Individual differences drive social hierarchies in male mouse societies
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Johannes Gutenberg University, Germany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany
- Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany
- Department of Clinical Health Technologies, Institute for Manufacturing Engineering and Automation, Fraunhofer Society, Germany
Figures
Figure 1 with 1 supplement
NoSeMaze setup, cohort structure, and accelerated longitudinal reshuffling design.
(A) The NoSeMaze (see Figure 1—figure supplement 1) 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.
Figure 1—figure supplement 1
Illustration of NoSeMaze modules.
Illustration of the NoSeMaze containing the automated tube test and the reward learning module. NoSeMaze, Non-invasive Sensor-rich Maze; RFID, radio-frequency identification.
Figure 2 with 2 supplements
Inter-individual differences in reinforcement learning in the Non-invasive Sensor-rich Maze (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 s 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, n = 39 mice) in each phase (B). 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 Figure 2—figure supplement 1B). (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 Figure 2—figure supplement 1C). (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, α=0.05). (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. Box plots show the median (line), interquartile range (IQR; box), and whiskers extending to the most extreme values within 1.5 × IQR (n = 30, 45, and 87 mouse-round observations for cautious, impulsive, and flexible learners, respectively). Horizontal bars denote statistical comparisons between clusters (p-values from permutation tests with 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.
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Figure 2—source data 1
Source data and statistical reporting for Figure 2.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig2-data1-v1.xlsx
Figure 2—figure supplement 1
Number of trials at the stimulus–outcome (S–O) learning module and associations between reward-seeking features.
(A) Box plots (median, 25th and 75th percentile) show the number of trials performed per animal per day at the S–O learning module during the first three weeks in the NoSeMaze. Whiskers extend to the most extreme values within 1.5× the interquartile range; individual points beyond the whiskers represent outliers. The data were obtained from 17 groups (each n=9–10 male mice on C57BL/6 J background, n=163 mouse-round observations in total). (B) No significant correlation was found between the pre-CS licking rate and the CS+ modulation peak. The CS+ modulation peak values were calculated from the 150 trials before each reversal (stable lick phase) across the 17 groups, while the pre-CS licking rate was computed from all trials. The dashed line marks the diagonal. (C) The pre-CS licking rate and the correct rejection rate were strongly negatively correlated. All trials were used to calculate these rates. CS, conditioned stimulus.
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Figure 2—figure supplement 1—source data 1
Source data for Figure 2—figure supplement 1.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig2-figsupp1-data1-v1.xlsx
Figure 2—figure supplement 2
Association between pre-CS licking rate and switch latencies during reversal learning at the stimulus–outcome (S–O) learning module.
(A–D) The pre-conditioned stimulus (pre-CS) licking rate was negatively correlated with the switch latencies at the CS +across reversals 1–4. Both variables were cube root transformed. (E–H) The pre-CS licking rate was positively correlated with the switch latencies at the CS− across reversals 1–4. Both variables were cube root transformed. CS, conditioned stimulus.
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Figure 2—figure supplement 2—source data 1
Source data for Figure 2—figure supplement 2.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig2-figsupp2-data1-v1.xlsx
Figure 3 with 5 supplements
Social rank derived from incidental competitions in the integrated tube tests of the Non-invasive Sensor-rich Maze (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 n = 18 groups, including transitivity, steepness, stability, and uncertainty-by-repeatability (for details, see Figure 3—source data 1). Boxes show the median (line) and interquartile range; whiskers extend to the most extreme values within 1.5 × the interquartile range. Individual points represent groups. 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 file 1). (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 with 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. 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.
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Figure 3—source data 1
Source data and statistical reporting for Figure 3.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig3-data1-v1.xlsx
Figure 3—figure supplement 1
Number and timing of tube competitions and association between the David’s scores from manual tube tests and tube competitions in the NoSeMaze.
(A) Boxplots (median, 25th and 75th percentile) illustrate the number of tube competitions per day and group during the first three weeks in the NoSeMaze. Whiskers extend to the most extreme values within 1.5× the interquartile range; individual points beyond the whiskers represent outliers. The mean value over all days is represented by a dashed line. The data are derived from n = 18 different groups (each n=9–10 male mice). (B) The polar histogram shows the distribution of the relative number of tube competitions across the 24 hr cycle. Most competitions occurred during the dark phase (between 20:00 and 08:00). (C) The David’s scores from tube competitions in the NoSeMaze over three weeks positively correlated with the David’s scores from manual tube tests after the animals had been removed from the NoSeMaze. The data were derived from 10 groups (each with 9–10 animals). NoSeMaze, Non-invasive Sensor-rich Maze.
