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首页> 美国卫生研究院文献>eNeuro >Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other’s Actions by Humans
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Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other’s Actions by Humans

机译：自我行动估计与人类对他人行动的预测中的共同机制

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The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants’ ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert’s abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert’s self-estimation is explained only by considering a change in the individual’s forward model, showing that an improvement in an expert’s ability to predict outcomes of observed actions affects the individual’s forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions.
机译：人类如何预测他人观察到的运动行为的结果这一问题是认知和社会神经科学中的一个基本问题。先前的理论研究表明，大脑使用正向模型的一部分（用于估计自我生成的动作的感觉结果）来预测观察到的动作的结果。但是，由于缺乏直接的实验证据，该假设仍存在争议。为了解决这个问题，我们在理解学习范式中分析了飞镖专家的行为，并利用计算模型来检验观察到的动作的结果预测如何影响参与者估计自己动作的能力。我们之所以招募飞镖专家，是因为众所周知，体育专家能够准确估计自己的动作，并能预测他人的动作。我们首先表明，学习预测观察到的飞镖投掷的结果会降低专家产生自己的飞镖动作并估算自己投掷的结果（或自我估计）的能力。接下来，我们引入一个状态空间模型，通过我们的实验来解释飞镖性能和自我评估的逐次尝试变化。基于模型的分析表明，仅通过考虑个体的前瞻模型的变化来解释专家的自我估计的变化，这表明专家对观察到的动作的结果进行预测的能力的提高会影响个体的前瞻模型。这些结果表明，同一正向模型的各个部分已在人类中用于估计自我生成动作的结果和预测观察到的动作的结果。

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