Ants integrate proprioception as well as visual context and efference copies to make robust predictions

Abstract

Forward models are mechanisms enabling an agent to predict the sensory outcomes of its actions. They can be implemented through efference copies: copies of motor signals inhibiting the expected sensory stimulation, literally canceling the perceptual outcome of the predicted action. In insects, efference copies are known to modulate optic flow detection for flight control in flies. Here we investigate whether forward models account for the detection of optic flow in walking ants, and how the latter is integrated for locomotion control. We mounted Cataglyphis velox ants in a virtual reality setup and manipulated the relationship between the ants' movements and the optic flow perceived. Our results show that ants compute predictions of the optic flow expected according to their own movements. However, the prediction is not solely based on efference copies, but involves proprioceptive feedbacks and is fine-tuned by the panorama's visual structure. Mismatches between prediction and perception are computed for each eye, and error signals are integrated to adjust locomotion through the modulation of internal oscillators. Our work reveals that insects' forward models are non-trivial and compute predictions based on multimodal information.

Fig. 1. Ants with a gain of 0 behave similarly than when they are with a negative gain.

a Evolution of the angular velocity signal and the associated trajectories for an individual ant across gain −1 (inverted visual feedback), gain 0 (still image), gain 1 (natural visual feedback) and when surrounded by black. Signals show 40 s extracted from each condition. Lighter curves correspond to the raw signal while the darker ones correspond to the smoothed signal. Trajectories shown correspond to the gray boxes on the signal and last 12 s. The blue parts correspond to right turns whereas the green parts correspond to left turns. b, c Mean ± SEM of the individuals’ average absolute angular velocity (b) and proportion of ants’ oscillations blocked above or under 0 deg/s (without switching direction) (c) of ants in gains −1, 0, 1, 3, 5 and surrounded by black. b top-right inset: mean oscillation cycles of ants in gain 0 (red curve) or surrounded by black (black curve). Data based on 22 ants tested in the VR with both eyes uncovered. P-values were obtained using Wald chi-square tests with LMMs (b) or GLMMs (c), see statistical analysis section for more information. Pairs of groups not sharing a letter account for a significant difference in pairwise comparisons using the sequential Bonferroni correction after Holm; see the Source data file for the exact P-values. Each point corresponds to the response of an individual ant while the lines connect the responses of the same ant across the different conditions. Source data are provided as a Source Data file.

Fig. 2. Ants turning faster get more blocked turning in a same direction when exposed to a fixed panorama.

Proportion of blocked oscillations of ants in relation to their mean absolute angular velocity and the visual context. Pooled data based on: 22 ants from the gain alteration series with both eyes uncovered (series 1), 24 ants from the visual structure alteration series (series 2) and 24 ants from the weight of the ball alteration series (series 3). P-values were obtained using Wald chi-square tests with GLMM, see statistical analysis section for more information. There is a positive significant correlation only when ants are exposed to a static panorama (GLMM for proportional data: ${\chi }_1^2$ = 24.5, P < 0.001 red curve corresponds to the regression line) but not when ants are surrounded by black (GLMM for proportional data: ${\chi }_1^2$ = 2.28, P = 0.131). Source data are provided as a Source Data file.

Fig. 3. The visual structure surrounding ants impacts their oscillatory behavior.

a, b Mean ± SEM of the individuals' average absolute angular velocity (a) and proportion of oscillations blocked (see Supplementary Fig. 2) (b) of ants exposed to different static visual structures (gain 0) as well as a panorama in gain 2.5. Data based on n = 24 ants tested in the VR. Each point corresponds to the response of an individual ant while the lines connect the responses of the same ant across the different conditions. P-values were obtained using Wald chi-square tests with LMMs (a) or GLMMs (b), see statistical analysis section for more information. Pairs of groups not sharing a letter account for a significant difference in pairwise comparisons using the sequential Bonferroni correction after Holm; see the Source data file for the exact P-values. c Reconstructed trajectories of an ant tested in the different visual conditions. Source data are provided as a Source Data file.

Fig. 4. Ants walking on a lighter ball turn faster and get more blocked turning in one direction.

Mean ± SEM of the individuals’ average absolute angular velocity (a) and the proportion of ants’ oscillations blocked (b) of ants walking on a heavier or lighter polystyrene ball depending on the visual context (exposed to a fixed panorama or to homogeneous black). Data based on 24 ants tested in the VR with n = 21 tested with the lighter ball and n = 19 tested with the heavier ball. Each point corresponds to the response of an individual ant while the lines connect the responses of the same ant across the different conditions. P-values were obtained using Wald chi-square tests with LMMs (a) or GLMMs (b); see statistical analysis section for more information. Pairwise comparison testing between the heavier and the lighter ball inside both visual context conditions was carried out only for the proportion of blocked oscillations since the interaction between both factors was not significant for the average absolute angular speed. Because the contrasts used here are orthogonal no correction was used. Source data are provided as a Source Data file.

Fig. 5. Visuomotor control of ant’s oscillations.

The prediction error is computed as the difference between the optic flow perceived and the one predicted. The perceived optic flow in the VR is a direct result of the ant’s movement multiplied by the gain we set up for our experiment. The prediction error then impacts the oscillations by increasing the current turn if the perceived optic flow is lower than expected, and inhibiting it if the opposite is true. a Reactive model. The predicted optic flow is computed using both a motor efference copy and proprioceptive feedback. A local loop, independent of vision, regulates the strength of the motor commands according to the proprioceptive feedback. b Calibration model. The predicted optic flow is solely based on a motor efference copy but the gain of this efference copy is calibrated by a proprioceptive forward model minimizing the proprioception prediction errors. In addition, the proprioception prediction errors also calibrate the gain of the motor command to keep the initially desired turn amplitude.

Fig. 6. Covering one of the ants’ eyes decreases the effect of gain modulation compared to when they are in the dark.

Impact of the number of eyes uncovered on the average absolute angular velocity (a) and the proportion of oscillation blocked above or under 0 deg/s (not changing direction) (b) of ants depending on the gain in closed-loop. Bar plots show the mean ± SEM. Each point corresponds to the responses of an individual ant while the lines connect the responses of the same ant across the different conditions. P-values were obtained using Wald chi-square tests with LMMs (a) or GLMMs (b), because the contrasts used here for multiple comparisons are orthogonal no correction was used; see statistical analysis section for more information. Data based on 27 ants tested in the VR including n = 25 with one eye covered and n = 22 with both eyes uncovered. c Mean oscillation cycles of ants with both eyes uncovered or with one eye covered in gain 0, gain 5 and surrounded by black. Curves with lighter shades correspond to the average oscillations of individual ants, while the dark ones are averaged at the population level. Source data are provided as a Source Data file.

Fig. 7. Virtual reality experimental setup.

a Pictures of the virtual reality arena and the trackball. b Operating mode of the virtual reality (c) Placement of the paint on ants before getting tested in the virtual reality.