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Figure 3—figure supplement 1—source data 1
Source data for Figure 3—figure supplement 1.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig3-figsupp1-data1-v1.xlsx
Figure 3—figure supplement 2
Relationship between body weight and outcomes of tube competitions in the Non-invasive Sensor-rich Maze (NoSeMaze).
(A) Relative body weight before entering the NoSeMaze was positively associated with the David’s score. (B) Body weight (z-scored per group) was significantly negatively correlated with the fraction of losses in tube competitions. (C) No association was found between body weight and the fraction of wins in tube competitions.
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Figure 3—figure supplement 2—source data 1
Source data for Figure 3—figure supplement 2.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig3-figsupp2-data1-v1.xlsx
Figure 3—video 1
Example of an incidental tube competition in the Non-invasive Sensor-rich Maze (NoSeMaze).
Representative example of an incidental dyadic tube competition in the integrated tube-test module of the NoSeMaze.
Figure 3—video 2
Example of an incidental tube competition in the Non-invasive Sensor-rich Maze (NoSeMaze).
Representative example of an incidental dyadic tube competition in the integrated tube-test module of the NoSeMaze.
Figure 3—video 3
Example of an incidental tube competition in the Non-invasive Sensor-rich Maze (NoSeMaze).
Representative example of an incidental dyadic tube competition in the integrated tube-test module of the NoSeMaze.
Figure 4 with 2 supplements
Chasing in the Non-invasive Sensor-rich Maze (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 Figure 4—figure supplement 1A). (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, separately ordered by the respective fraction of chases initiated or received. Lines show the mean and shaded areas ± SD across n = 18 groups. 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, 10,000 permutations based on the mean, ***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.
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Figure 4—source data 1
Source data and statistical reporting for Figure 4.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig4-data1-v1.xlsx
Figure 4—figure supplement 1
Distributions of win–loss outcomes from chasing and tube competitions in the Non-invasive Sensor-rich Maze (NoSeMaze).
(A) Histograms show the fraction of active chases (violet) and times being chased (dark red) in chasing events per animal, together with their scaled gamma fits (left panel). The two distributions were significantly different (Kolmogorov–Smirnov test, p<0.0001). The cumulative probability plot of the same data is shown in the right panel, highlighting the differences between the distributions. (B) Histograms show the fraction of wins (violet) and losses (dark red) in tube competitions per animal, together with their scaled fits (left panel). The two distributions did not differ significantly (Kolmogorov–Smirnov test, p=0.7135). The cumulative probability plot of the same data is shown in the right panel, further illustrating the similarity between the distributions.
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Figure 4—figure supplement 1—source data 1
Source data for Figure 4—figure supplement 1.
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Figure 4—figure supplement 2
Association of the fraction of times being chased across different weeks in the Non-invasive Sensor-rich Maze (NoSeMaze).
Scatterplots show that the fraction of times being chased was significantly correlated across all three weekly comparisons within one NoSeMaze round, indicating temporal stability of this metric. Data were obtained from 18 groups and cube-root transformed.
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Figure 4—figure supplement 2—source data 1
Source data for Figure 4—figure supplement 2.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig4-figsupp2-data1-v1.xlsx
Figure 5 with 3 supplements
Stability of social rank, chasing, and reward-seeking features across repeated Non-invasive Sensor-rich Maze (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 0.57, 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% bootstrap CIs estimated across all rounds (n = 79 mice contributing 178 mouse-round observations) 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% bootstrap CIs), whereas orange squares give ICCs computed across all rounds (n = 79 mice contributing 178 mouse-round observations; 95% bootstrap 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 Figure 5—figure supplement 1A–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 Figure 5—figure supplement 1D). 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.
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Figure 5—source data 1
Source data and statistical reporting for Figure 5.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig5-data1-v1.xlsx
Figure 5—figure supplement 1
Stability of reward-seeking features across Non-invasive Sensor-rich Maze (NoSeMaze) rounds with different group members.
