TY - JOUR
T1 - Statistical Learning Model of the Sense of Agency
AU - Yano, Shiro
AU - Hayashi, Yoshikatsu
AU - Murata, Yuki
AU - Imamizu, Hiroshi
AU - Maeda, Takaki
AU - Kondo, Toshiyuki
N1 - Funding Information:
Funding. This work was partially supported by JSPS KAKENHI, Grant-in-Aid for Scientific Research on Innovative Areas Understanding brain plasticity on body representations to promote their adaptive functions (grant number 26120005). This research was partially supported by JSPS KAKENHI (grant numbers JP17K12737, 19H05725, and 18H01098).
Funding Information:
This work was partially supported by JSPS KAKENHI, Grant-in-Aid for Scientific Research on Innovative Areas Understanding
Publisher Copyright:
© Copyright © 2020 Yano, Hayashi, Murata, Imamizu, Maeda and Kondo.
PY - 2020/10/14
Y1 - 2020/10/14
N2 - A sense of agency (SoA) is the experience of subjective awareness regarding the control of one's actions. Humans have a natural tendency to generate prediction models of the environment and adapt their models according to changes in the environment. The SoA is associated with the degree of the adaptation of the prediction models, e.g., insufficient adaptation causes low predictability and lowers the SoA over the environment. Thus, identifying the mechanisms behind the adaptation process of a prediction model related to the SoA is essential for understanding the generative process of the SoA. In the first half of the current study, we constructed a mathematical model in which the SoA represents a likelihood value for a given observation (sensory feedback) in a prediction model of the environment and in which the prediction model is updated according to the likelihood value. From our mathematical model, we theoretically derived a testable hypothesis that the prediction model is updated according to a Bayesian rule or a stochastic gradient. In the second half of our study, we focused on the experimental examination of this hypothesis. In our experiment, human subjects were repeatedly asked to observe a moving square on a computer screen and press a button after a beep sound. The button press resulted in an abrupt jump of the moving square on the screen. Experiencing the various stochastic time intervals between the action execution (button-press) and the consequent event (square jumping) caused gradual changes in the subjects' degree of their SoA. By comparing the above theoretical hypothesis with the experimental results, we concluded that the update (adaptation) rule of the prediction model based on the SoA is better described by a Bayesian update than by a stochastic gradient descent.
AB - A sense of agency (SoA) is the experience of subjective awareness regarding the control of one's actions. Humans have a natural tendency to generate prediction models of the environment and adapt their models according to changes in the environment. The SoA is associated with the degree of the adaptation of the prediction models, e.g., insufficient adaptation causes low predictability and lowers the SoA over the environment. Thus, identifying the mechanisms behind the adaptation process of a prediction model related to the SoA is essential for understanding the generative process of the SoA. In the first half of the current study, we constructed a mathematical model in which the SoA represents a likelihood value for a given observation (sensory feedback) in a prediction model of the environment and in which the prediction model is updated according to the likelihood value. From our mathematical model, we theoretically derived a testable hypothesis that the prediction model is updated according to a Bayesian rule or a stochastic gradient. In the second half of our study, we focused on the experimental examination of this hypothesis. In our experiment, human subjects were repeatedly asked to observe a moving square on a computer screen and press a button after a beep sound. The button press resulted in an abrupt jump of the moving square on the screen. Experiencing the various stochastic time intervals between the action execution (button-press) and the consequent event (square jumping) caused gradual changes in the subjects' degree of their SoA. By comparing the above theoretical hypothesis with the experimental results, we concluded that the update (adaptation) rule of the prediction model based on the SoA is better described by a Bayesian update than by a stochastic gradient descent.
KW - Bayes' rule
KW - online learning
KW - sense of agency
KW - statistical learning
KW - stochastic gradient descent
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U2 - 10.3389/fpsyg.2020.539957
DO - 10.3389/fpsyg.2020.539957
M3 - Article
AN - SCOPUS:85094965323
VL - 11
JO - Frontiers in Psychology
JF - Frontiers in Psychology
SN - 1664-1078
M1 - 539957
ER -