Contemporary models of categorization typically tend to sidestep the problem of how information is initially encoded during decision making. Instead, a focus of this work has been to investigate how, through selective attention, stimulus representations are “contorted” such that behaviorally relevant dimensions are accentuated (or “stretched”), and the representations of irrelevant dimensions are ignored (or “compressed”). In high-dimensional real-world environments, it is computationally infeasible to sample all available information, and human decision makers selectively sample information from sources expected to provide relevant information. To address these and other shortcomings, we develop an active sampling model, Sampling Emergent Attention (SEA), which sequentially and strategically samples information sources until the expected cost of information exceeds the expected benefit. The model specifies the interplay of two components, one involved in determining the expected utility of different information sources and the other in representing knowledge and beliefs about the environment. These two components interact such that knowledge of the world guides information sampling, and what is sampled updates knowledge. Like human decision makers, the model displays strategic sampling behavior, such as terminating information search when sufficient information has been sampled and adaptively adjusting the search path in response to previously sampled information. The model also shows human-like failure modes. For example, when information exploitation is prioritized over exploration, the bidirectional influences between information sampling and learning can lead to the development of beliefs that systematically differ from reality. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

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信息抽样和概念学习的双向影响。

当代的分类模型通常倾向于回避在决策过程中信息最初是如何编码的问题。相反，这项工作的重点是研究如何通过选择性注意来“扭曲”刺激表征，从而突出（或“拉伸”）与行为相关的维度，并忽略（或“压缩”）不相关维度的表征). 在高维现实世界环境中，从计算上不可能对所有可用信息进行采样，人类决策者有选择地从预期提供相关信息的来源中采样信息。为了解决这些和其他缺点，我们开发了一个主动采样模型，采样紧急注意（SEA），它按顺序和战略性地对信息源进行抽样，直到信息的预期成本超过预期收益。该模型指定了两个组件的相互作用，一个组件涉及确定不同信息源的预期效用，另一个组件代表有关环境的知识和信念。这两个组成部分相互作用，使得世界知识指导信息采样，而采样更新知识。与人类决策者一样，该模型表现出策略性的采样行为，例如在采样到足够的信息时终止信息搜索，并自适应地调整搜索路径以响应先前采样的信息。该模型还显示了类似人类的故障模式。例如，当信息开发优先于探索时，信息抽样和学习之间的双向影响可能导致系统地与现实不同的信念的发展。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）