September 2024
Volume 24, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2024
Control theoretical models for visuomotor control explains brain activity during naturalistic driving
Author Affiliations & Notes
  • Tianjiao Zhang
    UC Berkeley
  • Christopher A Strong
    UC Berkeley
  • Kaylene Stocking
    UC Berkeley
  • Jingqi Li
    UC Berkeley
  • Claire Tomlin
    UC Berkeley
  • Jack L Gallant
    UC Berkeley
  • Footnotes
    Acknowledgements  This work is supported by funding from the NEI, ONR, Ford URP, and NSF GRFP
Journal of Vision September 2024, Vol.24, 1091. doi:https://doi.org/10.1167/jov.24.10.1091
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      Tianjiao Zhang, Christopher A Strong, Kaylene Stocking, Jingqi Li, Claire Tomlin, Jack L Gallant; Control theoretical models for visuomotor control explains brain activity during naturalistic driving. Journal of Vision 2024;24(10):1091. https://doi.org/10.1167/jov.24.10.1091.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Visuomotor control is crucial for successfully navigating through the world, as we must continuously adjust our actions to account for the behavior of other agents. Multiple brain regions, including the intraparietal sulcus (IPS), motor cortex, supplementary motor areas (SMA), and the prefrontal cortex (PFC), have been implicated in visuomotor control. However, the control algorithms implemented by these regions during interactions with other agents remain poorly understood. Here, we examined whether the brain may use algorithms similar to control theoretical models for car-following. We used fMRI to record brain activity from six participants performing a taxi-driver task in a large virtual world (110-180 minutes of data per participant). Virtual traffic required participants to constantly monitor other vehicles and adjust their own actions. We implemented three control theoretical car-following algorithms: the optimal velocity model (OVM) and intelligent driver model (IDM), two reactive dynamical systems models, and a model predictive control (MPC) model, a forward predictive model. In preliminary analysis in two participants, we tuned the parameters of the three control models to match the behavior of each participant, and used these tuned control models to create features for modeling brain activity. We used banded ridge regression (Nunez-Elizalde et al., 2019, Dupré la Tour et al., 2022) to estimate voxelwise encoding models for these control models along with 34 other feature spaces for the taxi-driver task. The MPC control model better matches the participants’ behavior than IDM or OVM, and the MPC encoding model better predicts brain activity than the IDM and OVM encoding models. Well-predicted regions include parts of the PFC, IPS, SMA, and motor cortex. Encoding model weights reveal multiple timescales of predictive control in the cortex. These results suggest that the human brain may implement a forward predictive algorithm similar to MPC for optimal visuomotor control during driving.

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