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Bayesian Filtering with Multiple Internal Models: Toward a Theory of Social Intelligence

机译：具有多个内部模型的贝叶斯过滤：一种社会智能理论

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摘要

To exhibit social intelligence, animals have to recognize whom they are communicating with. One way to make this inference is to select among internal generative models of each conspecific who may be encountered. However, these models also have to be learned via some form of Bayesian belief updating. This induces an interesting problem: When receiving sensory input generated by a particular conspecific, how does an animal know which internal model to update? We consider a theoretical and neurobiologically plausible solution that enables inference and learning of the processes that generate sensory inputs (e.g., listening and understanding) and reproduction of those inputs (e.g., talking or singing), under multiple generative models. This is based on recent advances in theoretical neurobiology-namely, active inference and post hoc (online) Bayesian model selection. In brief, this scheme fits sensory inputs under each generative model. Model parameters are then updated in proportion to the probability that each model could have generated the input (i.e., model evidence). The proposed scheme is demonstrated using a series of (real zebra finch) birdsongs, where each song is generated by several different birds. The scheme is implemented using physiologically plausible models of birdsong production. We show that generalized Bayesian filtering, combined with model selection, leads to successful learning across generative models, each possessing different parameters. These results highlight the utility of having multiple internal models when making inferences in social environments with multiple sources of sensory information.
机译：为了展示社会智力，动物必须识别与之交流的人。进行这种推断的一种方法是在每个可能遇到的同种个体的内部生成模型中进行选择。但是，还必须通过某种形式的贝叶斯信念更新来学习这些模型。这引起了一个有趣的问题：当接收到由特定的特定物种产生的感觉输入时，动物如何知道要更新哪个内部模型？我们考虑一种理论上和神经生物学上可行的解决方案，该解决方案可以在多种生成模型下推理和学习生成感官输入（例如，听和理解）以及这些输入的再现（例如，说话或唱歌）的过程。这是基于理论神经生物学的最新进展，即主动推理和事后（在线）贝叶斯模型选择。简而言之，该方案适合每个生成模型下的感觉输入。然后与每个模型可能已经生成输入的概率（即模型证据）成比例地更新模型参数。使用一系列（真正的斑马雀科）鸟鸣声演示了所提出的方案，其中每首歌都是由几种不同的鸟鸣产生的。该计划是使用禽类生产的生理合理模型实施的。我们表明，广义贝叶斯滤波与模型选择相结合，可以成功地跨生成模型学习，每个生成模型具有不同的参数。这些结果突出了在具有多种感官信息来源的社交环境中进行推理时具有多个内部模型的实用性。

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  • 来源
    《Neural computation》 |2019年第12期|2390-2431|共42页
  • 作者

    Isomura Takuya; Parr Thomas; Friston Karl;

  • 作者单位

    RIKEN Ctr Brain Sci Lab Neural Computat & Adaptat Wako Saitama 3510198 Japan;

    UCL Inst Neurol Wellcome Ctr Human Neuroimaging London WC1N 3AR England;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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