Economic and social modulations of innate decision-making in mice exposed to visual threats
- Academy for Advanced Interdisciplinary Studies, Peking University, China
- Beijing Institute for Brain Research, Chinese Academy of Medical Sciences and Peking Union Medical College, China
- Chinese Institute for Brain Research, China
- Department of Neurobiology, School of Basic Medical Sciences, Capital Medical University, China
Figures
Figure 1 with 2 supplements
A behavioral paradigm for investigating innate decision-making in mice.
(A) Schematic of the behavioral assay (3D and top-down views). (B) Top: arena occupancy patterns for five example mice in one session. Bottom: visit frequency and average duration for each visit. Different colors mark the mouse’s identity. (C) Visit frequency and duration under exploration and threat conditions. Error bars represent standard deviation. n = 46 mice; paired -test. (D) Pipeline for behavioral classification. (E) Distribution of decisions across 3862 trials from 140 mice. (F) Left: distance to the safe zone over time for four decision types (10 example trials each). Red dashed lines mark the onset of each stimulus repetition; solid lines mark the end of the last repetition. Gray shading indicates the safe zone. Right: locomotion speed toward the safe zone over time for the same trials. Positive speed indicates movement toward the safe zone. (G) Distribution of the longest stationary time for ‘freezing’ and ‘assessment + escape’. n = 224 and 1667 trials, Mann–Whitney test. Blue lines mark the median; red solid lines mark the end of the last repetition. (H) Distribution of latency to flee for ‘direct escape’ and ‘assessment + escape’. n = 458 and 1667 trials, Mann–Whitney test. Blue lines mark the median; red dashed and solid lines mark the end of the first and last repetitions, respectively. (I) Distribution of peak speed for ‘direct escape’ and ‘assessment + escape’. n = 458 and 1667 trials, Mann–Whitney test. (J) Distribution of hiding latency in the safe zone for ‘direct escape’ and ‘assessment + escape’. n = 455 and 1563, Mann–Whitney test. (K) Distribution of post-decision behavioral states. Numbers indicate the trial counts in which mice recovered from the fear state within 20 s after stimulus onset. (L) Distribution of fear recovery time for ‘direct escape’, ‘assessment + escape’, and ‘freezing’. n = 74, 458, and 205, Kruskal–Wallis test followed by Dunn’s post hoc test with Holm correction. For all panels: *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 1—figure supplement 1
Model validation and feature analysis for automated behavioral classification.
(A) Tracking accuracy of the mouse nose and tail base using DeepLabCut. (B) Feature weights in the random forest classifier. (C) Model performance evaluated by a confusion matrix on the test dataset.
Figure 1—figure supplement 2
Effects of threat position, prey–threat distance, and prey–safety distance on defensive responses to looming stimuli.
(A) Schematic of the experiment in which looming stimuli were presented either in front of or behind the mice during foraging. (B) Distributions of escape directions for front and rear stimulus positions. n = 97, 16 trials. (C) Schematic of the experiment in which looming stimuli were presented at the end of the linear arena with varying distances between the mouse and the threat. (D) Distance to the safe zone over time for four prey–threat distances. Red dashed lines mark the onset of each stimulus repetition; solid lines mark the end of the last repetition. Gray shading indicates the safe zone. Blue lines mark the mouse position at stimulus onset; orange lines mark the stimulus location. n = 7 (15 cm), 7 (35 cm), 9 (55 cm), and 8 (75 cm) trials from 5 mice. (E) Distribution of decisions across prey–threat distances. (F, G) Latency to flee and peak escape speed across prey–threat distances. Kruskal–Wallis test with post hoc Dunn’s test (Holm correction). (H) Schematic of the experiment in which looming stimuli were presented in front of the mouse at varying distances between the mouse and the safe zone. (I) Distribution of decisions across prey–safety distances. n = 4, 4 trials. (J) Schematic of the experiment in which low-contrast looming stimuli were presented at varying distances between the mouse and the safe zone in the linear arena with barriers. (K) Distribution of decisions across prey–safety distances in the barrier condition. n = 8, 7 trials. *p < 0.05, **p < 0.01.
Figure 2 with 1 supplement
Rapid learning shapes economic decisions under threat.
