Author response:
Reviewer #1 (Public review):
Summary:
This study investigates how mice make defensive decisions when exposed to visual threats and how those decisions are influenced by reward value and social hierarchy. Using a naturalistic foraging setup and looming stimuli, the authors show that higher threat leads to faster escape, while lower threat allows mice to weigh reward value. Dominant mice behave more cautiously, showing higher vigilance. The behavioral findings are further supported by a computational model aimed at capturing how different factors shape decisions.
Strengths:
(1) The behavioral paradigm is well-designed and ethologically relevant, capturing instinctive responses in a controlled setting.
(2) The paper addresses an important question: how defensive behaviors are influenced by social and value-based factors.
(3) The classification of behavioral responses using machine learning is a solid methodological choice that improves reproducibility.
Weaknesses:
(1) Key parts of the methods are hard to follow, especially how trials are selected and whether learning across trials is fully controlled for. For example, it is unclear whether animals are in the nest during the looming stimulus presentations. The main text and methods should clarify whether multiple mice are in the nest simultaneously and whether only one mouse is in the arena during looming exposure. From the description, it seems that all mice may be freely exploring during some phases, but only one is allowed in the arena at a time during stimulus presentation. This point is important for understanding the social context and potential interactions, and should be clearly explained in both the main text and methods.
When the door system operated normally, only one mouse was allowed in the arena at a time. Specifically, when all mice were in the nest, the door between the nest and the tunnel was left open, while the door between the tunnel and the arena remained closed. When a single mouse entered the tunnel, as detected by the OpenMV camera, the door to the nest closed and the door to the arena opened, allowing only that mouse to enter the arena.
All mice were habituated to the behavioral platform for two days before the looming test. On the first day, five mice from the same home cage were placed in the nest for 30 minutes with all doors closed. Then, under normal door operation, each mouse was allowed to explore the arena individually for at least 10 minutes and access the reward at least twice. Afterward, all five mice were returned to the nest, and all doors were opened to allow free exploration of the arena for two hours. This phase ensured that each mouse learned that a reward was available at the end of the linear arena. On the second day, each mouse was given one hour to explore the arena individually under normal door operation.
On the third day, mice were exposed to the looming stimuli under normal door operation. The stimulus was triggered when the mouse’s position was detected within 20 cm of the reward port located at the end of the arena.
We will clarify these details in the main text and Methods section.
(2) It is often unclear whether the data shown (especially in the main summary figures) come from the first trial or are averages across several exposures. When is the cut-off for trials of each animal? How do we know how many trial presentations were considered, and how learning at different rates between individuals is taken into account when plotting all animals together? This is important because the looming stimulus is learned to be harmless very quickly, so the trial number strongly affects interpretation.
Because the probability of defensive responses decreased from nearly 100% to ~70% over the first five trials and then remained relatively stable through the 10th trial, the data shown in Figures 3, 4 and 5 were taken from the first 10 trials. To account for individual differences in learning rate, we will validate our findings using only the first trials and incorporate individual-level analyses of learning rates and decision patterns in the revised manuscript.
(3) The reward-related effects are difficult to interpret without a clearer separation of learning vs first responses.
As noted above, we will re-analyze reward-related data using only the first trials to separate reward effects from learning.
(4) The model reproduces observed patterns but adds limited explanatory or predictive power. It does not integrate major findings like social hierarchy. Its impact would be greatly improved if the authors used it to predict outcomes under novel or intermediate conditions.
We will expand the model to incorporate the effects of social hierarchy on decision-making and predict outcomes under novel conditions.
(5) Some conclusions (e.g., about vigilance increasing with reward) are counterintuitive and need stronger support or alternative explanations. Regarding the interpretation of social differences in area coverage, it's also possible that the observed behavioral differences reflect access to the nesting space. Dominant mice may control the nest, forcing subordinates to remain in the open arena even during or after looming stimuli. In this case, subordinates may be choosing between the threat of the dominant mouse and the external visual threat. The current data do not distinguish between these possibilities, and the authors do not provide evidence to support one interpretation over the other. Including this alternative explanation or providing data that addresses it would strengthen the conclusions.
