Average freezing is higher in male rats

Figure 2A shows the group freezing data of observers and demonstrators of both sexes. A test of normality of the freezing variables separately for the two sexes revealed that freezing during the shock epoch is normally distributed for both sexes and roles (Shapiro-Wilk47, all W > 0.9, p > 0.3). Unfortunately, freezing during baseline deviates from normality for both sexes and roles (Shapiro-Wilk, all W < 0.78, p < 0.05). Accordingly, parametric tests including the baseline should be interpreted with caution, and are supplemented by non-parametric tests where possible.

Figure 2 Emotional Contagion as a function of sex. (A) Freezing percent during the baseline (open violins) and the shock period (filled violins) for male (blue) and female (purple) rats, with observer data on the left and demonstrator data on the right. The black bar represents the mean, the box ± SEM. (B) Observer freezing as a function of demonstrator freezing during the shock period, including linear regression lines and their 95% confidence intervals. (C) Demonstrator freezing as a function of observer freezing during the shock period including linear regression lines and their 95% confidence intervals. (D) Granger causality F values in the dem- > obs (left) and obs- > dem (right) direction during the shock period. For all panels: *:p < 0.05, **:p < 0.01, ***:p < 0.001 in two-tailed t-test; ## = p < 0.005 in Wilcoxon test. Other conventions are the same as in (A). We use violin plots here, because some of the data is not normally distributed, and mean and s.e.m. therefore do not provide a full picture of the distribution of the data. Full size image

For observers a 2 Sex (male vs female) x 2 Epoch (baseline vs shock) ANOVA revealed a main effect of Epoch (F(1,17) = 56, p < 0.001, BF incl = 874958), a trend for Sex (F(1,17) = 3.86, p = 0.066, BF incl = 1.3) but no interaction (F(1,17) = 1.002, p = 0.33, BF incl = 1.3). For those less familiar with Bayesian statistics, BF incl refers to the Bayes factor for including the effect and is calculated as the likelihood of models including a certain effect divided by that of models excluding that effect. BF incl values above 3 are considered moderate evidence that including the effect improves the model, whilst BF incl below 1/3 are considered moderate evidence that including the effect worsens the model. Intermediate BF values are considered inconclusive48. So here, the Bayesian ANOVA provides very strong evidence for the effect of Epoch, but with regard to the effect of Sex and interaction, the data is inconclusive, in that the variance is such that the data is about equally likely with models with or without these effects. Paired tests comparing observers’ freezing during the baseline and the shock period confirm that witnessing the demonstrator receive shocks increases freezing compared to baseline in both sexes examined individually (Wilcoxon W(9) = 0, p = 0.002 for males; W(8) = 0, p = 0.004 for females; Fig. 2A). We then compared the freezing of the male versus the female observers during the shock period. Considering the findings from previous research, we had hypothesized lower freezing in females compared to male observers. A one-tailed t-test on the observer freezing confirmed this prediction (t(17) = 1.8, p < 0.044), with a large effect size with males freezing 1.5 times as much as females (Cohen d = 0.8, Fig. 2A).

For demonstrators a 2 Sex (male vs female) x 2 Epoch (baseline vs shock) on freezing also revealed a main effect of Epoch (F(1,17) = 115, p < 0.001, BF incl = 1E10), but also revealed a significant main effect for Sex (F(1,17) = 19, p < 0.001, BF incl = 208) and an interaction (F(1,17) = 11, p = 0.004, BF incl = 103), due to a larger increase in freezing following the shocks in males. A one-tailed t-test again confirms that during the shock epoch, male demonstrators froze more than female demonstrators (t(17) = 3.97, p < 0.001) with an even larger effect size than for the observers: males froze twice as much as females (Cohen d = 1.8, Fig. 2A).

For both observers and demonstrators, males thus froze more than females. That the effect size was more pronounced for demonstrators (d = 1.8) than observers (d = 0.8) raises the question of whether the smaller sex difference in observers is a downstream result of the larger sex difference in demonstrators, a question we will address in the next section.

Demonstrator freezing level, independently of sex, is the best predictor of observer freezing

To understand whether sex differences in observers’ freezing during the shock epoch (obs f ) were due to differences in the demonstrators’ freezing (dem f ) during the shock period alone we performed an ANCOVA, which tests whether after regressing out the individual differences in demonstrator freezing (dem f ), there is a residual main effect of observer sex, or an interaction of dem f and observer sex. This was done using a traditional (frequentist) ANCOVA and a Bayesian ANCOVA with 2 sex (male vs female) x dem f , see Table 1A.

Table 1 Analysis of observer freezing considering the shock period only. Full size table

The frequentist analysis revealed a significant influence of dem f confirming that the observer freezing reflects the demonstrator freezing but neither a significant main effect of sex or interaction of sex * dem f were detected. This shows, that once the difference in demonstrator freezing have been accounted for, sex fails to explain additional variance. This suggests, that difference in observer freezing can be most parsimoniously explained by knowing the level of freezing of the demonstrator, independently of sex. In Fig. 2B, this is apparent in the linear regression lines that have confidence intervals of the slope that overlap. In addition, Fig. 2B shows how when considering females paired with the higher-freezing female demonstrators (on the right of the figure), the confidence interval (pink) overlaps considerably with the male data. This suggests that the differences in demonstrator freezing simply acted as distinct input onto a transmission function (i.e. slope and offset) that is the same independently of the sex of the observer. Given that female demonstrators reacted to the shocks with less freezing, this simply transforms into the group difference in observer freezing we observe.

