Xue Kai, Shekhar Medha, Rahnev Dobromir
School of Psychology, Georgia Institute of Technology, Atlanta, GA.
Res Sq. 2024 Dec 11:rs.3.rs-5510856. doi: 10.21203/rs.3.rs-5510856/v1.
A central goal of research in perceptual decision making is to determine the internal computations underlying choice and confidence in complex, multi-alternative tasks. However, revealing these computations requires knowledge of the internal representation upon which the computations operate. Unfortunately, it is unknown how traditional stimuli (e.g., Gabor patches and random dot motion) are represented internally, which calls into question the computations inferred when using such stimuli. Here we develop a new behavioral paradigm where subjects discriminate the dominant color in a cloud of differently colored dots. Critically, we show that the internal representation for these stimuli can be described with a simple, one-parameter equation and that a single free parameter can explain multi-alternative data for up to 12 different conditions. Further, we use this paradigm to test three popular theories: that confidence reflects (1) the probability of being correct, (2) only choice-congruent (i.e., positive) evidence, or (3) the evidence difference between the highest and the second-highest signal.The predictions of the first two theories were falsified in two experiments involving either six or 12 conditions with three choices each. We found that the data were best explained by a model where confidence is based on the difference of the two alternatives with the largest evidence. These results establish a new paradigm in which a single parameter can be used to determine the internal representation for an unlimited number of multi-alternative conditions and challenge two prominent theories of confidence computation.
感知决策研究的一个核心目标是确定复杂多选项任务中选择和信心背后的内部计算过程。然而，要揭示这些计算过程，需要了解这些计算所基于的内部表征。不幸的是，目前尚不清楚传统刺激（如Gabor斑块和随机点运动）在内部是如何被表征的，这使得在使用此类刺激时推断出的计算过程受到质疑。在此，我们开发了一种新的行为范式，让受试者辨别不同颜色点云团中的主色。关键的是，我们表明这些刺激的内部表征可以用一个简单的单参数方程来描述，并且一个自由参数可以解释多达12种不同条件下的多选项数据。此外，我们使用这种范式来测试三种流行的理论：即信心反映（1）正确的概率，（2）仅选择一致（即正向）的证据，或（3）最高信号与第二高信号之间的证据差异。在前两个实验中，涉及六种或十二种条件，每种条件有三种选择，前两种理论的预测被证伪。我们发现，数据最好由一个模型来解释，在该模型中，信心基于具有最大证据的两个选项之间的差异。这些结果建立了一种新的范式，其中一个参数可用于确定无限数量多选项条件下的内部表征，并对两种著名的信心计算理论提出了挑战。
