I See What You Mean: How Attentional Selection Is Shaped by Ascribing Intentions to Others

Abstract

The ability to understand and predict others’ behavior is essential for successful interactions. When making predictions about what other humans will do, we treat them as intentional systems and adopt the intentional stance, i.e., refer to their mental states such as desires and intentions. In the present experiments, we investigated whether the mere belief that the observed agent is an intentional system influences basic social attention mechanisms. We presented pictures of a human and a robot face in a gaze cuing paradigm and manipulated the likelihood of adopting the intentional stance by instruction: in some conditions, participants were told that they were observing a human or a robot, in others, that they were observing a human-like mannequin or a robot whose eyes were controlled by a human. In conditions in which participants were made to believe they were observing human behavior (intentional stance likely) gaze cuing effects were significantly larger as compared to conditions when adopting the intentional stance was less likely. This effect was independent of whether a human or a robot face was presented. Therefore, we conclude that adopting the intentional stance when observing others’ behavior fundamentally influences basic mechanisms of social attention. The present results provide striking evidence that high-level cognitive processes, such as beliefs, modulate bottom-up mechanisms of attentional selection in a top-down manner.

On the Imitation of Goal Directed Movements of a Humanoid Robot

“…According to Wykowska et al [6] an intentional stance is required before people can attribute mental states to agents, and it was shown that beliefs about agency affect the effectiveness of a gaze cue [4,6]. The effects of gaze timing on the onset of imitation in our study are much larger than those observed using a gaze cueing paradigm, which is about 15 ms [4].…”

“…Therefore, as emphasized by Scassellati [3] and other researchers [4][5][6], it is crucial to focus on how people naturally adopt an 'intentional stance' and interpret the behaviour of the robot as if it possesses goals, intentions, and beliefs.…”

Integrating human observer inferences into robot motion planning

“…Here, the robot can learn a C that explains the demonstrations [4], using tools like Inverse Optimal Control (IOC) [1,44,60]. However, extending these tools to higher dimensions is an open problem [44], and recent work focuses on learning costs that make the demonstrations locally optimal [32,38], or on restricting the space of trajectories to one in which optimization is tractable [30].…”

“…Following [60], we assume trajectories are separable, i.e. P(ξ X→Y→Z ) = P(ξ X→Y )P(ξ Y→Z ), giving us:…”

“…C still possible, but exponentially less probable, i.e. P(ξ) ∝ exp −C[ξ] , adopting the principle of maximum entropy [60]. With this, (8) becomes:…”