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新模型解释了未达成目标时的目标导向选择

研究人员开发了一个名为风险敏感满意度(RS)的新计算模型,该模型解释了即使在未达成目标时,目标导向行为是如何发生的。该模型也被称为注意力竞争贝叶斯(ACB),它表明未达成的目标充当先验均值,与有限的注意力资源一起影响行动选择。该模型预测了一种称为“伪遗忘”的现象,即未选择的行动的估计会漂移到期望水平,而无需明确的数据衰减,并证明调整不可达的期望可以减少决策中的遗憾。 AI

影响 引入了一个理解目标导向决策的新计算框架,可能适用于 AI 代理设计。

排序理由 该集群包含一篇详细介绍新计算模型的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新模型解释了未达成目标时的目标导向选择

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该集群包含一篇详细介绍新计算模型的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Moto Shinriki, Yu Kono, Tatsuji Takahashi ·

    满意作为竞争性贝叶斯加权:目标导向选择在未达成目标下的最小模型

    arXiv:2611.07270v1 Announce Type: cross Abstract: Animals and humans often do not maximize; they act relative to goals (aspiration levels) that are frequently not yet met. How can a goal that is not (yet) realized organize choice? Deacon (2012) calls such absence-referenced organ…