(A–C) Scatterplots show strongly significant positive correlations of individual behavioral metrics between round 1 and 2 in the NoSeMaze for (A) the average pre-conditioned stimuli (CS) licking rate, (B) the correct rejection rate, and (C) the CS+ modulation peak. Each dot represents one animal. Group compositions differed between the two rounds. (D) The consistency matrix shows individual behavioral clustering into cautious, impulsive, and flexible learners across both NoSeMaze rounds. Most animals retained their classification between rounds, indicating stability of behavioral learning profiles despite changes in group composition.
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Figure 5—figure supplement 1—source data 1
Source data for Figure 5—figure supplement 1.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig5-figsupp1-data1-v1.xlsx
Figure 5—figure supplement 2
Effects of OXTRΔAON on social rank and chasing.
(A–C) Comparisons between control and OXTRΔAON mice for (A) David’s score (z-scored from tube competitions), (B) fraction of active chases, and (C) fraction of times being chased across all Non-invasive Sensor-rich Maze (NoSeMaze) rounds (n = 79 mice: 53 control and 26 OXTRΔAON, contributing 178 mouse-round observations). The four panels show the comparison, including all weeks (far left), and separately for week 1 (middle left), week 2 (middle right), and week 3 (far right). Box plots show the median (line), interquartile range (IQR; box), and whiskers extending to the most extreme values within 1.5 × IQR. Individual points represent mouse-round observations. No significant group differences were detected, except for a transient reduction in the David’s score for week 1 (A, second panel). Group differences were assessed using permutation tests (10,000) based on group medians. To account for repeated measures, group labels were shuffled at the level of the animal. Week-wise comparisons were marked with # if surviving false discovery rate (FDR) correction. The findings indicate that selective Oxtr deletion in the AON pars centralis exerted only minor and short-lived effects on social rank and chasing.
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Figure 5—figure supplement 2—source data 1
Source data for Figure 5—figure supplement 2.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig5-figsupp2-data1-v1.xlsx
Figure 5—figure supplement 3
Effects of OXTRΔAON on reinforcement-learning measures in the Non-invasive Sensor-rich Maze (NoSeMaze).
(A–F) Comparisons between control and OXTRΔAON mice for key reinforcement-learning features: (A) pre-conditioned stimuli (CS) licking rate, (B) correct hit rate, (C) correct rejection rate, (D) CS+ modulation peak, (E) CS+ switch latency, and (F) CS− switch latency (n = 76 mice: 50 control and 26 OXTRΔAON, contributing 163 mouse-round observations). Box plots show the median (line), interquartile range (IQR; box), and whiskers extending to the most extreme values within 1.5 × IQR. Individual points represent mouse-round observations. No significant group differences were detected in any measure. Group differences were assessed using permutation tests (10,000) based on group medians, with group labels shuffled at the animal level to account for repeated measures. These results indicate that selective Oxtr deletion in the AON pars centralis did not affect reinforcement-learning performance in the NoSeMaze.
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Figure 5—figure supplement 3—source data 1
Source data for Figure 5—figure supplement 3.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig5-figsupp3-data1-v1.xlsx
Figure 6 with 5 supplements
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 n = 18 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 the 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 clarify dominance. (G) Comparison of the fraction of active chases between the three different clusters defined from the reward-seeking task. Cautious mice initiated fewer chases than flexible individuals (p-values from permutation tests with 100,000 permutations using the median). Box plots show the median (line), interquartile range (IQR; box), and whiskers extending to the most extreme values within 1.5 × IQR (n = 30, 45, and 87 mouse-round observations for cautious, impulsive, and flexible learners, respectively). 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 comparisons. (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 100,000 permutations using the median). Box plots show the median (line), interquartile range (IQR, box), and whiskers extending to the most extreme values within 1.5 × IQR (n = 30, 45, and 87 mouse-round observations for cautious, impulsive, and flexible learners, respectively). (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, 10,000 permutations). 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.
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Figure 6—source data 1
Source data and statistical reporting for Figure 6.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig6-data1-v1.xlsx
Figure 6—figure supplement 1
Association between active chases of the animal with the highest social rank and the transitivity of the social hierarchy in the respective group.
Across groups, the fraction of active chases from the mouse with the highest social rank (ranking based on David’s scores from tube competitions) was negatively associated with group-level transitivity. Groups with lower transitivity showed a higher fraction of active chases, suggesting that aggressive status signaling via active chases becomes more relevant when social hierarchies are less well-defined.