We compared differences in total arena visit duration between dominant and subordinate mice across three phases: before, during, and after looming exposure (Figure 4C). Subordinates spent significantly more time in the arena prior to looming exposure, suggesting that subordinates perceived a threat from dominant conspecifics in the absence of external visual threat. However, this difference disappeared during and after looming exposure, indicating that the social threat relationship may be altered by the presence of an external threat, and that subordinates adapt to the conspecific threat under such conditions. To further validate this shift in their relationship, we will analyze their behaviors in the nest as suggested.
(6) While potential neural circuits are mentioned in the discussion, an earlier introduction of candidate brain regions and their relevance to threat and value processing would help ground the study in existing systems neuroscience.
We will revise the Introduction to incorporate relevant brain regions and neural circuits involved in threat and value processing.
(7) Some figures are difficult to interpret without clearer trial/mouse labeling, and a few claims in the text are stronger than what the data fully support. Figure 3H is done for low contrast, but the interesting findings will be to do this experiment with high contrast. Figure 4H - I don't understand this part. If the amount of time in the center after the loom changes for subordinate mice, how does this lead to the conclusion that they spend most of their time in the reward zone?. Figure 3A - The example shown does not seem representative of the claim that high contrast stimuli are more likely to trigger escape. In particular, the 10% sucrose condition appears to show more arena visits under low contrast than high contrast, which seems to contradict that interpretation. Also, the plot currently uses trials on the Y-axis, but it would be more informative to show one line per animal, using only the first trial for each. This would help separate initial threat responses from learning effects and clarify individual variability.
Figure 3H includes data from both low- and high-contrast conditions. We will clarify this in the figure legend.
Regarding Figure 4H, we are not entirely certain about the concern. In this panel, we measured time spent in the reward zone, not the center of the arena, during looming exposure. Subordinate mice spent significantly more time in the reward zone than dominant mice.
In Figure 3A, under high-contrast conditions, animals were more likely to escape to the nest with shorter latency and spent less time in the reward zone, which was especially evident in the 10% sucrose condition. To better separate initial threat responses from learning effects and to highlight individual variability, we will re-plot Figure 3A using only the first trials for each mouse and include this as a supplementary figure.
(8) The analysis does not explore individual variability in behavior, which could be an important source of structure in the data. Without this, it is difficult to know whether social hierarchy alone explains behavioral differences or if other stable traits (e.g., anxiety level, prior experiences) also contribute.
To attribute the observed behavioral differences specifically to social hierarchy rather than other individual traits, we will conduct paired comparisons between dominant and subordinate mice and incorporate analyses of individual variability.
(9) The study shows robust looming responses in group-housed animals, which contrasts with other studies that often require single housing to elicit reliable defensive responses. It would be valuable for the authors to discuss why their results differ in this regard and whether housing conditions might interact with social rank or habituation.
Looming exposure elicits robust defensive behaviors in both group- and single-housed mice (Yilmaz and Meister, 2013, Lenzi et al., 2022), with group-housed animals habituating more quickly to the stimulus (Lenzi et al., 2022). We will discuss this in the revised Discussion, including potential interactions between housing, rank, and habituation.
Reviewer #2 (Public review):
Zhe Li and colleagues investigate how mice exposed to visual threats and rewards balance their decisions in favour of consuming rewards or engaging in defensive actions. By varying threat intensity and reward value, they first confirm previous findings showing that defensive responses increase with threat intensity and that there is habituation to the threat stimulus. They then find that water-deprived mice have a reduced probability of escaping from low contrast visual looming stimuli when water or sucrose are offered in the environment, but that when the stimulus contrast is high, the presence of sucrose or water increases the probability of escape. By analysing behaviour metrics such as the latency to flee from the threat stimulus, they suggest that this increase in threat sensitivity is due to increased vigilance. Analysis of this behaviour as a function of social hierarchy shows that dominant mice have higher threat sensitivity, which is also interpreted as being due to increased vigilance. These results are captured by a drift diffusion model variant that incorporates threat intensity and reward value.
