Influence of sensory modality and control dynamics on human path integration

  1. Akis Stavropoulos
  2. Kaushik J Lakshminarasimhan
  3. Jean Laurens
  4. Xaq Pitkow
  5. Dora E Angelaki

  • Curated by eLife

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    Summary: In this manuscript, the authors investigated the importance of visual and vestibular sensory cues and the underlying motion dynamics to the accuracy of spatial navigation by human subjects. A virtual environment coupled with a 6-degrees of motion platform, as described in prior studies, allowed precise control over sensory cues and motion dynamics. To investigate whether control dynamics influence performance, the transfer function between joystick deflection and self-motion velocity was modified at each trial, resulting in subjects relying more on velocity or acceleration to find their way. To explain the main result that navigation error depends on control dynamics, the authors propose a probabilistic model in which an internal estimate of dynamics is biased by a strong prior. Overall, the three reviewers agree that additional data are not necessary. However, the analyses need to be clarified and the conclusion better justified.

    Reviewer #1, Reviewer #2 and Reviewer #3 opted to reveal their name to the authors in the decision letter after review.

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Abstract

Path integration is a sensorimotor computation that can be used to infer latent dynamical states by integrating self-motion cues. We studied the influence of sensory observation (visual/vestibular) and latent control dynamics (velocity/acceleration) on human path integration using a novel motion-cueing algorithm. Sensory modality and control dynamics were both varied randomly across trials, as participants controlled a joystick to steer to a memorized target location in virtual reality. Visual and vestibular steering cues allowed comparable accuracies only when participants controlled their acceleration, suggesting that vestibular signals, on their own, fail to support accurate path integration in the absence of sustained acceleration. Nevertheless, performance in all conditions reflected a failure to fully adapt to changes in the underlying control dynamics, a result that was well explained by a bias in the dynamics estimation. This work demonstrates how an incorrect internal model of control dynamics affects navigation in volatile environments in spite of continuous sensory feedback.

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  1. Version published to 10.7554/elife.63405 on eLife
  2. eLife

    Reviewer #3:

    The manuscript describes interesting experimental and modelling results of a novel study of human navigation in virtual space, where participants had to move towards a briefly flashed target using optic flow and/or vestibular cues to infer their trajectory via path integration. To investigate whether control dynamics influence performance, the transfer function between joystick deflection and self-motion velocity was modified trial-by-trial in a clever way. To explain the main result that navigation error depends on control dynamics, the authors propose a probabilistic model in which an internal estimate of dynamics is biased by a strong prior. Even though the paper is clearly written and contains most of the necessary information, the study has several shortcomings, as outlined below, and an important alternative hypothesis has not been considered, so that some of the conclusions are not fully supported by results and modelling.

    Substantive concerns

    1. The main idea of the paper for explaining the influence of control dynamics is that for accurate path integration performance participants have to estimate dynamics. This idea is apparently inspired by studies on limb motor control. However, tasks in these studies are often ballistic, because durations are short compared to feedback delays. In navigation, this is not the case and participants can therefore rely on feedback control (for another reason, why reliance on sensory feedback in the present study is a good idea, see point 2 below). This means that the task can be solved, even though not perfectly, without actually knowing the control dynamics. Thus, an alternative hypothesis for explaining the results that has not been considered is that the error dependence of control dynamics is a direct consequence of feedback control. Feedback control models have previously been suggested for goal-directed path integration (e.g., Grasso et al. 1999; Glasauer et al. 2007).

    To test this assumption, I modelled the experiment assuming a simple bang-bang feedback control that switches at a predefined and constant perceived distance from the target from +1 to -1 and stops when perceived velocity is smaller than an epsilon. Sensory feedback is perceived position, which is assumed to be computed via integration of optic flow. This model predicts a response gain of unity, a strong dependence of error on time constant (slope similar to Fig. 3) or of response gain on time constant (Eqn. 4.1) with regression coefficients of 0.8 and 0.05 (cf. Fig. 3D), and a modest correlation between movement duration and time constant (r approximately 0.2, similar to Fig. 3A). Thus, a feedback model uninformed about actual motion dynamics and without any attempt to estimate them can explain most features of the data. Modifications (velocity uncertainty, delayed perception, noise on the stopping criterion, etc.) do not change the main features of the simulation results.

    Accordingly, since simple feedback control seems to be an alternative to estimating control dynamics in this experiment, the authors' conclusion in the abstract "that people need an accurate internal model of control dynamics when navigating in volatile environments" is not supported by the current results.

    1. Modelling: the main rationale of the model (line 173 ff: "From a normative standpoint, ...") is correct, but an accurate estimate of the dynamics is only required if the uncertainty of the velocity estimate based on the efference copy is not too large. Otherwise, velocity estimation should rely predominantly on sensory input. In my opinion that's what happens here: due to the trial-by-trial variation in dynamics, estimates based on efference copy are very unreliable (the same command generates a different sensory feedback in each trial), and participants resort to sensory input for velocity estimation. This results in feedback control, which, as mentioned above, seems to be compatible with the results.

    2. Motion cueing: Motion cueing can, in the best case, approximate the vestibular cues that would be present during real motion. Furthermore, it is not clear whether the applied tilt is really perceived as linear acceleration, or whether the induced semicircular canal stimulus is too strong so that subjects experience tilt. Participants might have used the tilt as indicator for onset or offset of translational motion, specifically because it is self-generated, but the contribution of the vestibular cues found in the present experiment might be completely different from what would happen during real movement. Therefore, conclusions about vestibular contributions are not warranted here and cannot solve the questions around "conflicting findings" mentioned in the introduction.

