Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology.
Department of Psychology, Harvard University.
Cogn Sci. 2021 Sep;45(9):e13041. doi: 10.1111/cogs.13041.
Humans routinely make inferences about both the contents and the workings of other minds based on observed actions. People consider what others want or know, but also how intelligent, rational, or attentive they might be. Here, we introduce a new methodology for quantitatively studying the mechanisms people use to attribute intelligence to others based on their behavior. We focus on two key judgments previously proposed in the literature: judgments based on observed outcomes (you're smart if you won the game) and judgments based on evaluating the quality of an agent's planning that led to their outcomes (you're smart if you made the right choice, even if you didn't succeed). We present a novel task, the maze search task (MST), in which participants rate the intelligence of agents searching a maze for a hidden goal. We model outcome-based attributions based on the observed utility of the agent upon achieving a goal, with higher utilities indicating higher intelligence, and model planning-based attributions by measuring the proximity of the observed actions to an ideal planner, such that agents who produce closer approximations of optimal plans are seen as more intelligent. We examine human attributions of intelligence in three experiments that use MST and find that participants used both outcome and planning as indicators of intelligence. However, observing the outcome was not necessary, and participants still made planning-based attributions of intelligence when the outcome was not observed. We also found that the weights individuals placed on plans and on outcome correlated with an individual's ability to engage in cognitive reflection. Our results suggest that people attribute intelligence based on plans given sufficient context and cognitive resources and rely on the outcome when computational resources or context are limited.
人们通常会根据观察到的行为来推断他人的想法和思维过程。人们会考虑他人的需求和所知，也会考虑他们可能有多么聪明、理性或专注。在这里，我们介绍了一种新的方法，用于定量研究人们根据他人的行为来判断他人智力的机制。我们专注于文献中提出的两个关键判断：基于观察结果的判断（如果你赢得了比赛，你就很聪明）和基于评估导致结果的代理人的规划质量的判断（如果你做出了正确的选择，即使你没有成功，你也很聪明）。我们提出了一个新的任务，即迷宫搜索任务（MST），在这个任务中，参与者对搜索迷宫以寻找隐藏目标的代理人的智力进行评分。我们根据代理人在达到目标时的观察到的效用来建模基于结果的归因，较高的效用表示较高的智力，我们通过测量观察到的行动与理想规划者的接近程度来建模基于规划的归因，使得产生更接近最优规划的代理人被视为更聪明。我们在三个使用 MST 的实验中研究了人类对智力的归因，并发现参与者同时使用结果和规划作为智力的指标。然而，即使没有观察到结果，参与者仍然会根据规划进行智力归因。我们还发现，个人对计划和结果的重视程度与个人进行认知反思的能力相关。我们的结果表明，当计算资源或上下文有限时，人们会根据足够的背景和认知资源来根据计划推断智力，并在需要时依赖结果。
