基于动物主观感知的奖励是通过在线贝叶斯感知偏差估计实现的。

IF 9.8 1区 生物学 Q1 Agricultural and Biological Sciences
Yelin Dong, Gabor Lengyel, Sabyasachi Shivkumar, Akiyuki Anzai, Grace F DiRisio, Ralf M Haefner, Gregory C DeAngelis
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引用次数: 0

摘要

阐明感知偏差的神经基础,例如由视觉错觉产生的偏见,可以为感知推理的神经机制提供有力的见解。然而,研究动物的主观感知带来了一个根本性的挑战:与人类参与者不同,动物不能被口头指示报告它们的所见所闻或所感。相反,它们必须被训练去执行一项任务以获得奖励,研究人员必须从它们的反应中推断出动物所感知到的东西。然而,动物的反应是由奖励反馈形成的,因此引起了人们的主要担忧,即奖励方案可能会改变动物的决策策略,甚至改变它们内在的感知偏见。通过对强化学习代理的模拟,我们证明了传统的奖励策略无法准确估计感知偏差。我们开发了一种在任务执行过程中估计感知偏差的方法,然后根据对动物感知偏差的进化估计计算每次试验的奖励。我们的方法利用多个刺激上下文来分离感知偏差和决策相关偏差。从信息先验开始,我们的贝叶斯方法在每次试验后更新感知偏差的后验。先验可以根据过去会话的数据来指定,从而减少在线估计的可变性,并允许它在少量试验中收敛到一个稳定的值。在合成数据上验证了我们的方法后,我们将其应用于估计猴子在运动方向识别任务中的感知偏差,其中不同的背景光流会引起鲁棒的感知偏差。这种方法克服了理解主观感知的神经基础的一个重要挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rewarding animals based on their subjective percepts is enabled by online Bayesian estimation of perceptual biases.

Elucidating the neural basis of perceptual biases, such as those produced by visual illusions, can provide powerful insights into the neural mechanisms of perceptual inference. However, studying the subjective percepts of animals poses a fundamental challenge: unlike human participants, animals cannot be verbally instructed to report what they see, hear, or feel. Instead, they must be trained to perform a task for reward, and researchers must infer from their responses what the animal perceived. However, animals' responses are shaped by reward feedback, thus raising the major concern that the reward regimen may alter the animal's decision strategy or even their intrinsic perceptual biases. Using simulations of a reinforcement learning agent, we demonstrate that conventional reward strategies fail to allow accurate estimation of perceptual biases. We developed a method that estimates perceptual bias during task performance and then computes the reward for each trial based on the evolving estimate of the animal's perceptual bias. Our approach makes use of multiple stimulus contexts to dissociate perceptual biases from decision-related biases. Starting with an informative prior, our Bayesian method updates a posterior over the perceptual bias after each trial. The prior can be specified based on data from past sessions, thus reducing the variability of the online estimate and allowing it to converge to a stable value over a small number of trials. After validating our method on synthetic data, we apply it to estimate perceptual biases of monkeys in a motion direction discrimination task in which varying background optic flow induces robust perceptual biases. This method overcomes an important challenge to understanding the neural basis of subjective percepts.

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来源期刊
PLoS Biology
PLoS Biology BIOCHEMISTRY & MOLECULAR BIOLOGY-BIOLOGY
CiteScore
15.40
自引率
2.00%
发文量
359
审稿时长
3-8 weeks
期刊介绍: PLOS Biology is the flagship journal of the Public Library of Science (PLOS) and focuses on publishing groundbreaking and relevant research in all areas of biological science. The journal features works at various scales, ranging from molecules to ecosystems, and also encourages interdisciplinary studies. PLOS Biology publishes articles that demonstrate exceptional significance, originality, and relevance, with a high standard of scientific rigor in methodology, reporting, and conclusions. The journal aims to advance science and serve the research community by transforming research communication to align with the research process. It offers evolving article types and policies that empower authors to share the complete story behind their scientific findings with a diverse global audience of researchers, educators, policymakers, patient advocacy groups, and the general public. PLOS Biology, along with other PLOS journals, is widely indexed by major services such as Crossref, Dimensions, DOAJ, Google Scholar, PubMed, PubMed Central, Scopus, and Web of Science. Additionally, PLOS Biology is indexed by various other services including AGRICOLA, Biological Abstracts, BIOSYS Previews, CABI CAB Abstracts, CABI Global Health, CAPES, CAS, CNKI, Embase, Journal Guide, MEDLINE, and Zoological Record, ensuring that the research content is easily accessible and discoverable by a wide range of audiences.
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