社会决策信息的抽象与泛化&#x0002A;

汪涵; 董昱林; 刘凝丰; 朱露莎

doi:10.16719/j.cnki.1671-6981.20250416

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心理科学 ›› 2025, Vol. 48 ›› Issue (4) : 962-971. DOI: 10.16719/j.cnki.1671-6981.20250416

计算建模与人工智能

社会决策信息的抽象与泛化^*

汪涵, 董昱林, 刘凝丰, 朱露莎^**

作者信息 +

The Abstraction and Generalization of Social Decision Information

Wang Han, Dong Yulin, Liu Ningfeng, Zhu Lusha

Author information +

文章历史 +

摘要

在人工智能、心理和神经科学等领域,研究者致力于探索智能体如何从复杂信息提取关键特征,以实现不同任务中的灵活决策,这类关于抽象与泛化的研究多集中于非社会领域。然而社会决策更为复杂和独特,深入研究多智能体在互动中如何理解、使用和泛化相关信息显得尤为重要。在社会互动中,智能体不仅需要过滤无关细节,抽象出影响决策的核心信息（如概念、观点和策略等）,还需要推测其理解与抽象是否与其他智能体一致。这种社会性抽象和泛化是通用社会智能的核心。文章举例回顾非社会决策领域的成果有助于展望社会决策领域的关键研究问题,并且进一步强调,理解人脑如何从复杂社交信息文章中提炼通用模式是阐明通用社会智能的神经与类脑计算机制的核心环节。

Abstract

Recent advances in artificial intelligence (AI), cognitive psychology, and neuroscience have significantly enhanced our understanding of abstraction and generalization — how agents extract key features from complex decision environments to support efficient and generalizable decision-making. While extensive research has elucidated the mechanisms of abstraction and generalization in non-social contexts, such as rule-based learning, far less is known about how these processes in social domains. During social interactions, agents must not only filter out irrelevant details and abstract core decision-relevant information (e.g., concepts, perspectives, strategies), but also infer whether their internal representations align with those of others. Investigating how multi-agent systems understand, utilize, and generalize relevant information in service of effective interactions is increasingly critical for understanding the mechanisms underlying general social intelligence. Building on recent findings in non-social decision-making, this paper outlines future research directions for studying abstraction and generalization in social contexts.
Specifically, in the non-social domain, studies of generalization have highlighted two core mechanisms: rule-based strategies, which involve hierarchical categorization of features, and similarity-based approaches, such as analogical reasoning via prototype matching. At the computational level, a prominent example in this area is the successor representation (SR) model, which provides a unified computational framework for AI, behavioral and neuroscience research. Developed in close relation to reinforcement learning theories, SR compresses decision states by encoding predictive relationships, thereby enabling rapid adaptation to reward changes. Inspired by SR predictions, emerging neurobiological evidence has implicated the hippocampal and prefrontal representations of abstract knowledge, potentially facilitating knowledge transfer and strategy generalization. Parallel research in AI demonstrates how deep successor reinforcement learning (DSR) can leverage SR to achieve cross-task generalization in navigation and robotic design. These findings underscore abstraction as a conserved yet effective mechanism for flexible decision-making across both biological and artificial systems.
However, social interactions introduce unique computational demands. Agents must extract and organize relevant information, while negotiating shared intentions and conventions during cooperative interactions or strategizing to outmaneuver opponents during competitive interactions. Despite progress in characterizing the neural and computational mechanisms of various social cognitive functions, fundamental questions remain. For example, how do interacting agents form aligned or misaligned abstractions? How do goals and environmental constraints shape these abstractions, and how do these, in turn, influence decision strategies? What computational and neural mechanisms give rise to the dynamic alignment or divergence of abstractions and generalization across individuals?
Three interconnected open questions critical to future research should be addressed. First, effective social cooperation typically requires abstraction hierarchies and knowledge organization shared across interacting agents. While neural coupling across cooperative individuals is well-documented, the computational role of such alignment in facilitating social decision-making and its strategy generalization remains unclear. Research in AI suggests that alignment enhances cross-task performance, but its relevance to human social cognition requires further investigation.
Second, abstractions of decision-relevant information may vary across individuals with divergent social experiences, network positions, and cultural backgrounds. Therefore, it is critical to elucidate, at a computational level, when and how individual differences give rise to misaligned internal representations, which may contribute to phenomena such as prejudice and polarization. Investigating these misaligned representations will shed light on how social identities shape perception and decision-making, ultimately informing strategies to mitigate bias and foster social cohesion.
Finally, successful social cooperation often depends on aligning initially misaligned representations across individuals. It is important to identify the mechanisms that support such interpersonal adaptation. Although prior research in AI and evolutionary biology has highlighted the benefits of alignment dynamics, the cognitive and neurocomputational processes that govern these internal changes remain to be explored.
Together, this paper proposes to bridge ideas and computational methods in social decision-making, AI, and cognitive neuroscience for developing a mechanistic understanding of abstraction and representation. Such an integrative framework holds the potential to reveal the computational principles of general social intelligence and inspire the design of socially intelligent systems.

导出引用

汪涵, 董昱林, 刘凝丰, 朱露莎. 社会决策信息的抽象与泛化^*[J]. 心理科学. 2025, 48(4): 962-971 https://doi.org/10.16719/j.cnki.1671-6981.20250416

Wang Han, Dong Yulin, Liu Ningfeng, Zhu Lusha. The Abstraction and Generalization of Social Decision Information[J]. Journal of Psychological Science. 2025, 48(4): 962-971 https://doi.org/10.16719/j.cnki.1671-6981.20250416

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