The main contribution of this work is to quantify how the presence of water or sucrose in water-deprived mice affects escape behaviour. The differential effects of reward between the low and high contrast conditions are intriguing, but I find the interpretation that vigilance plays a major role in this process is not supported by the data. The idea that reward value exerts some form of graded modulation of the escape response is also not supported by the data. In addition, there is very limited methodological information, which makes assessing the quality of some of the analyses difficult, and there is no quantification of the quality of the model fits.
(1) The main measure of vigilance in this work is reaction time. While reaction time can indeed be affected by vigilance, reaction times can vary as a function of many variables, and be different for the same level of vigilance. For example, a primate performing the random dot motion task exhibits differences in reaction times that can be explained entirely by the stimulus strength. Reaction time is therefore not a sound measure of vigilance, and if a goal of this work is to investigate this parameter, then it should be measured. There is some attempt at doing this for a subset of the data in Figure 3H, by looking at differences in the action of monitoring the visual field (presumably a rearing motion, though this is not described) between the first and second trials in the presence of sucrose. I find this an extremely contrived measure. What is the rationale for analysing only the difference between the first and second trials? Also, the results are only statistically significant because the first trial in the sucrose condition happens to have zero up action bouts, in contrast to all other conditions. I am afraid that the statistics are not solid here. When analysing the effects of dominance, a vigilance metric is the time spent in the reward zone. Why is this a measure of vigilance? More generally, measuring vigilance of threats in mice requires monitoring the position of the eyes, which previous work has shown is biased to the upper visual field, consistent with the threat ecology of rodents.
We agree that reaction time can be influenced by multiple factors, including stimulus strength. Consistent with this, reaction times (i.e. latencies to flee) were substantially shorter under high-contrast conditions (Figure 3E). However, even under the same high-contrast condition, reaction times were significantly shorter in the water condition compared to the no-reward condition, suggesting that other factors such as vigilance may contribute.
Upward-directed attention includes rearing, up-stretching, and upward head orientation, which will be clarified in the Method section. To address concerns about statistical validity, we will quantify these behaviors across the first 10 trials rather than limiting the analysis to the first two.
As for the dominance-related results, we interpret them as reflecting both enhanced vigilance and reduced reward-seeking behavior. Time spent in the reward zone is not a measure of vigilance but an indicator of reward-seeking motivation. We will clarify this in the revised manuscript.
(2) In both low and high contrast conditions, there are differences in escape behaviour between no reward and water or sucrose presence, but no statistically significant differences between water and sucrose (eg, Figure 3B). I therefore find that statements about reward value are not supported by the data, which only show differences between the presence or absence of reward. Furthermore, there is a confound in these experiments, because according to the methods, mice in the no-reward condition were not water deprived. It is thus possible that the differences in behaviour arise from differences in the underlying state.
In Figure 3B, the difference between water and sucrose conditions did not reach statistical significance (p = 0.08). We plan to collect additional data to determine whether this is due to limited statistical power. It is also possible that some behavioral readouts are more sensitive to the differences between water and sucrose conditions. For example, Figure 3F shows that escape speed was significantly higher in the sucrose than in the water condition under high-contrast stimulation.
Thank you for pointing this out. To control for the potential confounds related to internal state, mice were not water-deprived under any of the three conditions in Figures 3A-3H. We will clarify this in the main text and Methods. For Figures 3I-3M, which compare decision-making under no-reward and water conditions, we will conduct additional experiments using non-deprived mice in the water condition.
(3) There is very little methodological information on behavioural quantification. For example, what is hiding latency? Is this the same are reaction time? Time to reach the safe zone? What exactly is distance fled? I don't understand how this can vary between 20 and 100cm. Presumably, the 20cm flights don't reach the safe place, since the threat is roughly at the same location for each trial? How is the end of a flight determined? How is duration measured in reward zone measures, e.g., from when to when? How is fleeing onset determined?