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Figure 6—figure supplement 1—source data 1
Source data for Figure 6—figure supplement 1.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig6-figsupp1-data1-v1.xlsx
Figure 6—figure supplement 2
Validation of social rank metrics in tube competitions and chasing.
(A–C) Correlations between the z-scored David’s score (derived from automated tube competition data) and (A) the fraction of wins, (B) the fraction of losses, and (C) the total number of tube competition events per animal (all cube root transformed). David’s score was strongly associated with both the fraction of wins (r=0.76) and the fraction of losses (r = –0.67), confirming symmetry in dominance relationships. No significant association was found between David’s score and competition frequency (r=0.11, p=0.139), suggesting that tube encounters occurred incidentally and were not strategically avoided. (D–F) Correlations between the z-scored ‘chasing David’s score’ and (D) the number of chasing events, (E) the fraction of active chases, and (F) the fraction of times being chased (all cube root transformed). The ‘chasing David’s score’ was positively associated with both the number of chases (r=0.45) and the fraction of active chases (r=0.85), but showed no relationship with the fraction of times being chased (r = –0.01). These findings indicate that chasing behavior was asymmetrically distributed across individuals and reflected volitional initiation rather than reciprocal dominance-subordination interactions.
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Figure 6—figure supplement 2—source data 1
Source data for Figure 6—figure supplement 2.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig6-figsupp2-data1-v1.xlsx
Figure 6—figure supplement 3
Divergent win–loss relationships in tube competitions and chasing.
(A) Scatterplot comparing the fraction of wins (orange) and the fraction of active chases (violet) against the corresponding fraction of losses and times being chased, respectively. Linear fits show that the strong negative relationship between wins and losses in tube competitions differs markedly from the flatter and mildly positive relationship observed for active chases and being chased. (B) Fixed-effect estimates from a linear mixed-effects model (LME) predicting the fraction of ‘wins’ across both behavioral modalities. The model included the fraction of ‘losses,’ event type (tube competition vs. chasing), and their interaction as fixed effects, with mouse identity as a random intercept. Bootstrapped confidence intervals (10,000 cluster-resampled iterations) are shown for each term. A significant interaction confirmed that the relationship between win and loss fractions differs between tube competitions and chasing behavior, supporting a lack of symmetry in chasing.
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Figure 6—figure supplement 3—source data 1
Source data for Figure 6—figure supplement 3.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig6-figsupp3-data1-v1.xlsx
Figure 6—figure supplement 4
Distribution of normalized dyadic competition and chasing events across matrix quadrants.
(A) Box plots show the distribution of normalized dyadic competition event counts for each quadrant of the competition matrix (Figure 6D), where animals are sorted by their competition-based social ranks. Box plots show the median (line), interquartile range (IQR; box), and whiskers extending to the most extreme values within 1.5 × IQR. The matrices were divided into four quadrants: top-left (dominant vs. dominant), top-right (dominant vs. subordinate), bottom-left (subordinate vs. dominant), and bottom-right (subordinate vs. subordinate). Each point represents a single dyadic value (i.e. a specific pair of individuals) from one group. Data were obtained from n = 18 groups, comprising 360, 445, 445, and 352 dyadic observations in the top-left, top-right, bottom-left, and bottom-right quadrants, respectively. Normalization was performed within each group by its maximum value to ensure comparability across groups. (B) Same as A, but for chasing event counts. Dyadic chasing events were extracted from matrices sorted by the fraction of active chases and analyzed by quadrant. The numbers of dyadic observations per quadrant were the same as in A. (C) Same as B, but chasing events were extracted from matrices sorted by competition-based social ranks. The numbers of dyadic observations per quadrant were the same as in A. Notably, the highest frequency of chasing events occurred in the top-left quadrant, indicating frequent chasing between individuals with high social ranks. Beta linear mixed-effects (LMEs) were applied using quadrant as a fixed effect and group as a random intercept. Pairwise post hoc comparisons between quadrants were conducted on the response scale with Tukey-adjusted p-values for multiple comparisons (# indicates adjusted p < 0.05). For details, see Methods.
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Figure 6—figure supplement 4—source data 1
Source data for Figure 6—figure supplement 4.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig6-figsupp4-data1-v1.xlsx
Figure 6—figure supplement 5
Comparison of the fraction of times being chased across the behavioral clusters defined from the reward-seeking task.