A non-significant main effect of sex or interaction with sex could reflect evidence that there is no effect of sex (evidence of absence), or that our study was underpowered and cannot speak for or against the absence of a sex difference. To shed light on this issue, we performed a Bayesian ANCOVA that explains observer freezing using competing models with or without sex as a factor (Table 1B). A model only considering dem f is the best model by a margin (P(M|data), Table 1B), and an analysis of effects provides strong evidence for an effect of dem f (BF incl = 19), confirming that the demonstrator freezing strongly determines observer freezing. The analysis also shows that the evidence leans towards the absence of an effect of sex, be it as a main effect (BF incl = 0.471) or sex*dem f interaction (BF incl = 0.463), showing that the data is over twice as likely in models without these factors than in models with them (BF incl = 1/2 = 0.5 indicates that models without the factor are twice as likely as models with, and values below 0.5 show that models without the effect are more then twice as likely). Indeed, comparing the probability of different models given the data (Table 1B, column P(M|data)) shows a full model in which sex and its interaction are included (sex + dem f + sex * dem f ) is over 5 times less likely than one only including dem f . Altogether this shows that our data is best explained by a model that considers the level of freezing of the demonstrator (which is different for male and female demonstrators, as shown above) but ignores the sex of the animals involved.

To further characterize the relation between observers’ and demonstrators’ freezing in the two sexes, we performed separate Bayesian regressions for males and females. This gave highly overlapping posterior estimates for the regression weight for obs f = β*dem f + intercept, with β for females having mean = 0.44 (95% credibility interval CI = [0.0,1.09]) and male having mean = 0.36(95%CI = [−0.5,1.9]). Figure 2B illustrates this as the similarity in slope.

In our past work28, we have shown that differences in the freezing level of the observer can influence back the freezing level of the demonstrator, a phenomenon akin to social buffering, in that observers that showed unusually low levels of freezing due to inactivation of the ACC reduced freezing levels in the demonstrators. To explore whether there might be a sex difference in this influence of obs f on dem f , we performed a frequentist ANCOVA and a Bayesian model comparison between different models explaining freezing of the demonstrators as a function of sex, freezing of observers (obs f ) and their interaction (sex*obs f , Table 2). The frequentist ANCOVA showed significant main effects (Table 2A). The Bayesian model comparison found that including sex and observers’ freezing in an additive model (obs f + sex) best describes the data (Table 2B). There was strong evidence for a contribution of sex in predicting demonstrator freezing (BF incl = 14.712), with the females freezing less to the shock than the males. There was also strong evidence for a contribution of observer freezing (BF incl = 18.390). However, there was only a trend and anecdotal evidence for including an interaction effect (BF incl = 2.804) indicating that if there was a sex difference in the feedback from the observer to the demonstrator, we would need a larger group to find robust evidence for such an effect. This is evident in the similarity in slope across the sexes in Fig. 2C.

Table 2 Analysis of demonstrator freezing during the shock period. Full size table

No sex difference in granger-causality across the animals

To further explore whether males and females differ in the temporal coupling of the freezing between demonstrators and observers, we performed Granger causality analyses (Fig. 2D). Unlike the other analyses that explore the average freezing over the 12 min of the shock period, the Granger causality analyses explore the relation between the second-to-second freezing of demonstrators and observers. Specifically, it examines if past demonstrator freezing can explain present observer freezing (to quantify influences in the dem → obs direction), and if past observer freezing can explain present demonstrator freezing (to quantify influences in the obs → dem direction). Higher G-causality values (i.e. Granger F values) indicate higher temporal coupling of the behavior of the two animals in a pair, and thereby stronger social sensitivity to the behavior of the other. A Granger analysis considering all animals (irrespective of sex) revealed significant information flow in both directions (dem → obs Granger F = 0.039, p < 0.0001; obs → dem Granger F = 0.034, p < 0.0001). Because the G-causality values were not normally distributed (Shapiro W < 0.76, p < 0.05), we used non-parametric tests to compare the sexes. We found no significant sex difference in Granger causality in either direction (Mann-Whitney U, dem → obs, U(17) = 38, p = 0.6; obs → dem, U(17) = 41, p = 0.78; Fig. 2D). Bayesian Mann-Whitney U tests revealed that in both directions, the evidence leans towards the null hypothesis (i.e. no sex difference), but with limited evidence strength: in dem → obs direction, the Bayes factor in favor of the null hypothesis BF 01 = 2.4, in the obs → dem direction BF 01 = 2.1.

