Prospective Optimization with Limited Resources.
Prospective Optimization with Limited Resources.
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DOI：
10.1371/journal.pcbi.1004501
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发表时间：
2015-09
影响因子：
4.3
通讯作者：
Gepshtein S
中科院分区：
文献类型：
--
作者：
Snider J;Lee D;Poizner H;Gepshtein S
未来是不确定的，因为一些即将发生的事件是不可预测的，也因为我们预见自己行为的无数后果的能力是有限的。在这里，我们研究了人类如何选择这样的外在和内在的不确定性下的行动，鉴于一个分支多值视觉刺激的前景数量呈指数级增长。一个由不同大小的磁盘组成的三角形网格以可变的速度在触摸屏上滚动。圆盘越大，奖励越多。任务是通过快速连续地触摸一个圆盘来最大化累积奖励，形成一条穿过网格的向上路径，而沿着路径的每一步沿着都限制了未来可访问的网格部分。这项任务捕捉到了风险和动态世界中自然行为的一些复杂性，在这个世界中，正在进行的决策会改变未来回报的前景。通过将人类行为与理想行为者的行为进行比较，我们确定了人类所使用的策略，包括他们对未来的展望（他们的“计算深度”）以及他们尝试整合有关未来奖励的新信息的频率（他们的“重新计算期”）。我们发现，对于一个给定的任务难度，人类权衡了他们的计算深度的重新计算时间。这种权衡的形式与在资源有限的有限深度内对所有可能的路径进行完全的、蛮力的探索是一致的。对人类行为的逐步分析表明，参与者考虑到了未来奖励之间非常细微的区别，并且在评估替代路径时避免了一些简单的策略，例如只寻找最大的磁盘或避免较小的磁盘。与会者宁愿减少他们的计算深度或增加重新计算的时间，而不是牺牲计算的精度。我们研究了人类在危险的动态环境中前瞻性地组织行为的能力，为未来的多个步骤。在一个类似于视频游戏的环境中，参与者选择了最有价值的路径，穿过一个由不同大小的磁盘组成的三角形网格，而网格以恒定的速度在触摸屏上滚动。磁盘大小代表奖励;丢失磁盘将受到惩罚。每一个选择都排除了一些未来可以访问的磁盘，鼓励受试者尽可能地检查未来的预期路径。与先前的证据表明人类倾向于通过简化数学来降低决策的计算难度相反，我们的参与者似乎在有限的计算范围内对所有可能的未来情景进行了详尽的计算。随着时间压力的增加，参与者要么减少了计算范围，要么减少了重新计算预期奖励的频率，这表明他们在资源有限的情况下能够快速详细地计算预期行动。为了执行这种密集的计算，参与者可以利用视觉系统的大规模并行神经架构，允许同时处理来自多个视网膜位置的信息。
The future is uncertain because some forthcoming events are unpredictable and also because our ability to foresee the myriad consequences of our own actions is limited. Here we studied how humans select actions under such extrinsic and intrinsic uncertainty, in view of an exponentially expanding number of prospects on a branching multivalued visual stimulus. A triangular grid of disks of different sizes scrolled down a touchscreen at a variable speed. The larger disks represented larger rewards. The task was to maximize the cumulative reward by touching one disk at a time in a rapid sequence, forming an upward path across the grid, while every step along the path constrained the part of the grid accessible in the future. This task captured some of the complexity of natural behavior in the risky and dynamic world, where ongoing decisions alter the landscape of future rewards. By comparing human behavior with behavior of ideal actors, we identified the strategies used by humans in terms of how far into the future they looked (their “depth of computation”) and how often they attempted to incorporate new information about the future rewards (their “recalculation period”). We found that, for a given task difficulty, humans traded off their depth of computation for the recalculation period. The form of this tradeoff was consistent with a complete, brute-force exploration of all possible paths up to a resource-limited finite depth. A step-by-step analysis of the human behavior revealed that participants took into account very fine distinctions between the future rewards and that they abstained from some simple heuristics in assessment of the alternative paths, such as seeking only the largest disks or avoiding the smaller disks. The participants preferred to reduce their depth of computation or increase the recalculation period rather than sacrifice the precision of computation. We investigated the human ability to organize behavior prospectively, for multiple future steps in risky, dynamic environments. In a setting that resembled a video game, participants selected the most rewarding paths traversing a triangular lattice of disks of different sizes, while the lattice scrolled down a touchscreen at a constant speed. Disk sizes represented the rewards; missing a disk incurred a penalty. Every choice excluded a number of the disks accessible in the future, encouraging subjects to examine prospective paths as far into the future as they could. In contrast to previous evidence that humans tend to reduce the computational difficulty of decision making by means of simplifying heuristics, our participants appeared to perform an exhaustive computation of all possible future scenarios within a horizon limited by a fixed number of computations. Under increasing time pressure, participants either reduced the computational horizon or recalculated the expected rewards less frequently, revealing a resource-limited ability for rapid detailed computation of prospective actions. To perform such intensive computations, participants could take advantage of the massively parallel neural architecture of the visual system allowing one to concurrently process information from multiple retinal locations.