No significant differences were found between the cautious, impulsive, and flexible clusters (p-values from permutation tests with 100,000 permutations using the median). Boxplots show the median (line), interquartile range (interquartile range IQR, box), and whiskers extending to the most extreme values within 1.5 × IQR (n = 30, 45, and 87 mouse-round observations for cautious, impulsive, and flexible learners, respectively).
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Figure 6—figure supplement 5—source data 1
Source data for Figure 6—figure supplement 5.
- https://cdn.elifesciences.org/articles/109354/elife-109354-fig6-figsupp5-data1-v1.xlsx
Additional files
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Supplementary file 1
Overview of group characteristics and acquisition of behavioral data in the NoSeMaze.
ID, identity; NoSeMaze, Non-invasive Sensor-rich Maze; SD, standard deviation.
- https://cdn.elifesciences.org/articles/109354/elife-109354-supp1-v1.xlsx
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Supplementary file 2
Overview of group compositions and animal information.
OXTRΔAON, bilateral oxytocin receptor deletion in the AON pars centralis; RFID, radio-frequency identification; WT, wild type.
- https://cdn.elifesciences.org/articles/109354/elife-109354-supp2-v1.xlsx
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Supplementary file 3
Differences in conditioned stimuli (CS)+ and CS− switch latencies between NoSeMaze rounds.
Observed median differences (round 1 – round 2) are shown for each reversal and the overall median across all reversals. P-values were calculated using two-tailed paired permutation tests on the median (10,000 permutations).
- https://cdn.elifesciences.org/articles/109354/elife-109354-supp3-v1.xlsx
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Supplementary file 4
Round participation across datasets.
Distribution of the number of Non-invasive Sensor-rich Maze (NoSeMaze) rounds contributed per mouse for the tube dataset (competition-based social rank and chasing) and the lickport dataset (reinforcement learning). Percentages refer to the total number of mice available in each dataset. Most animals contributed at least two rounds (tube: 68/79, 86.1%; lickport: 65/78, 83.3%), indicating that R1–R2 stability estimates are based on paired observations for the majority of subjects.
- https://cdn.elifesciences.org/articles/109354/elife-109354-supp4-v1.xlsx
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Supplementary file 5
Cross-round stability of social and reinforcement-learning metrics.
Stability was quantified by Spearman correlations between round 1 and round 2 (R1–R2) with 95% bootstrap confidence intervals, and intraclass correlation coefficients (ICCs) estimated from variance-component mixed-effects models across all available rounds. ‘ICC no age’ denotes ICCacross_group from a restricted maximum likelihood (REML) model with mouse identity as a random intercept and group as a random effect (repetition included as a fixed effect; cf. Methods). ‘ICC age-adj’ includes an age decomposition (group-mean age and within-group deviation) as additional fixed effects. ‘ICC balanced first2’ recomputes ICCacross_group in a conservative balanced subset restricted to each mouse’s first two observed sessions and mice with both sessions, to control for unequal round participation. npairs indicates the number of mice contributing data to both R1 and R2.
- https://cdn.elifesciences.org/articles/109354/elife-109354-supp5-v1.xlsx
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Supplementary file 6
Effects of repetition and age on mean metric levels.
Mixed-effects model diagnostics testing whether metric values shift systematically with session experience (repetition) or age. Reported are likelihood-ratio test p-values for adding repetition conditional on the age block (prep, cond.) and for adding the age block conditional on repetition (page, cond.). The age block was decomposed into a between-group component (mean age per group; βmean) and a within-group component (each animal’s deviation from its group mean; βrel), with corresponding p-values for each coefficient from the full model. These tests assess mean-level shifts (age/experience effects) and are reported separately from the stability estimates in Supplementary file 5.
- https://cdn.elifesciences.org/articles/109354/elife-109354-supp6-v1.xlsx
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MDAR checklist
- https://cdn.elifesciences.org/articles/109354/elife-109354-mdarchecklist1-v1.pdf
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Reporting standard 1
ARRIVE Guidelines 2.0 Author Checklist.
- https://cdn.elifesciences.org/articles/109354/elife-109354-repstand1-v1.pdf
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Individual differences drive social hierarchies in male mouse societies
eLife 15:RP109354.
https://doi.org/10.7554/eLife.109354.3
