I Hear My Voice; Therefore I Spoke: The Sense of Agency Over Speech Is Enhanced by Hearing One's Own Voice

Control over self and others' face: exploitation and exploration

The face serves as a crucial cue for self-identification, while the sense of agency plays a significant role in determining our influence through actions in the environment. The current study investigates how self-identification through facial recognition may influence the perception of control via motion. We propose that self-identification might engender a belief in having control over one's own face, leading to a more acute detection and greater emphasis on discrepancies between their actions and the sensory feedback in control judgments. We refer to the condition governed by the belief in having control as the exploitation mode. Conversely, when manipulating another individual's face, the belief in personal control is absent. In such cases, individuals are likely to rely on the regularity between actions and sensory input for control judgments, exhibiting behaviors that are exploratory in nature to glean such information. This condition is termed the explorative mode. The study utilized a face-motion mixing paradigm, employing a deep generative model to enable participants to interact with either their own or another person's face through facial and head movements. During the experiment, participants observed either their own face or someone else's face (self-face vs. other-face) on the screen. The motion of the face was driven either purely by their own facial and head motion or by an average of the participant's and the experimenter's motion (full control vs. partial control). The results showed that participants reported a higher sense of agency over the other-face than the self-face, while their self-identification rating was significantly higher for the self-face. More importantly, controlling someone else's face resulted in more movement diversity than controlling one's own face. These findings support our exploration-exploitation theory: When participants had a strong belief in control triggered by the self-face, they became highly sensitive to any sensorimotor prediction errors, leading to a lower sense of agency. In contrast, when the belief of control was absent, the exploration mode triggered more explorative behaviors, allowing participants to efficiently gather information to establish a sense of agency.

Evaluating the Bayesian causal inference model of intentional binding through computational modeling

Intentional binding refers to the subjective compression of the time interval between an action and its consequence. While intentional binding has been widely used as a proxy for the sense of agency, its underlying mechanism has been largely veiled. Bayesian causal inference (BCI) has gained attention as a potential explanation, but currently lacks sufficient empirical support. Thus, this study implemented various computational models to describe the possible mechanisms of intentional binding, fitted them to individual observed data, and quantitatively evaluated their performance. The BCI models successfully isolated the parameters that potentially contributed to intentional binding (i.e., causal belief and temporal prediction) and generally better explained an observer's time estimation than traditional models such as maximum likelihood estimation. The estimated parameter values suggested that the time compression resulted from an expectation that the actions would immediately cause sensory outcomes. Furthermore, I investigated the algorithm that realized this BCI and found probability-matching to be a plausible candidate; people might heuristically reconstruct event timing depending on causal uncertainty rather than optimally integrating causal and temporal posteriors. The evidence demonstrated the utility of computational modeling to investigate how humans infer the causal and temporal structures of events and individual differences in that process.