In cognitive science, Theory of Mind (ToM) is the mental faculty of assessing intentions and beliefs of others and requires, in part, to distinguish incoming sensorimotor signals and accordingly attribute these to either the self-model, the model of the other, or one pertaining to the external world, including inanimate objects. To gain an understanding of this mechanism, we perform a computational analysis of sensorimotor interactions in a dual-arm robotic setup. Our main contribution is that, under the common fate principle, a correlation analysis of the velocities of visual pivots is shown to be sufficient to characterize "the self" (including proximo-distal arm-joint dependencies) and assess motor to sensory influences, and "the other" by computing clusters in the correlation dependency graph. A correlational analysis, however, is not sufficient to assess the non-symmetric/directed dependencies required to infer autonomy, the ability of entities to move by themselves. We subsequently validate 3 measures that can potentially quantify a metric for autonomy: Granger Causality, Transfer Entropy, as well as a novel `Acceleration Transfer' (AT) measure, which is an instantaneous measure that computes the estimated instantaneous transfer of acceleration between visual features, from which one can compute a directed sensorimotor graph. Subsequently, autonomy is characterized by the sink nodes in this directed graph. Our results show that although Transfer Entropy is able to capture the directional dependencies, a rectified subtraction operation denoted here as AT is both sufficient and computationally cheaper.

中文翻译：

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从视觉反馈中区分自我、他人和自主：相关性和加速度传递组合分析


在认知科学中，心智理论 (ToM) 是评估他人意图和信念的心理能力，部分需要区分传入的感觉运动信号，并相应地将这些信号归因于自我模型、他人模型或一种与外部世界有关，包括无生命的物体。为了了解这种机制，我们对双臂机器人设置中的感觉运动相互作用进行了计算分析。我们的主要贡献是，在共同命运原则下，视觉枢轴速度的相关分析足以表征“自我”（包括近远端手臂关节依赖性）并评估运动对感觉的影响，以及通过计算相关依赖图中的簇来“另一个”。然而，相关分析不足以评估推断自主性（实体自行移动的能力）所需的非对称/定向依赖性。随后，我们验证了 3 种可能量化自主性指标的度量：格兰杰因果关系、传递熵，以及一种新颖的“加速度传递”（AT）度量，这是一种瞬时度量，用于计算视觉特征之间加速度的估计瞬时传递，从中可以计算出有向感觉运动图。随后，自治性由该有向图中的汇聚节点来表征。我们的结果表明，虽然传递熵能够捕获方向依赖性，但此处表示为 AT 的修正减法运算既足够又计算成本更低。