Donnarumma Francesco, Dindo Haris, Pezzulo Giovanni
Institute of Cognitive Sciences and Technologies, National Research Council Rome, Italy.
Computer Science Engineering, University of Palermo Palermo, Italy.
Front Psychol. 2017 Feb 23;8:237. doi: 10.3389/fpsyg.2017.00237. eCollection 2017.
Humans excel at recognizing (or inferring) another's distal intentions, and recent experiments suggest that this may be possible using only subtle kinematic cues elicited during early phases of movement. Still, the cognitive and computational mechanisms underlying the recognition of intentional (sequential) actions are incompletely known and it is unclear whether kinematic cues alone are sufficient for this task, or if it instead requires additional mechanisms (e.g., prior information) that may be more difficult to fully characterize in empirical studies. Here we present a computationally-guided analysis of the execution and recognition of intentional actions that is rooted in theories of motor control and the coarticulation of sequential actions. In our simulations, when a performer agent coarticulates two successive actions in an action sequence (e.g., "reach-to-grasp" a bottle and "grasp-to-pour"), he automatically produces kinematic cues that an observer agent can reliably use to recognize the performer's intention early on, during the execution of the first part of the sequence. This analysis lends computational-level support for the idea that kinematic cues may be sufficiently informative for early intention recognition. Furthermore, it suggests that the social benefits of coarticulation may be a byproduct of a fundamental imperative to optimize sequential actions. Finally, we discuss possible ways a performer agent may combine automatic (coarticulation) and strategic (signaling) ways to facilitate, or hinder, an observer's action recognition processes.
人类擅长识别（或推断）他人的远期意图，最近的实验表明，仅通过运动早期阶段产生的细微运动学线索就可能做到这一点。然而，对于有意（连续）动作识别背后的认知和计算机制，我们还不完全清楚，目前尚不清楚仅靠运动学线索是否足以完成这项任务，或者是否还需要其他机制（例如先验信息），而这些机制在实证研究中可能更难完全描述清楚。在此，我们基于运动控制理论和连续动作的协同发音，对有意动作的执行和识别进行了计算引导分析。在我们的模拟中，当一个执行主体在一个动作序列中协同发音两个连续动作时（例如，“伸手去抓”一个瓶子和“抓着去倒”），他会自动产生运动学线索，观察者主体可以在序列第一部分的执行过程中，早期就可靠地利用这些线索来识别执行者的意图。这一分析为运动学线索对于早期意图识别可能具有足够信息这一观点提供了计算层面的支持。此外，它还表明协同发音的社会益处可能是优化连续动作这一基本需求的副产品。最后，我们讨论了执行主体可能结合自动（协同发音）和策略（信号传递）方式来促进或阻碍观察者动作识别过程的可能方式。