Hiding latency was defined as the time from stimulus onset to the animal’s arrival at the safe zone. Reaction time was quantified as the latency to flee, measured from stimulus onset to the initiation of the first flight state. The flight state was defined as locomotion exceeding 10 cm at a speed greater than 10 cm/s. Distance fled was defined as the distance covered between stimulus onset and offset for all trials. However, in trials classified as no reaction or freezing, this measure does not accurately reflect escape behavior. We will therefore rename it as distance under threat to better capture its meaning. The reward zone was defined as the region within 15 cm of the reward port at the end of the arena. Duration in the reward zone was measured as the time spent within this region during the 20 seconds following stimulus onset. In Figure 4E, the percentage of time spent in the reward zone was calculated relative to the total time the mouse remained in the arena during the 2-hour social session.
All definitions and additional details on behavioral quantification will be included in the revised Methods section.
(4) There is little methodological information on how the model was fit (for example, it is surprising that in the no reward condition, the r parameter is exactly 0. What this constrained in any way), and none of the fit parameters have uncertainty measures so it is not possible to assess whether there are actually any differences in parameters that are statistically significant.
We appreciate the comment and agree that further clarification is needed. We will provide a more detailed description of the model fitting procedure in the revised Methods section. Specifically, the drift rate parameter (r), which reflects the perceived reward value, was constrained to zero in the no-reward condition. To enable statistical comparison across conditions, we will report uncertainty measures for all fit parameters.
Reviewer #3 (Public review):
Male mice were tested in a classic behavioral "flee the looming stimulus" paradigm. This is a purely behavioral study; no neural analyses were done. Mice were housed socially, but faced the looming stimulus individually. Drift-diffusion modeling found that reward-level interacted with threat level such that at low-threat levels, reward contrasted with threat as classically expected (high reward overwhelms low threat, low threat overwhelms low reward), but that reward aligned with threat at higher threat levels.
Note that they define threat level by the darkness of the looming stimulus. I am not sure that darker stimuli are more threatening to mice. But maybe. Figure 3 shows that mice react more quickly to high contrast looming stimuli, but can the authors distinguish between the ability to detect the visual signal from considering it a more dangerous threat? (The fact that vigilance makes a difference in the high contrast condition, not the low contrast condition, actually supports the author's hypotheses here.)
We thank the reviewer for raising the important point regarding the interpretation of contrast as a proxy for threat level. While increased escape probability and faster reaction times under high-contrast stimuli are consistent with prior work (Evans et al., 2018), they could reflect either improved detection or heightened threat perception.
To disentangle these possibilities, we analyzed not only latency to flee but also distance fled and peak speed, which reflect the intensity of the escape response. If contrast only affected detection, we would expect differences in latency but not in distance or speed. However, all three metrics differed significantly across contrast conditions, supporting the interpretation that high-contrast stimuli are perceived as more threatening, not merely more detectable.
To further clarify this distinction, we will manually examine no-reaction trials to determine whether mice detected the stimulus. In trials classified as “not-threatened”, mice may orient to the stimulus without fleeing; in “not-seen” cases, behavior is unrelated to stimulus onset. If the differece comes from detection, we would expect a higher number of “not-seeen” trials under low-contrast conditions. Conversely, if it reflects threat perception, the difference should be observed in the number of ‘non-threatened’ cases.
The drift-diffusion model (DDM) is fine. I note that the authors included a "leakage rate", which is not a standard DDM parameter (although I like including it). I would have liked to see more about the parameters. What were the distributions? What did the parameters correlate with behaviorally? I would have liked to see distributions of the parameters under the different conditions and different animals. Figure 2C shows the progression of learning. How do the fit parameters change over time as mice shift from choice to choice? How do the parameters change over mice? How do the parameters change over distance to the threat/distance to safety (as per Fanselow and Lester 1988)? They did a supplemental experiment where the threat arrived halfway along the corridor - we could get a lot more detail about that experiment - how did it change the modeling?
