Planning with Theory of Mind

心理学 心理理论 选择(遗传算法) 平面图(考古学) 认知心理学 认知科学 心理学理论 心理干预 认知 认识论 社会心理学 人工智能 计算机科学 哲学 考古 神经科学 精神科 历史
作者
Mark K. Ho,Rebecca Saxe,Fiery Cushman
出处
期刊:Trends in Cognitive Sciences [Elsevier]
卷期号:26 (11): 959-971 被引量:42
标识
DOI:10.1016/j.tics.2022.08.003
摘要

Theory of Mind research has traditionally emphasized its predictive function (e.g., predicting someone will be angry after being stuck in traffic). Prediction tasks have dominated decades of experimental and computational research. Theory of Mind is also used to plan interventions on other minds (e.g., choosing how to cheer someone up who has been stuck in traffic) and representations used for planning will have different requirements from those only used for prediction. Research on planning emphasizes the importance of abstract and structured causal models, like Theory of Mind. Focusing on Theory of Mind for planning can illuminate a range of socio-cognitive phenomena, such as interpersonal affect regulation, impression management, pragmatic speech, and pedagogy. Understanding Theory of Mind should begin with an analysis of the problems it solves. The traditional answer is that Theory of Mind is used for predicting others’ thoughts and actions. However, the same Theory of Mind is also used for planning to change others’ thoughts and actions. Planning requires that Theory of Mind consists of abstract structured causal representations and supports efficient search and selection from innumerable possible actions. Theory of Mind contrasts with less cognitively demanding alternatives: statistical predictive models of other people’s actions, or model-free reinforcement of actions by their effects on other people. Theory of Mind is likely used to plan novel interventions and predict their effects, for example, in pedagogy, emotion regulation, and impression management. Understanding Theory of Mind should begin with an analysis of the problems it solves. The traditional answer is that Theory of Mind is used for predicting others’ thoughts and actions. However, the same Theory of Mind is also used for planning to change others’ thoughts and actions. Planning requires that Theory of Mind consists of abstract structured causal representations and supports efficient search and selection from innumerable possible actions. Theory of Mind contrasts with less cognitively demanding alternatives: statistical predictive models of other people’s actions, or model-free reinforcement of actions by their effects on other people. Theory of Mind is likely used to plan novel interventions and predict their effects, for example, in pedagogy, emotion regulation, and impression management. a mental representation that categorizes and generalizes across instances with diverse particular features. a model of the structure of relations between variables that supports counterfactual and hypothetical reasoning (i.e., one that specifies the probability of one variable setting conditioned upon intervention on another variable). actions chosen based on learned action–reward associations. Habits do not represent, and cannot be used to predict, the outcomes of actions. learning the value of an action, in context, from repeated association of the action with subsequent reward. a mechanism for action selection that relies on using a model to simulate the future consequences of potential interventions in order to choose one that maximizes expected value. a model of statistical correlations or sequences of events that can be queried to generate an expectation for one variable or state, given an observation of another variable. mechanisms and strategies for organizing memory that facilitate rapid retrieval of context- and goal-appropriate action representations in large state spaces. a mental representation that is the composition of simpler representations and whose content derives from these constituents and how they are combined. a causal model of the mind, specifying how mental states like perceptions, beliefs, and desires combine to cause actions and feelings. top-down recombination of causal elements to construct a simplified causal model that is useful for planning in context.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
DUOMI完成签到,获得积分20
1秒前
chang完成签到,获得积分10
1秒前
wan发布了新的文献求助10
1秒前
an慧儿发布了新的文献求助50
1秒前
清歌完成签到,获得积分10
2秒前
科研通AI2S应助vasp采纳,获得10
2秒前
2秒前
哟哟哟哟完成签到,获得积分20
2秒前
2秒前
咚咚蛋完成签到,获得积分10
3秒前
迅速冰岚完成签到,获得积分10
4秒前
ste56完成签到,获得积分10
4秒前
曹曹完成签到,获得积分20
4秒前
爱笑映菡完成签到,获得积分10
5秒前
5秒前
5秒前
kyle完成签到 ,获得积分10
5秒前
动听心锁完成签到 ,获得积分10
6秒前
roberto20发布了新的文献求助10
6秒前
肉肉完成签到,获得积分10
6秒前
67完成签到 ,获得积分10
7秒前
7秒前
搜集达人应助丶氵一生里采纳,获得10
7秒前
笑天倸完成签到,获得积分10
9秒前
9秒前
10秒前
敏感松鼠完成签到,获得积分20
10秒前
南风不竞发布了新的文献求助10
10秒前
ZzZz发布了新的文献求助10
10秒前
11秒前
11秒前
PAD完成签到,获得积分10
11秒前
蓝色的云发布了新的文献求助10
12秒前
12秒前
Orange应助671采纳,获得10
13秒前
帅气的颜演完成签到,获得积分20
13秒前
13秒前
自信安荷发布了新的文献求助10
13秒前
Forever发布了新的文献求助10
13秒前
13秒前
高分求助中
Evolution 2024
Impact of Mitophagy-Related Genes on the Diagnosis and Development of Esophageal Squamous Cell Carcinoma via Single-Cell RNA-seq Analysis and Machine Learning Algorithms 2000
Experimental investigation of the mechanics of explosive welding by means of a liquid analogue 1060
Die Elektra-Partitur von Richard Strauss : ein Lehrbuch für die Technik der dramatischen Komposition 1000
How to Create Beauty: De Lairesse on the Theory and Practice of Making Art 1000
Gerard de Lairesse : an artist between stage and studio 670
CLSI EP47 Evaluation of Reagent Carryover Effects on Test Results, 1st Edition 600
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3005490
求助须知:如何正确求助?哪些是违规求助? 2664808
关于积分的说明 7223959
捐赠科研通 2301615
什么是DOI,文献DOI怎么找? 1220460
科研通“疑难数据库(出版商)”最低求助积分说明 594762
版权声明 593281