A trade-off between rearing and freezing

The finding that the female demonstrators froze less than their male counterparts raises the question of whether they reacted to the shocks using an alternative strategy. It has often been described that individual and sex differences exist in the propensity to react to danger with escape vs freezing33. We thus explored whether females reared (including attempts to climb out of the box) more than their male counterparts (Fig. 3A). All rearing data were normally distributed (Shapiro W > 0.86, p > 0.1) except for male observer rearing during the shock epoch.

Figure 3 Rearing/climbing as a function of sex. (A) distribution of rearing and climbing. (B) The trade-off of rearing and climbing during the shock epoch for demonstrators. All conventions as in Fig. 2. ## = p < 0.01 in the Wilcoxon test. Full size image

All groups showed reduced rearing/climbing following the shocks (Fig. 3A). To explore if that reduction is sex-dependent, we performed mixed frequentist ANOVAs separately for observer rearing and demonstrator rearing, including 2 Sexes (male vs female) x 2 Epochs (baseline vs shock). We found main effects of Epoch in both cases (Obs: F(1,17) = 67, p < 0.001, BF incl = 8.7E7; Dem: F(1,17) = 25, p < 0.001, BF incl = 2478), but no main effect of Sex (Obs: F(1,17) = 2.4, p = 0.141, BF incl = 0.66; Dem: F(1,17) = 0.121, p = 0.732, BF incl = 0.56) or interaction of Sex x Epoch (Obs: F(1,17) = 0.004, p = 0.95, BF incl = 0.66; Dem: F(1,17) = 2.3, p = 0.146, BF incl = 1.25).

We also observed a consistent negative correlation between rearing and freezing in our animals (Fig. 3B). To explore that relationship further, we performed ANCOVAs that explore rearing during the shock period as a function of sex, freezing and sex * freezing, separately for observers and demonstrators. In both cases, the effect of freezing was negative (Obs: F(1,15) = 11.8, p = 0.004, BF incl = 21.5; Dem: F(1,15) = 4.15, p = 0.06, BF incl = 6.07) while the effect of sex (Obs: F(1,15) = 0.12, p = 0.73, BF incl = 0.39, Dem: F(1,15) = 0.30, p = 0.59, BF incl = 0.5) or sex*freezing interaction (Obs: F(1,15) = 0.13, p = 0.72, BF incl = 0.4, Dem: F(1,15) = 0.08, p = 0.78, BF incl = 0.55) were negligible, suggesting a sex-independent trade-off: the more an individual freezes, the less it rears, and vice-versa, confirming the notion that animals that froze less reared more. While most male demonstrators consistently showed high levels of freezing (>50%) and low rearing/climbing (<20%) during the shock period, interindividual differences were salient amongst female demonstrators (Fig. 3B): about half showed a pattern similar to the males, with freezing above 50% and low rearing/climbing, whilst the other half showed a pattern only seen in females, with low levels of freezing (<50%) but higher levels of rearing/climbing (>20%). Hence, while the trade-off is similar across the sexes (similar regression lines), females seem to use a broader range along this trade-off.

No sex differences in freezing to shocks during pre-exposure freezing/rearing

Considering the large sex differences in demonstrator freezing in response to shocks, we also analyzed freezing levels during pre-exposure, where the observers experienced shocks (Fig. 4A). Based on the results from the demonstrator rats, we expected male observers to show about twice as much freezing as female observers. Because freezing in the males during pre-exposure was not normally distributed (Shapiro, W = 0.7, p < 0.001), we used non-parametric tests. Tests revealed significant increases in freezing from baseline to shock in both sexes (Wilcoxon, females: W = 0, p < 0.004, males: W = 0, p < 0.002). While during baseline, males showed significantly higher freezing levels in response to a novel environment (Mann-Whitney U = 13, p = 0.008), no sex-driven differences in freezing were detected during the shock epoch (Mann-Whitney U = 39, p = 0.66). Females during the shock period of this pre-exposure froze much more (mean = 92%; SEM = 3%), than their female demonstrators later did in the contagion test (mean = 37%; SEM = 29%, Mann-Whitney U = 0, p < 0.001).

Figure 4 Freezing level for animals receiving shocks. (A) freezing in observer animals receiving shocks during pre-exposure. (B) freezing of demonstrators receiving shocks during the contagion test of the limited bedding and nesting (LBN) pilot group. The experimental schema above the panels illustrates the shock parameters and the fact that animals were alone in a new context in (A) but together with another animal in a familiar context in (B). ##: Wilcoxon test, p < 0.01, #: Wilcoxon test, p < 0.05. $$: Mann-Whitney U, p < 0.01. **: t-test p < 0.01. Full size image

To explore whether observers that froze more during pre-exposure also froze more while observing their demonstrator receive shocks, despite the non-normality of the male pre-exposure freezing, we tentatively performed an ANCOVA on observer freezing during the shock epoch in the emotional contagion test that included sex as a fixed factor and demonstrator freezing and observer freezing during pre-exposure as covariates. The analysis confirmed that demonstrator freezing explains observer freezing during the shock epoch (F(1,15) = 8.3, p = 0.011, BF incl = 22), but neither sex (F(1,15) = 0.08, p = 0.78, BF incl = 0.5) nor pre-exposure freezing do (F(1,15) = 1.23, p = 0.28, BF incl = 0.6).