(A) Schematic of the behavioral assay for studying the economic modulation of innate decision-making. (B, C) Distance to the safe zone and locomotion speed over time for the first and tenth trials in response to low- and high-contrast looming stimuli across different reward conditions. The right inset in (B) shows the distribution of distance to the safe zone at the end of each trial. Dashed lines mark the start of each stimulus, and solid lines mark the stimulus offset. n = 11 (no reward, low), 13 (water, low), 10 (sucrose, low), 10 (no reward, high), 8 (water, high), and 10 (sucrose, high) mice. (D) Summary of the decisions across the first 10 trials under different threat and reward conditions. n = 11, 13, 10, 10, 8, and 10 mice. (E–H) Escape distance under threat, duration in the reward zone, peak escape speed, and latency to flee across trials in all conditions. Shading denotes the standard error of the mean. (I) Transition trials marking the shift between phases across conditions. Red lines indicate the median; boxes span the interquartile range (IQR); whiskers extend to 1.5 × IQR beyond the box.
Figure 2—figure supplement 1
Control analyses of consummatory behavior, looming detection, and behavioral dimensionality.
(A) Water and sucrose consumption during exploration and looming experiments under low- and high-contrast conditions. n = 10, 10, 5, 5, 5, and 5 sessions; Mann–Whitney test. Boxes represent the interquartile range (IQR), and whiskers show the full data range. (B) Pie chart showing the proportion of looming stimuli detected by mice in no-response trials under low- and high-threat conditions. Low contrast: n = 117 trials from 29 mice; high contrast: n = 2 trials from 2 mice. (C) Proportion of behavioral variance explained by principal component 1 (PC1) for individual mice across all conditions. n = 11, 13, 10, 10, 8, and 10 mice. (D) Cumulative PC1 score for an example mouse in each condition. Red dashed lines mark the transition from the early to the late phase. *p < 0.05, **p < 0.01.
Figure 3 with 3 supplements
Mice make economic decisions modulated by vigilance.
(A) Distribution of decisions in the early phase under six experimental conditions. n = 43 trials from 11 mice (no reward, low), 42 trials from 13 mice (water, low), 29 trials from 10 mice (sucrose, low), 31 trials from 10 mice (no reward, high), 21 trials from 8 mice (water, high), and 32 trials from 10 mice (sucrose, high); chi-squared test. (B–G) Escape distance under threat, duration in the reward zone, latency to flee, foraging interval, foraging speed, and peak escape speed in the early phase across all conditions. n = 43, 42, 29, 31, 21, and 32 trials for B, C, F, G; n = 35, 37, 24, 31, 21, and 32 trials for D; n = 116, 84, 54, 58, 36, and 48 intervals for E; Scheirer–Ray–Hare test with post hoc Dunn’s test (Holm correction). (H) Distribution of decisions in the late phase under six experimental conditions. n = 59 trials from 11 mice (no reward, low), 88 trials from 13 mice (water, low), 71 trials from 10 mice (sucrose, low), 67 trials from 10 mice (no reward, high), 59 trials from 8 mice (water, high), and 48 trials from 9 mice (sucrose, high); chi-squared test. (I–N) Escape distance under threat, duration in the reward zone, latency to flee, foraging interval, foraging speed, and peak escape speed in the late phase across all conditions. n = 59, 88, 71, 67, 59, and 48 trials for I, J, M, N; n = 43, 49, 15, 66, 59, and 47 trials for K; n = 116, 182, 114, 137, 71, and 57 intervals for L; Scheirer–Ray–Hare test with post hoc Dunn’s test (Holm correction). For all panels: *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 3—figure supplement 1
Distribution of foraging speed.
(A) Distribution of foraging speed as a function of distance from the safe zone in the early phase across different threat and reward conditions. (B) Distribution of foraging speed in the late phase. Data are from 11 mice (no reward, low contrast), 13 mice (water, low contrast), 10 mice (sucrose, low contrast), 10 mice (no reward, high contrast), 8 mice (water, high contrast), and 10 mice (sucrose, high contrast).
Figure 3—figure supplement 2
Behavioral responses to looming stimuli across threat and reward conditions in the first trial.
