论文标题
大脑概率的量化表示
A Quantized Representation of Probability in the Brain
论文作者
论文摘要
常规和当前的智慧假设大脑将概率表示为许多小数点的连续数字。考虑到大脑中的有限和稀缺的资源，这个假设似乎令人难以置信。量化是一个信息编码过程，将连续数量系统地分为有限数量的可能类别。舍入是量化的简单示例。我们应用此信息理论概念来开发一种新颖的（即离散）概率失真函数。我们开发了三个连词概率赌博任务，以寻找大脑中量化概率表示的证据。我们假设如果存在量化表示，则某些概率范围将在同一冷漠类别中融合在一起。例如，可以无动处理两个不同的概率，例如0.57和0.585。我们广泛的数据分析发现了有力的证据来支持这种量化的表示：59/76名参与者（即78％）证明了与4位量化模型而不是连续模型的最佳拟合度。这一观察是当前工作的主要发展和新颖性。大脑很可能采用量化概率的量化表示。这一发现证明了大脑代表性和决策能力的主要精确限制。
Conventional and current wisdom assumes that the brain represents probability as a continuous number to many decimal places. This assumption seems implausible given finite and scarce resources in the brain. Quantization is an information encoding process whereby a continuous quantity is systematically divided into a finite number of possible categories. Rounding is a simple example of quantization. We apply this information theoretic concept to develop a novel quantized (i.e., discrete) probability distortion function. We develop three conjunction probability gambling tasks to look for evidence of quantized probability representations in the brain. We hypothesize that certain ranges of probability will be lumped together in the same indifferent category if a quantized representation exists. For example, two distinct probabilities such as 0.57 and 0.585 may be treated indifferently. Our extensive data analysis has found strong evidence to support such a quantized representation: 59/76 participants (i.e., 78%) demonstrated a best fit to 4-bit quantized models instead of continuous models. This observation is the major development and novelty of the present work. The brain is very likely to be employing a quantized representation of probability. This discovery demonstrates a major precision limitation of the brain's representational and decision-making ability.
