Investigation of the Sense of Agency in Social Cognition, Based on Frameworks of Predictive Coding and Active Inference: A Simulation Study on Multimodal Imitative Interaction.
Investigation of the Sense of Agency in Social Cognition, Based on Frameworks of Predictive Coding and Active Inference: A Simulation Study on Multimodal Imitative Interaction.
复制标题
DOI：
10.3389/fnbot.2020.00061
复制
发表时间：
2020
影响因子：
3.1
通讯作者：
Tani J
中科院分区：
文献类型：
--
作者：
Ohata W;Tani J
当代理人以不同的意图（或意愿）进行社会互动时，冲突是难以避免的。虽然社会主体自主解决这些问题的手段尚未确定，但代理的动态特征可能会揭示潜在的机制。因此，目前的研究集中在代理感上，代理感是代理的一个特定方面，指的是行为主体的行为意图与结果之间的一致性，特别是在社会互动环境中。采用预测编码和主动推理作为感知和行动生成的理论框架，我们假设智能体模型证据下界的复杂性调节会影响智能体的代理感强度，并对社会互动产生重大影响。为了验证这一假设，我们利用变分贝叶斯递归神经网络建立了机器人与人通过视觉本体感觉进行模仿交互的计算模型，并利用记录的人体运动数据作为模拟交互的对应物，以伪模仿交互的形式模拟了该模型。该模型的一个关键特征是，可以通过改变分配给模型的每个局部模块的超参数的值来不同地调节每种模态的复杂性。我们首先寻找一个最优的超参数设置，使模型具有适当的多模态感觉协调。这些研究表明，由于视觉信息流的不确定性更大，视觉模块的复杂性应该比本体感觉模块的复杂性受到更严格的调节。我们使用这个最优训练模型作为默认模型，研究了在训练后改变整个网络中复杂性调节的紧密程度如何影响模仿交互过程中代理感的强度。结果表明，随着对复杂性监管的放松，一个主体倾向于以自我为中心，而不去适应另一个主体。相反，在更严格的监管下，代理人倾向于通过调整其意图来跟随对方。我们得出结论，复杂性调控的严密性显著影响社会环境中主体意识的强度和主体间互动的动态。
When agents interact socially with different intentions (or wills), conflicts are difficult to avoid. Although the means by which social agents can resolve such problems autonomously has not been determined, dynamic characteristics of agency may shed light on underlying mechanisms. Therefore, the current study focused on the sense of agency, a specific aspect of agency referring to congruence between the agent's intention in acting and the outcome, especially in social interaction contexts. Employing predictive coding and active inference as theoretical frameworks of perception and action generation, we hypothesize that regulation of complexity in the evidence lower bound of an agent's model should affect the strength of the agent's sense of agency and should have a significant impact on social interactions. To evaluate this hypothesis, we built a computational model of imitative interaction between a robot and a human via visuo-proprioceptive sensation with a variational Bayes recurrent neural network, and simulated the model in the form of pseudo-imitative interaction using recorded human body movement data, which serve as the counterpart in the interactions. A key feature of the model is that the complexity of each modality can be regulated differently by changing the values of a hyperparameter assigned to each local module of the model. We first searched for an optimal setting of hyperparameters that endow the model with appropriate coordination of multimodal sensation. These searches revealed that complexity of the vision module should be more tightly regulated than that of the proprioception module because of greater uncertainty in visual information flow. Using this optimally trained model as a default model, we investigated how changing the tightness of complexity regulation in the entire network after training affects the strength of the sense of agency during imitative interactions. The results showed that with looser regulation of complexity, an agent tends to act more egocentrically, without adapting to the other. In contrast, with tighter regulation, the agent tends to follow the other by adjusting its intention. We conclude that the tightness of complexity regulation significantly affects the strength of the sense of agency and the dynamics of interactions between agents in social settings.
登录
查看更多内容
影响因子：
4.7
作者：
Boucenna, Sofiane;Gaussier, Philippe;Hafemeister, Laurence
通讯作者：
Hafemeister, Laurence
影响因子：
1.1
作者：
Huys, R;Daffertshofer, A;Beek, PJ
通讯作者：
Beek, PJ
DOI：
10.1098/rstb.2005.1622
发表时间：
2005-04-29
影响因子：
6.3
作者：
Friston, KJ
通讯作者：
Friston, KJ
影响因子：
1.9
作者：
KAWATO, M;FURUKAWA, K;SUZUKI, R
通讯作者：
SUZUKI, R
影响因子：
3.7
作者：
Friston KJ;Daunizeau J;Kiebel SJ
通讯作者：
Kiebel SJ