Natural statistics support a rational account of confidence biases.
Natural statistics support a rational account of confidence biases.
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DOI：
10.1038/s41467-023-39737-2
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发表时间：
2023-07-06
影响因子：
16.6
通讯作者：
Lau, Hakwan
中科院分区：
文献类型：
--
作者：
Webb, Taylor W.;Miyoshi, Kiyofumi;So, Tsz Yan;Rajananda, Sivananda;Lau, Hakwan
以前的工作试图将决策信心理解为对决策正确概率的预测，这导致了关于这些预测是否是最优的，以及它们是否依赖于与决策本身相同的决策变量的争论。这项工作通常依赖于理想化的低维模型，需要对计算置信度的表示进行强有力的假设。为了解决这个问题，我们使用深度神经网络开发了一个决策信心模型，该模型直接在高维自然刺激下运行。该模型解释了决策和信心之间的一些令人困惑的分离，揭示了这些分离的合理解释，优化感官输入的统计数据，并作出令人惊讶的预测，尽管这些分离，决策和信心取决于一个共同的决策变量。人类的决策信心显示出许多偏见，并已被证明与决策准确性无关。在这里，通过使用神经网络和贝叶斯模型，作者表明，这些影响可以解释的感官输入的统计。
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. Human decision confidence displays a number of biases and has been shown to dissociate from decision accuracy. Here, by using neural network and Bayesian models, the authors show that these effects can be explained by the statistics of sensory inputs.
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影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
DOI：
10.1098/rstb.2011.0380
发表时间：
2012-05-19
影响因子：
6.3
作者：
Ko, Yoshiaki;Lau, Hakwan
通讯作者：
Lau, Hakwan
影响因子：
16.2
作者：
Lak, Armin;Costa, Gil M.;Romberg, Erin;Koulakov, Alexei A.;Mainen, Zachary F.;Kepecs, Adam
通讯作者：
Kepecs, Adam
影响因子：
4.3
作者：
Aitchison L;Bang D;Bahrami B;Latham PE
通讯作者：
Latham PE
影响因子：
16.6
作者：
Balsdon, Tarryn;Wyart, Valentin;Mamassian, Pascal
通讯作者：
Mamassian, Pascal