    3. Methods: I was not able to find an important piece of information: how many trials were performed in each condition? Without this information, the statistical results are incomplete. It was also not possible to compute the maximal velocity allowed by joystick control, since for Eqn. 1.9 not just the displacement x and the time constant is required, but also the trial duration T, which is not reported. One can only guess from Fig. 1D that vmax is about 50 cm/s for tau=0.6 s and therefore the average T is assumed to be around 8.5 s.

    4. Results: information that would be useful is not reported. On page 6 it is mentioned that the "effect of control dynamics must be due to either differences in travel duration or velocity profiles", it is then stated that both are "unlikely", but no results are given. It turns out that in the supplementary Figure 4A the correlation between time constant and duration/velocity is shown, and apparently the correlation with duration is significant (but small) in the majority of cases. Why is that not discussed in the results section? Other results are also not reported, for example, what was the slope of the dependence between time constant and error? Why is the actual control signal, the joystick command, not shown and analyzed?

  3. eLife

    Reviewer #2:

    The authors asked how the brain uses different sensory signals to estimate self-motion for path integration in the presence of different movement dynamics. They used a new paradigm to show that path integration based on vision was mostly accurate, but vestibular signals alone led to systematic errors particularly for velocity-based control.

    While I really like the general idea and approach, the conclusions of this study hinge on a number of assumptions for which it would be helpful if the authors could provide better justifications. I also have some clarification questions for certain parts of the manuscript.

    1. Lines 26-7: "performance in all conditions was highly sensitive to the underlying control dynamics". This is hard to really appreciate from the residual error regressions in Fig 3 and seems to be contradicting Fig 5A (for vestibular condition). A more explicit demonstration of how tau affects performance would be helpful.

    2. One of the main potential caveats I see in the study design is the fact that trial types (vest, visual, combined) were randomly interleaved. In the combined condition, this could potentially result in a form of calibration of the vestibular signal and/or a better estimate of tau that then is used for a subsequent vestibular-only trial. As such, you'd expect a history effect based on trial type more so (or in addition to) simple sequence effects. This is particularly true since you have a random walk design for across-trial changes of tau. In other words, my question is whether in the vestibular condition participants simply use their previous estimate of tau, since that would be on average close enough to the real tau?

    3. I thought the experimental design was very clever, but I was missing some crucial information regarding the design choices and their consequences. First, has there been a psychophysical validation of GIA vs pure inertial acceleration? Second, were GIAs always well above the vestibular motion detection threshold? In other words could the worse performance in the vestibular condition be simply related to signal detection limitations? Third, how often did the motion platform enter the platform motion range limit regime (non-linear portion of sigmoid)?

    4. Lines 331-345: it's unclear to me why you did not propose a more normative framework as outlined here. Especially, a model that would "constrain the hypothesized brain computation and their neurophysiological correlates" would be highly desirable and really strengthen the future impact of this study.

    5. I would highly recommend all data to be made available online in the same way as the analysis code has been made available.

  4. eLife

    Reviewer #1:

    The authors investigated the importance of visual and vestibular sensory cues and the underlying motion dynamics to the accuracy of spatial navigation by human subjects. A virtual environment coupled with a 6-degrees of motion platform, as described in prior studies, allowed precise control over sensory cues and motion dynamics. The research builds on previous work in several important ways: 1) the authors demonstrate that reliance on vestibular cues leads to an undershooting of trajectories to hidden goal locations, 2) manipulation of the underlying motion dynamics (the time constant) during navigation alters the accuracy of trajectories particularly when subjects are reliant on vestibular cues, 3) probabilistic models were used to demonstrate that path integration errors can be explained by mis-estimates of the underlying motion time constants, and 4) time constant estimates were improved when visual cues were available. Overall, the analyses are appropriate, the conclusions are judicious, and the authors provide an important contribution to understanding the sensory mechanisms underlying human spatial navigation.

    1. Some minor methodological clarifications: how many trials were performed per subject? How many of the trials were performed in each condition (visual, vestibular, combined)?

    2. The study tested performance by both male and female subjects. Could the authors comment as to whether sex differences were observed across performance measures? Perhaps sex can be indicated in some of the scatter plots.

    3. Figure 2A. It would be helpful if the authors identified the start-point of the trajectory and also provided more explanation of the schematic in the caption.

    4. Figure 2B-C. It would be helpful if the authors could expand this section to show some example trajectories and the relationship between examples and plotted data points. This could be done by presenting measures (radial distance, angular eccentricity, grain) for each example trajectory.

    5. Because the range of sampled time-constants can vary across subjects, it would be nice to show plots as in Figure 3B for each subject (i.e., in supplementary material).

    6. Discussion. The broader implications of the findings from the models are not sufficiently discussed. In addition, some comparison could also be made to other recent efforts to model path integration error (e.g., PMC7250899).

  5. eLife

    Summary: In this manuscript, the authors investigated the importance of visual and vestibular sensory cues and the underlying motion dynamics to the accuracy of spatial navigation by human subjects. A virtual environment coupled with a 6-degrees of motion platform, as described in prior studies, allowed precise control over sensory cues and motion dynamics. To investigate whether control dynamics influence performance, the transfer function between joystick deflection and self-motion velocity was modified at each trial, resulting in subjects relying more on velocity or acceleration to find their way. To explain the main result that navigation error depends on control dynamics, the authors propose a probabilistic model in which an internal estimate of dynamics is biased by a strong prior. Overall, the three reviewers agree that additional data are not necessary. However, the analyses need to be clarified and the conclusion better justified.

    Reviewer #1, Reviewer #2 and Reviewer #3 opted to reveal their name to the authors in the decision letter after review.

  6. Version published to 10.1101/2020.09.21.307256 on bioRxiv