We appreciate the reviewer’s comments regarding the modeling. In the revised manuscript, we will include the distribution of model parameters across different conditions and individual animals, along with additional analyses examining how specific parameters relate to behavioral metrics. To better capture individual variability and learning effects, we also plan to fit model parameters across trials for each mouse.
Regarding the influence of distance to safety, our data indicate that it did not significantly affect defensive responses (Figure S2). One possible explanation is that, once a threat is detected, animals prioritize escape over evaluating the proximity to safety. To test this further, we plan to introduce barriers that lengthen the return path to the safe zone, allowing us to assess whether increased distance alters behavior. We will include quantitative analyses, as well as new figures and videos, to support these findings.
We did not examine the effect of distance to threat in our paradigm, as aerial predators typically descend rapidly, giving prey little opportunity to detect and react to the threat at varying distances. This contrasts with terrestrial predator contexts, such as those described in Fanselow and Lester (1988), where prey can observe an approaching threat from a distance and adjust their defensive strategies accordingly.
Overall, this is a reasonable study showing mostly unsurprising results. I think the authors could do more to connect the vigilance question to their results (which seems somewhat new to me).
Thank you for this suggestion. We will expand our analyses to better link vigilance to behavioral outputs.
Although the data appear generally fine and the modeling reasonable, the authors do not do the necessary work to set themselves within the extensive literature on decision-making in mice retreating from threats.
First of all, this is not a new paradigm; variants of this paradigm have been used since at least the 1980s. There is an ‘extensive’ literature on this, including extensive theoretical work on the relation of fear and other motivational factors. I recommend starting with the classic Fanselow and Lester 1988 paper (which they cite, but only in passing), and the reviews by Dean Mobbs and Jeansok Kim, and by Denis Paré and Greg Quirk, which have explicit theoretical proposals that the authors can compare their results to. I would also recommend that the authors look into the "active avoidance" literature. Moreover, to talk about a mouse running from a looming stimulus without addressing the other "flee the predator" tasks is to miss a huge space for understanding their results. Again, I would start with the reviews above, but also strongly urge the authors to look at the Robogator task (work by June-Seek Choi and Jeansok Kim, work by Denis Paré, and others).
We agree that integrating prior literature will strengthen the manuscript. We will revise the Introduction and Discussion to incorporate relevant theories, studies on active avoidance, and other “flee the predator” paradigms, including the Robogator task.
Similarly, in their anatomical review, they do not mention the amygdala. Given the extensive literature on the role of the amygdala in retreating from danger, both in terms of active avoidance and in terms of encoding the danger itself, it would surprise me greatly if this behavior does not involve amygdala processing. (If there is evidence that the amygdala does not play a role here, but that the superior colliculus does, then that would be a *very* important result that needs to be folded into our understanding of decision-making systems and neural computational processing.)
Thank you for highlighting this important point. We will revise the Discussion to address the roles of amygdala and superior colliculus in threat processing.
Second, there is an extensive economic literature on non-human animals in general and on rodents in particular. Again, the authors seem unaware of this work, which would provide them with important data and theories to broaden the impact of their results (by placing them within the literature). First, there are explicit economic literatures in terms of positively-valenced conflicts (e.g., neuroeconomics within the primate literature, sequential foraging and delay-discounting tasks within the rodent literature), but also there is a long history within the rodent conditioning world, such as the classic work by Len Green and Peter Shizgal. I would strongly urge the authors to explore the motivational conflict literature by people like Gavin McNally, Greg Quirk, and Mark Andermann. Again, putting their results into this literature will increase the impact of their experiment and modeling.
We appreciate this valuable recommendation. We will incorporate relevant work from the fields of neuroeconomics, including sequantial foraging and delay-discounting tasks, and studies on motivational conflict in rodents. Integrating these perspectives will help us better frame our task within broader frameworks of decision-making under conflict.
