Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
University of Rome "La Sapienza", Rome, Italy.
PLoS Comput Biol. 2023 Jan 6;19(1):e1010829. doi: 10.1371/journal.pcbi.1010829. eCollection 2023 Jan.
When faced with navigating back somewhere we have been before we might either retrace our steps or seek a shorter path. Both choices have costs. Here, we ask whether it is possible to characterize formally the choice of navigational plans as a bounded rational process that trades off the quality of the plan (e.g., its length) and the cognitive cost required to find and implement it. We analyze the navigation strategies of two groups of people that are firstly trained to follow a "default policy" taking a route in a virtual maze and then asked to navigate to various known goal destinations, either in the way they want ("Go To Goal") or by taking novel shortcuts ("Take Shortcut"). We address these wayfinding problems using InfoRL: an information-theoretic approach that formalizes the cognitive cost of devising a navigational plan, as the informational cost to deviate from a well-learned route (the "default policy"). In InfoRL, optimality refers to finding the best trade-off between route length and the amount of control information required to find it. We report five main findings. First, the navigational strategies automatically identified by InfoRL correspond closely to different routes (optimal or suboptimal) in the virtual reality map, which were annotated by hand in previous research. Second, people deliberate more in places where the value of investing cognitive resources (i.e., relevant goal information) is greater. Third, compared to the group of people who receive the "Go To Goal" instruction, those who receive the "Take Shortcut" instruction find shorter but less optimal solutions, reflecting the intrinsic difficulty of finding optimal shortcuts. Fourth, those who receive the "Go To Goal" instruction modulate flexibly their cognitive resources, depending on the benefits of finding the shortcut. Finally, we found a surprising amount of variability in the choice of navigational strategies and resource investment across participants. Taken together, these results illustrate the benefits of using InfoRL to address navigational planning problems from a bounded rational perspective.
当我们需要回到之前去过的地方时，我们可以选择原路返回或者寻找更短的路径。这两种选择都有代价。在这里，我们提出了一个问题，即是否可以形式化地描述导航计划的选择作为一种权衡计划质量（例如，其长度）和寻找并实施计划所需的认知成本的有界理性过程。我们分析了两组人的导航策略，这些人首先接受了遵循“默认策略”的训练，即在虚拟迷宫中走一条路线，然后被要求以他们想要的方式（“前往目标”）或通过走新的捷径（“走捷径”）导航到各种已知的目标地点。我们使用 InfoRL 来解决这些寻路问题：一种信息论方法，它将设计导航计划的认知成本形式化为偏离学习路线（“默认策略”）的信息成本。在 InfoRL 中，最优性是指在路线长度和找到它所需的控制信息量之间找到最佳权衡。我们报告了五个主要发现。首先，InfoRL 自动识别的导航策略与虚拟现实地图中不同的路线（最优或次优）非常吻合，这些路线在之前的研究中是通过手动标注的。其次，人们在需要投入认知资源（即相关目标信息）的地方会更加深思熟虑。第三，与接受“前往目标”指令的组相比，接受“走捷径”指令的组找到的解决方案较短但不太理想，反映了找到最佳捷径的内在难度。第四，接受“前往目标”指令的组会根据寻找捷径的收益灵活调节认知资源。最后，我们发现参与者在导航策略选择和资源投入方面存在很大的可变性。总的来说，这些结果说明了从有界理性的角度使用 InfoRL 解决导航规划问题的好处。
