Efficient visual representations for learning and decision making.

IF 5.1 1区 心理学 Q1 PSYCHOLOGY Psychological review Pub Date : 2024-09-19 DOI:10.1037/rev0000498
Tyler Malloy, Chris R Sims
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Abstract

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).

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用于学习和决策的高效视觉表征。
由于世界的复杂性和不确定性,以及认知系统能力的内在限制,视觉信息的有效表征对于学习和决策至关重要。我们假设,生物制剂能学会以一种平衡多个潜在竞争目标的方式有效地表征视觉信息。在本文中,我们将探讨这样两个目标:以支持准确回忆(最大化真实性)和促进基于效用的决策(最大化行为效用)的方式存储信息。这两个目标可能存在冲突,这一点并不明显。我们的假设表明,孤立地研究行为或表征的形成都无法完全理解它们,信息处理的限制因素会对它们产生总体影响。在提出这一假设的同时,我们借鉴机器学习和神经科学的最新方法,建立了表征形成和行为的计算模型。由此产生的模型既能解释人类视觉学习的有利方面,如快速获取和高度泛化,也能解释信息限制导致的偏差。为了检验这一模型,我们开发了决策和学习两个实验范例,以评估模型的预测与人类行为的匹配程度。该模型的一个主要特点是,它能预测人类决策过程中常见偏差的出现,这些偏差是由于人类希望形成有效的视觉信息表征,以实现学习和决策过程中的行为目标,并在信息处理约束条件下进行优化。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Psychological review
Psychological review 医学-心理学
CiteScore
9.70
自引率
5.60%
发文量
97
期刊介绍: Psychological Review publishes articles that make important theoretical contributions to any area of scientific psychology, including systematic evaluation of alternative theories.
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