The efficient representation of visual information is essential for learning and decision making due to the complexity and uncertainty of the world, as well as inherent constraints on the capacity of cognitive systems. We hypothesize that biological agents learn to efficiently represent visual information in a manner that balances performance across multiple potentially competing objectives. In this article, we examine two such objectives: storing information in a manner that supports accurate recollection (maximizing veridicality) and in a manner that facilitates utility-based decision making (maximizing behavioral utility). That these two objectives may be in conflict is not immediately obvious. Our hypothesis suggests that neither behavior nor representation formation can be fully understood by studying either in isolation, with information processing constraints exerting an overarching influence. Alongside this hypothesis we develop a computational model of representation formation and behavior motivated by recent methods in machine learning and neuroscience. The resulting model explains both the beneficial aspects of human visual learning, such as fast acquisition and high generalization, as well as the biases that result from information constraints. To test this model, we developed two experimental paradigms, in decision making and learning, to evaluate how well the model's predictions match human behavior. A key feature of the proposed model is that it predicts the occurrence of commonly found biases in human decision making, resulting from the desire to form efficient representations of visual information that are useful for behavioral goals in learning and decision making and optimized under an information processing constraint. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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用于学习和决策的有效视觉表示。


由于世界的复杂性和不确定性以及认知系统能力的固有限制，视觉信息的有效表示对于学习和决策至关重要。我们假设生物代理学习以平衡多个潜在竞争目标的性能的方式有效地表示视觉信息。在本文中，我们研究了两个这样的目标：以支持准确回忆（最大化真实性）的方式存储信息，以及以促进基于效用的决策（最大化行为效用）的方式存储信息。这两个目标可能会发生冲突，这一点并不明显。我们的假设表明，无论是行为还是表征形成，都不能通过孤立地研究来完全理解，信息处理的限制发挥着至关重要的影响。除了这个假设之外，我们还开发了一种由机器学习和神经科学的最新方法驱动的表征形成和行为的计算模型。由此产生的模型解释了人类视觉学习的有益方面，例如快速获取和高度泛化，以及信息限制导致的偏差。为了测试这个模型，我们开发了决策和学习两个实验范式，以评估模型的预测与人类行为的匹配程度。所提出模型的一个关键特征是，它预测了人类决策中常见偏差的发生，这些偏差是由于希望形成视觉信息的有效表示而产生的，这些表示对于学习和决策中的行为目标有用，并在信息处理下进行了优化约束。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。