking rates, CS switch latencies, and CS+ modulation peak. Horizontal bars denote statistical comparisons between clusters (p-values from permutation tests with n = 100,000 permutations using the median). P-values were corrected for multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR) procedure (α = 0.05). Significant comparisons after FDR correction are marked with #. CI, confidence interval; CS+, rewarded conditioned stimulus; CS−, unrewarded conditioned stimulus; SEM, standard error of the mean Note: The regression line is illustrative only; statistical inference is based on Spearman’s correlation, which does not assume linearity.

Social rank derived from incidental competitions in the integrated tube tests of the NoSeMaze.

A, Incidental encounters in the tubes triggered dyadic tube competitions. B, Network graph derived from the cumulative tube competitions in an example group over three weeks. Outgoing arrows indicate wins, incoming arrows indicate losses. Individual hierarchical positions were calculated using the David’s score and can be converted into linear social ranks from 1 (highest) to 10 (lowest). C, Box plots of metrics characterizing social hierarchy for 18 groups, including transitivity, steepness, stability, and uncertainty-by-repeatability (for details, see ‘Source Data’). Group labels correspond to original experimental group IDs. Only groups with available tube-competition data are shown. Therefore, numbering is non-consecutive (e.g., groups 16, 20, and 21 are absent; see Supplementary Table S2). D, Scatterplots show that David’s scores (z-scored) were significantly correlated across the three different weeks, indicating temporal stability of social rank within one NoSeMaze round (data from 18 groups). E, David’s scores were strongly correlated with Elo ratings, demonstrating convergent validity of social rank measures. F, Elo ratings corrected for differences in entry time highly correlated to uncorrected Elo ratings. G, Relative body weight (z-scored per group) was significantly negatively correlated to the fraction of losses in tube competitions (cube root transformed, red), with heavier animals being less likely to lose. No association was found between body weight and the fraction of wins in tube competitions (cube root transformed, dark blue). Body weight was measured prior to the animals’ introduction to the NoSeMaze, respectively. CI, confidence interval; SD, standard deviation; Note: Regression lines are illustrative only; statistical inference is based on Spearman’s correlation, which does not assume linearity.

Chasing in the NoSeMaze.

A, Schematic of the NoSeMaze setup for automated tracking of tube chasing. Chasing was quantified as the fraction of chases initiated by each individual relative to the total number of chases in their group (‘active chases’), as well as by the fraction of times being chased (see Supplementary Fig. S6A). B, Chasing occurred predominantly during the dark phase. C, Cumulative fraction of chases initiated (dark blue) and received (dark red) by individuals within each group, ordered by the fraction of chases they initiated. Chasing was concentrated in a small subset of individuals, with the two most active chasers accounting for more than 50% of all chases. Significant stars mark differences between the two curves (unpaired permutation test, n=10.000 permutations, ***, p < 0.001). D, Active chases were highly consistent across weeks. E, Active chases were not significantly correlated to relative weight before entering the NoSeMaze. However, the fraction of times being chased was negatively correlated with weight, indicating that heavier mice were less likely to be chased. CI, confidence interval; SD, standard deviation; Note: Regression lines are illustrative only; statistical inference is based on Spearman’s correlation, which does not assume linearity.

Stability of social rank, chasing, and reward-seeking features across repeated NoSeMaze rounds.

A–D, Social rank and chasing were stable across NoSeMaze rounds. Individual David’s scores (A), fraction of active chases (B), and fraction of times being chased (C) were all significantly correlated between round 1 and 2 (Spearman’s ρ = 0.57, 0.75, and 0.54, respectively; all p < 0.001). Lines in A–C are illustrative only; inference is based on Spearman’s correlation. (D) Intraclass correlation coefficients (ICC) confirm similar stability for the three metrics across all rounds. Blue circles show Spearman’s ρ between rounds 1 and 2 with 95% CIs; golden squares show ICC with 95% CIs estimated across all rounds to quantify across-round stability of each metric. E, Stability of key reward-seeking features. As in D, blue circles depict Spearman’s ρ between rounds 1 and 2 (95% CIs), whereas orange squares give ICCs computed across all rounds (95% CIs) to index stability over the full longitudinal series. Features include correct hits and correct rejections, pre-CS licking rate, CS+ modulation peak, and switch latencies for CS+ and CS− (see also Supplementary Fig. S8A-C). F, Sankey diagram illustrates consistency in individual reward-seeking strategies across rounds. Most mice retained their behavioral strategy classification (flexible, cautious, or impulsive) between rounds, with the majority of flexible and impulsive animals remaining in the same category (see Supplementary Fig. S8D). CI, confidence interval; CS+, rewarded conditioned stimulus; CS−, unrewarded conditioned stimulus Note: Regression lines are illustrative only; statistical inference is based on Spearman’s correlation, which does not assume linearity.

Relationship between social rank, chasing, and reward-seeking features.

A, Social rank (David’s score) was positively correlated with the fraction of active chases. B, Across groups, the correlation coefficient calculated between David’s scores from tube competitions and fraction of active chases (x-axis, calculated separately within each group, as in A) was negatively associated with the group-level transitivity of the social hierarchy derived from tube competitions. Groups with lower transitivity showed a stronger association between social rank and chasing, suggesting that aggressive status signaling via chasing becomes more relevant when social hierarchies are less well-defined. C, Correlation matrix showing relationships between social rank and chasing metrics. Color scale indicates Spearman’s correlation coefficients; statistically significant values (Bonferroni-corrected) are shown in white. Note that the David’s score from tube competitions correlated to both, fraction of wins and losses in tube competitions, while the ‘chasing David’s score’ was strongly correlated only to the fraction of active chases, but not to the fraction of times being chased. D-F, Wins and losses in tube competitions (D) were concentrated in the top-right, occurring most consistently between animals of opposing social ranks. In contrast, chasing interactions (E, F) clustered in the top-left, and thus mostly within a subset of mice with high social rank (F), in which they may help clarifying dominance. G, Comparison of the fraction of active chases between the three different clusters defined from the reward-seeking task. Cautious mice initiated less chases than flexible individuals (p-values from permutation tests with n = 100,000 permutations using the median). Box plots show the median (line), interquartile range (IQR, box), and whiskers extending to 1.5 × the IQR. P-values were corrected for multiple comparisons using the Benjamini–Hochberg false discovery rate (FDR) procedure (α = 0.05). Significant comparisons after FDR correction are marked with #. H, Correlation heatmap between David’s score (social rank from tube competitions), active chases, and reward-seeking features showed no significant associations after FDR correction for multiple comparison. I, Comparison of the David’s score (social rank from tube competitions) between the three different clusters defined from the reward-seeking task showed no significant difference (p-values from permutation tests with n = 100,000 permutations using the median). Box plots show the median (line), interquartile range (IQR, box), and whiskers extending to 1.5 × the IQR. J, Loading values from a principal component analysis of social rank (David’s score), chasing (active chases and being chased), and reward-seeking (cognitive) features for the top three components (PC1–PC3). Bars indicate each feature’s contribution to the respective component. PC1 and PC2 were dominated by cognitive features (p = 0.0009 and p = 0.0063), PC3 by social rank and chasing (p = 0.0016; permutation tests, n = 10,000). The largely non-overlapping loadings suggest orthogonality between social and cognitive domains. CI, confidence interval; CS+, rewarded conditioned stimulus; CS−, unrewarded conditioned stimulus Note: Regression lines are illustrative only; statistical inference is based on Spearman’s correlation, which does not assume linearity.