有限大小目标间感知与决策的加权模型
A weighted model of perception and decision-making between targets of finite size
AI总结：
本文提出有限大小目标间感知与决策的三阶段加权模型，其对应能量最小化问题，可复现实验观测，为解码神经几何提供途径，还为模型扩展与实验研究提供方向。
AI中文摘要：
近期基于实验的研究已将视觉神经环模型与动物如何在具有吸引力目标的复杂环境中导航关联起来。本文研究了一个三阶段模型的数学及生物学意义：动物先对视觉刺激进行预处理以识别离散的目标集合，再处理该输入以选择主导目标，最后对该信息进行后处理以在环境中导航。纳入有限目标大小和神经密度后，可实现与自然界观测一致的稳定目标获取与追踪。数学上，我们证明该模型对应一个能量最小化问题，简化了其分析与数值实现；生物学上，我们认为该模型具有生理学动机，可复现实验观测结果，为通过经验数据解码神经几何提供了直接途径。我们的结果还展示了未来模型扩展的分析流程，并提出了用于测试和参数化该定量框架的具体实验研究方向。
英文摘要：
Recent research grounded in experiments has connected neural ring models of vision to how animals navigate a complex landscape of attractive targets. In this paper we investigate the mathematical and biological implications of a three-stage model where animals pre-process visual stimuli to identify a discrete set of targets, process this input to select the dominant targets, and then post-process this information to navigate the landscape. Incorporating finite target sizes and a neural density allows consistent target acquisition and pursuit reflecting what is seen in nature. Mathematically, we show this model corresponds to an energy minimization problem, simplifying both its analysis and numerical implementation. Biologically, we argue that the model is physiologically motivated, reproduces experimental observations, and presents a fairly direct pathway to decoding neural geometry via empirical data. Our results also demonstrate an analysis pipeline for future model extensions and suggest specific avenues for experimental research to test and parameterize our quantitative framework.
