We offer philosophical motivations for a method we call Virtual World
Cognitive Science (VW CogSci), in which researchers use virtual embodied agents
that are embedded in virtual worlds to explore questions in the field of
Cognitive Science. We focus on questions about mental and linguistic
representation and the ways that such computational modeling can add rigor to
philosophical thought experiments, as well as the terminology used in the
scientific study of such representations. We find that this method forces
researchers to take a god's-eye view when describing dynamical relationships
between entities in minds and entities in an environment in a way that
eliminates the need for problematic talk of belief and concept types, such as
the belief that cats are silly, and the concept CAT, while preserving belief
and concept tokens in individual cognizers' minds. We conclude with some
further key advantages of VW CogSci for the scientific study of mental and
linguistic representation and for Cognitive Science more broadly.

我们提供一种我们称之为虚拟世界认知科学（VW CogSci）的方法的哲学动机，在这种方法中，研究人员使用嵌入虚拟世界中的虚拟具身代理来探索认知科学领域的问题。我们着重研究有关心理和语言表示以及这种计算建模如何为哲学思想实验增加严谨性的问题，以及在科学研究中所使用的术语。我们发现这种方法迫使研究人员以上帝的视角描述心智与环境中实体之间的动态关系，从而消除了有关信念和概念类型的问题性讨论，如猫是愚蠢的这种信念和 CAT 这个概念，同时保留了个体认知者心智中的信念和概念实例。最后，我们总结了 VW CogSci 在对心智和语言表示以及认知科学的科学研究方面的一些其他关键优势。

为更严谨的心理哲学和科学进行计算思维实验

Computational Thought Experiments for a More Rigorous Philosophy and  Science of the Mind

In motor neuroscience, artificial recurrent neural networks models often
complement animal studies. However, most modeling efforts are limited to
data-fitting, and the few that examine virtual embodied agents in a
reinforcement learning context, do not draw direct comparisons to their
biological counterparts. Our study addressing this gap, by uncovering
structured neural activity of a virtual robot performing legged locomotion that
directly support experimental findings of primate walking and cycling. We find
that embodied agents trained to walk exhibit smooth dynamics that avoid
tangling -- or opposing neural trajectories in neighboring neural space -- a
core principle in computational neuroscience. Specifically, across a wide suite
of gaits, the agent displays neural trajectories in the recurrent layers are
less tangled than those in the input-driven actuation layers. To better
interpret the neural separation of these elliptical-shaped trajectories, we
identify speed axes that maximizes variance of mean activity across different
forward, lateral, and rotational speed conditions.

通过研究虚拟机器人在进行腿部运动时的结构化神经活动，我们发现机器人训练后的神经轨迹比输入驱动的激励层的神经轨迹更为复杂，这一核心原则对于计算神经科学具有重要意义。