Dataset and Code for the Article: Predictive Uncertainty in State-Estimation Drives Active Sensing

<p>Animals use active sensing movements to shape the spatiotemporal characteristics of sensory signals to better perceive their environment under varying conditions. However, the underlying mechanisms governing the generation of active sensing movements are not known. To address this, we investigated the role of active sensing movements in the refuge tracking behavior of <em>Eigenmannia virescens</em>, a species of weakly electric fish. These fish track the longitudinal movements of a refuge in which they hide by swimming back and forth in a single linear dimension. During refuge tracking, <em>Eigenmannia</em> exhibits stereotyped whole-body oscillations when the quality of the sensory signals degrades. We developed a closed-loop feedback control model to examine the role of these ancillary movements on the task performance. Our modeling suggests that fish may use active sensing to minimize predictive uncertainty in state estimation during refuge tracking. The proposed model generates simulated fish trajectories that are statistically indistinguishable from that of the actual fish, unlike the open-loop noise generator and stochastic resonance generator models in the literature. These findings reveal the significance of closed-loop control in active sensing behavior, offering new insights into the underlying mechanisms of dynamic sensory modulation.</p>

动物通过主动感知运动来塑造感觉信号的时空特征，以便在多变的环境条件下更好地感知周围环境。然而，调控主动感知运动产生的内在机制尚不明确。为此，本研究针对电鱼属的Eigenmannia virescens物种在避难所追踪行为中的主动感知运动作用进行了探究。这些鱼类通过在单一线性维度内往返游动，追踪其藏身的避难所的纵向运动。在追踪避难所的过程中，当感觉信号的质量下降时，Eigenmannia表现出典型的全身振荡。我们开发了一个闭环反馈控制模型，以检验这些辅助运动对任务表现的影响。我们的模型表明，鱼类可能利用主动感知来最小化在避难所追踪过程中状态估计中的预测不确定性。该模型生成的模拟鱼轨迹在统计学上与实际鱼轨迹无法区分，与文献中的开环噪声发生器和随机共振发生器模型不同。这些发现揭示了闭环控制在主动感知行为中的重要性，为动态感觉调节的内在机制提供了新的见解。