University of California, Los Angeles, CA, USA.
Kyoto University, Kyoto, Japan.
Nat Commun. 2023 Jul 6;14(1):3992. doi: 10.1038/s41467-023-39737-2.
Previous work has sought to understand decision confidence as a prediction of the probability that a decision will be correct, leading to debate over whether these predictions are optimal, and whether they rely on the same decision variable as decisions themselves. This work has generally relied on idealized, low-dimensional models, necessitating strong assumptions about the representations over which confidence is computed. To address this, we used deep neural networks to develop a model of decision confidence that operates directly over high-dimensional, naturalistic stimuli. The model accounts for a number of puzzling dissociations between decisions and confidence, reveals a rational explanation of these dissociations in terms of optimization for the statistics of sensory inputs, and makes the surprising prediction that, despite these dissociations, decisions and confidence depend on a common decision variable.
先前的研究旨在理解决策信心作为决策正确概率的预测指标，由此引发了关于这些预测是否最优以及它们是否依赖于与决策本身相同的决策变量的争论。这些研究通常依赖于理想化的低维模型，因此需要对用于计算置信度的表示形式做出严格的假设。为了解决这个问题，我们使用深度神经网络开发了一个直接作用于高维自然刺激的决策信心模型。该模型解释了决策和信心之间的许多令人费解的分离现象，根据对感官输入统计数据的优化，为这些分离现象提供了合理的解释，并做出了令人惊讶的预测，即尽管存在这些分离现象，决策和信心仍然依赖于共同的决策变量。