(A) Distribution of decisions under six conditions. n = 11 (no reward, low contrast), 13 (water, low contrast), 10 (sucrose, low contrast), 10 (no reward, high contrast), 8 (water, high contrast), and 10 (sucrose, high contrast) mice; chi-squared test. (B–F) Escape distance under threat, duration in the reward zone, latency to flee, foraging speed, and peak escape speed across conditions. n = 11, 13, 10, 10, 8, and 10 mice for B, C, E, F; n = 11, 11, 10, 10, 8, and 10 mice for D; Scheirer–Ray–Hare test with post hoc Dunn’s test (Holm correction). *p < 0.05, ***p < 0.001.
Figure 3—figure supplement 3
Reward modulation of defensive behavior to looming stimuli within the same mouse.
(A) Experimental timeline illustrating how water reward influences innate decision-making within the same animal. (B) Distance to the safe zone and locomotion speed toward the safe zone across trials in response to low-contrast looming stimuli with and without water reward in two example mice. Left: no-reward condition followed by water-reward condition; right: water-reward condition followed by no-reward condition. (C) Decision patterns of nine mice across two sessions with five trials for each. Gray squares indicate trials where the mouse did not enter the arena within 30 min. (D) Defensive probability in no-reward and water-reward conditions. n = 9 mice; paired -test. (E) Escape distance under threat, duration in the reward zone, latency to flee, and peak escape speed in no-reward and water-reward conditions. n = 5 mice for latency to flee and 9 mice for other measures; paired -test. #p < 0.1, *p < 0.05, **p < 0.01.
Figure 4 with 2 supplements
Influence of social hierarchy on innate decision-making.
(A) Schematic of the behavioral assay for studying the social modulation of innate decision-making. Top, experimental timeline. Each session lasted 2 hr per mouse pair during the pre-threat, threat, and post-threat phases. Bottom, schematic of the tube test. (B) Arena occupancy for an example pair of mice during the three sessions. (C) Visit frequency and total visit duration for dominant and subordinate mice during the pre-threat, threat, and post-threat sessions. n = 5 pairs (pre), 5 pairs (threat), and 4 pairs (post), respectively; paired -test. (D) Proportion of nest-return trials in escape trials for dominant and subordinate mice. n = 9 pairs; paired -test. (E) Distance to the safe zone over 7 days for an example pair. Looming stimuli were presented on days 4 and 5. (F) Percentage of time spent in the reward zone across days. Error bars represent SEM. n = 9 pairs; paired -test. (G) Distance to the safe zone (left) and locomotion speed (right) for an example pair. (H) Behavioral decisions across the first 10 trials for 9 mouse pairs. (I) Pie charts showing decision distributions for dominant and subordinate mice. n = 90, 90 trials; Stuart–Maxwell test. (J–O) Latency to flee, foraging interval, foraging speed, escape distance under threat, peak escape speed, and duration in the reward zone for dominant and subordinate mice. n = 78 trials for J; n = 75 trials for K, 53 for L, and 90 for M–O; paired -test. For all panels: *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 4—figure supplement 1
Behavioral responses to looming stimuli for dominant and subordinate mice in different phases.
(A) Transition trial marking the start of the late phase for dominant and subordinate mice. n = 9 pairs. (B) Behavioral decisions for dominant and subordinate mice during their first threat exposure. n = 9 pairs; Stuart–Maxwell test. (C) Latency to flee, foraging speed, escape distance under threat, peak escape speed, and duration in the reward zone during the first threat exposure. n = 4 pairs for foraging speed; n = 9 pairs for other features; paired -test. (D) Behavioral decisions for dominant and subordinate mice in the early phase. n = 34 and 43 trials, respectively; chi-squared test. (E) Violin plots showing latency to flee, average foraging interval, foraging speed, escape distance under threat, peak escape speed, and duration in the reward zone for dominant and subordinate mice in the early phase. n = 34 and 43 trials, respectively; Mann–Whitney test. (F) Behavioral decisions for dominant and subordinate mice in the late phase. n=56 and 47 trials, respectively; chi-squared test. (G) Violin plots showing latency to flee, average foraging interval, foraging speed, escape distance under threat, peak escape speed, and duration in the reward zone for dominant and subordinate mice in the late phase. n = 56 and 35 trials for latency to flee; n=56 and 47 trials for other measures. Mann–Whitney test. For all panels: *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 4—figure supplement 2
Comparison of behavioral responses to looming stimuli before and after the tube test.
