RT Journal Article
SR Electronic
T1 Extended Bayesian inference incorporating symmetry bias
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 698290
DO 10.1101/698290
A1 Shinohara, Shuji
A1 Manome, Nobuhito
A1 Suzuki, Kouta
A1 Chung, Ung-il
A1 Takahashi, Tatsuji
A1 Gunji, Pegio-Yukio
A1 Nakajima, Yoshihiro
A1 Mitsuyoshi, Shunji
YR 2019
UL http://biorxiv.org/content/early/2019/07/10/698290.abstract
AB In this study, we start by proposing a causal induction model that incorporates symmetry bias. This model is important in two aspects. First, it can reproduce causal induction of human judgment with higher accuracy than conventional models. Second, it allows us to estimate the level of symmetry bias of subjects from experimental data. We further propose an inference method that incorporates the aforementioned causal induction model into Bayesian inference. In this method, the component of Bayesian inference, which updates the degree of confidence for each hypothesis, and the component of inverse Bayesian inference that modifies the model of the hypothesis coexist. Our study demonstrates that inverse Bayesian inference enables us to deal flexibly with unstable situations where the object of inference changes from time to time.Author summary We acquire knowledge through learning and make various inferences based on such knowledge and observational data (evidence). If the evidence is insufficient, then the certainty of the conclusion will decline. Moreover, even if the evidence is sufficient, the conclusion may be wrong if the knowledge is incomplete in the first place. In order to model such inference based on incomplete knowledge, we proposed an inference system that performs learning and inference simultaneously and seamlessly. Prepare two coins A and B with different probabilities of landing heads, and repeat the coin toss using either of them. However, the coin that is being tossed is also replaced repeatedly. The system observes only the result of coin toss each time, and estimates the probability of landing heads of coin tossed at the moment. In this task, it is necessary not only to estimate the probabilities of the landing heads of coin A and B, but also to estimate which coin is being used at the moment. In this paper, we show that the proposed system handles such tasks very efficiently by simultaneously performing inference and learning.