Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task.

Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task.
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DOI:
10.1038/s41467-022-33581-6
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发表时间:
2022-10-04
影响因子:
16.6
通讯作者:
--
中科院分区:
综合性期刊1区
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--
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灵长类动物可以充分解析感官输入来推断潜在信息。据推测,这种能力依赖于建立外部世界的心理模型,并对这些模型进行心理模拟。然而,支持这一假设的证据仅限于不模拟神经计算的行为模型。在这里,我们通过直接比较灵长类动物(人类和猴子)在拦截球任务中的行为与一组具有或不具有动态跟踪潜在变量能力的递归神经网络(RNN)模型的行为来验证这一假设。人类和猴子表现出相似的行为模式。这种灵长类动物的行为模式最好被赋予动态推理的rnn捕获,这与灵长类动物大脑使用动态推理来支持灵活的物理预测的假设一致。此外,我们的工作强调了使用模型神经系统来测试高级脑功能的计算假设的一般策略。据推测,推断物理对象动态的能力依赖于对心理模型的运行模拟。在这里,作者通过在物理推理任务中将人类和猴子的行为与递归神经网络模型进行比较来验证这一假设。
Primates can richly parse sensory inputs to infer latent information. This ability is hypothesized to rely on establishing mental models of the external world and running mental simulations of those models. However, evidence supporting this hypothesis is limited to behavioral models that do not emulate neural computations. Here, we test this hypothesis by directly comparing the behavior of primates (humans and monkeys) in a ball interception task to that of a large set of recurrent neural network (RNN) models with or without the capacity to dynamically track the underlying latent variables. Humans and monkeys exhibit similar behavioral patterns. This primate behavioral pattern is best captured by RNNs endowed with dynamic inference, consistent with the hypothesis that the primate brain uses dynamic inferences to support flexible physical predictions. Moreover, our work highlights a general strategy for using model neural systems to test computational hypotheses of higher brain function. The ability to infer the dynamics of physical objects is hypothesized to rely on running simulations of mental models. Here, the authors test this hypothesis by comparing human and monkey behavior to recurrent neural network models in a physical inference task.
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发表时间: 2007-12-26
期刊: The Journal of neuroscience : the official journal of the Society for Neuroscience
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