(A) Schematic timeline of the looming experiments before (first threat exposure) and after (second threat exposure) the tube test. (B) Behavioral decisions for dominant and subordinate mice during the first and second threat exposures. Dominant: n = 20 (first) and 20 (second) trials from 4 mice; subordinate: n = 20 (first) and 20 (second) trials from 4 mice; chi-squared test. (C) Violin plots showing latency to flee, escape distance under threat, peak escape speed, and duration in the reward zone for dominant (top) and subordinate (bottom) mice during the first and second threat exposures. n as in (B); Mann–Whitney test.
Figure 5 with 1 supplement
Drift-diffusion leaky integrator model for escape decisions.
(A–F) Simulated accumulation of escape evidence, along with predicted latencies to flee and decision distributions across six threat and reward conditions. Green dashed horizontal lines mark ; blue dashed horizontal lines mark ; red dashed vertical lines mark the onset of each looming stimulus; blue vertical lines mark the median. (G) Heatmap of decision scores as a function of threat gain and reward value. White and red stars indicate fitted parameters for the early and late phases of the reward–threat paradigm, respectively; upward and downward pink triangles indicate fitted parameters for dominant and subordinate mice in the social-threat paradigm, respectively. (H) Heatmap of latencies to flee as a function of threat gain and reward value. (I) Vigilance as a function of input strength, illustrating how the indirect effect of reward on defensive decisions via vigilance depends on the baseline vigilance level. (J) Schematic showing how threat intensity, reward value, and vigilance jointly determine defensive decisions. Color saturation indicates defense likelihood.
Figure 5—figure supplement 1
Fitting loss in the drift-diffusion leaky integrator model.
(A) Temporal profile of the normalized looming stimulus diameter. (B) Loss landscapes for estimating the leakage rate, high-threat gain, diffusion rate, and decision threshold during the first-stage fitting. Red stars indicate the optimal parameter estimates based on experimental data in the late phase of the reward-related paradigm under the no-reward condition. (C) Loss landscapes for estimating the reward value and high-threat gain under the water-reward condition. Red stars mark the optimal fits to experimental data in the late phase of the reward–threat paradigm. (D) Same as (C), for the sucrose-reward condition. (E) Model-predicted distribution of decisions across threat gain and reward values. (F) Predicted latency to flee as a function of threat gain for varying reward values. (G) Predicted latency to flee as a function of reward value for varying threat gain. (H, I) Escape decisions and latencies to flee across threat gain and reward value predicted by a simplified deterministic model. (J, K) Same as (F, G), but for the simplified deterministic model.
Videos
Video 1
Example video of a direct escape in response to looming stimuli.
Video 2
Example video of an escape after assessment decision in response to looming stimuli.
Video 3
Example video of a freezing decision in response to looming stimuli.
Video 4
Example video of a no response decision in response to looming stimuli.
Video 5
Example video showing defensive responses to looming stimuli presented behind the mouse.
Video 6
Example video showing defensive responses at a prey–threat distance of 35 cm.
Video 7
Example video showing defensive responses at a prey–threat distance of 55 cm.
Video 8
Example video showing defensive responses at a prey–threat distance of 75 cm.
Video 9
Example video showing defensive responses to looming stimuli in the linear arena with barriers.
Tables
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Strain, strain background (M. musculus, male) | C57BL/6J | Chinese Institute for Brain Research, Beijing | RRID:IMSR_JAX:000664 | |
| Chemical compound, drug | Sucrose | Sigma-Aldrich | Cat#:V900116 | |
| Software, algorithm | Python v3.9 | Python Software Foundation | RRID:SCR_008394 | |
| Software, algorithm | R v4.2.1 | R Project for Statistical Computing | RRID:SCR_001905 |
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Economic and social modulations of innate decision-making in mice exposed to visual threats
eLife 14:RP107306.
https://doi.org/10.7554/eLife.107306.5
