INSPIRE Track 1: Human reasoning and learning in a complex but tractable decision-making paradigm

该INSPIRE奖的部分资金来自计算机与信息科学与工程理事会信息与智能系统部门的稳健智能项目，以及社会、行为和经济科学理事会行为与认知科学部的感知、行动和认知项目。这个项目研究人类智能的一个标志，即提前思考的能力。在商业谈判、军事战略和教学等各种领域，预测自己和他人行为的后果至关重要。在这些领域中，一个人的决策质量取决于一个人对事件序列的心理模拟的质量，这可能受到认知能力的限制，或者一个人对决策空间复杂性的把握，或者两者兼而有之。该项目的目标是确定影响人们提前思考表现的因素，并调查通过培训可以在多大程度上提高这种表现。该项目与心理学和行为经济学中的启发式（决策者使用的一般规则）研究密切相关。在现实世界的问题中，提前思考是很难衡量和建模的。因此，研究者开发了一个两人战略决策任务作为可控的实验环境。参与者轮流将代币放在4x11的板上，并尝试连续获得四个自己的代币。这些规则对受试者来说是陌生的，但很容易学会。这个任务的状态空间的大小是10^20的数量级，比国际象棋（~10^47）小得多，但也相当复杂，而且对人类来说太大了，无法轻易掌握。研究人员使用一种改进版的α - β剪枝“弱解决”了这个任务。它也很可能是强求解的，这意味着人们可以在任何给定的位置确定任何给定的决策是否是错误的。人类数据将在三种任务模式中收集：在一种任务模式中，受试者被赋予一个位置，必须在一定数量的移动中获胜；人对计算机；以及人与人之间的对抗。研究人员将追踪研究对象的眼球运动，这可能会揭示计划的各个方面，甚至可能有助于可视化心理模拟的过程。该项目的一个重要组成部分将是数据的计算建模。人类无法提前想到任务的结束，所以我们假设他们使用简单的位置特征（启发式）来评估某些动作。特征的例子可以是三人行的存在，或者相邻的开放式两人行。初步的人类数据表明，“战略盲点”是由不正确的启发式应用造成的。研究人员的目标是预测受试者在给定位置做出特定移动的概率，基于该移动将产生的位置特征，以及受试者有限的推理深度。由此产生的模型将允许定量地解决学习是否主要用于增加一个人的推理深度或改进一个人的启发式调色板的问题。行为和眼动数据将为研究复杂决策环境下推理的神经基础奠定基础。该项目位于计算机科学、认知心理学、管理和决策科学以及教育的交叉点，并且有可能对这些领域做出贡献。从长远来看，该项目可能有助于理解和避免在现实生活中解决问题时“跳出框框”思考的失败。此外，像这个项目中使用的战略任务可以作为测试教学方法假设的迷你环境。

This INSPIRE award is partially funded by the Robust Intelligence Program in the Division of Information and Intelligent Systems in the Directorate for Computer and Information Science and Engineering and the Perception, Action, and Cognition Program in the Division of Behavioral and Cognitive Sciences in the Directorate for Social, Behavioral, and Economic Sciences.This project studies a hallmark of human intelligence, namely the ability to think ahead. Anticipating the consequences of one's own actions and those of others is of crucial importance in areas as diverse as business negotiations, military strategy, and teaching. In each of these domains, the quality of one's decisions depends on the quality of one's mental simulations of event sequences, which might be limited by cognitive capacity limitations, one's grasp of the complexities of the decision space, or both. The project's goal is to identify the factors that affect people's performance in thinking ahead, and investigate to what extent this performance can be improved through training. The project ties into the study of heuristics (general rules used by decision-makers) in psychology and behavioral economics.Thinking ahead is difficult to measure and model in real-world problems. Therefore, the investigator has developed a two-person strategic decision-making task as a controllable experimental environment. Participants take turns to put tokens on a 4x11 board and try to get four of their own tokens in a row. The rules are unfamiliar to subjects, yet easy to learn. The size of the state space for this task is of the order of 10^20, much smaller than that of chess (~10^47), yet of appreciable complexity and much too large for humans to easily grasp. The investigators have "weakly solved" this task using an improved version of alpha-beta pruning. It can most likely also be solved strongly, which means that one can determine in any given position whether any given decision is an error. Human data will be collected in three task modes: one in which the subject is given a position and has to win in a set number of moves; human versus computer; and human versus human. The investigators will track subjects' eye movements, which could reveal aspects of planning and perhaps even serve to visualize the process of mental simulation. An important component of the project will be computational modeling of the data. Humans cannot think ahead to the end of the task, so we hypothesize that they use simple features of positions (heuristics) to value certain moves over others. Examples of features could be the presence of a three-in-a-row, or of an adjacent, open-ended two-in-a-row. Preliminary human data suggest "strategic blind spots" created by the application of the incorrect heuristics. The investigators aim to predict the probability that a subject will in a given position make a particular move, based on features of the position that would be created by that move, as well as the subject's limited depth of reasoning. The resulting model will allow to quantitatively address the question of whether learning mostly serves to increase one's depth of reasoning or to refine one's palette of heuristics. The behavioral and eye movement data will lay the foundation for studies of the neural substrates of reasoning in complex decision-making contexts.The project is positioned at the intersection of computer science, cognitive psychology, management and decision science, and education, and has the potential to contribute to each of these fields. In the long run, the project might be able to contribute to understanding and perhaps avoiding failures to think "out of the box" in real-life problem-solving. Moreover, strategic tasks like the one used in this project could serve as a mini-environment for testing hypotheses about teaching methods.