Abstract

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.

Highlights

  • The sense of agency(SoA) is the subjective awareness about “the experience of controlling one’s own motor acts and, through them, the course of external events” (Haggard, 2017)

  • In the first half of this paper, we describe a systematic derivation for the proposed mathematical model of the SoA on the basis of the comparator model, which is related to the prospective generative process of the SoA

  • We propose an indicator that can be used to test the hypothesis obtained from the proposed mathematical model, and we analyze the gradual changes of the perception of the SoA during the task

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Summary

INTRODUCTION

The sense of agency(SoA) is the subjective awareness about “the experience of controlling one’s own motor acts and, through them, the course of external events” (Haggard, 2017). With deep insight (McClelland, 2009) While this approach would definitely help the SoA studies, to the best of our knowledge, very few studies have proposed a mathematical formulation of the generative process of the SoA except for the work by Moore and Fletcher (2012) and Legaspi and Toyoizumi (2019). Our objective in performing the experiments was to measure the degree of the SoA that was gradually changed during the task and to determine whether this was caused by the Bayesian inference or the SGD algorithm It had been reported by previous studies that the SoA adapts over several repeated experiences (Leotti et al, 2015), there are only a few studies that clarify the mechanism behind this adaptation (Legaspi and Toyoizumi, 2019; Di Plinio et al, 2020). The differences between these terms are described

Likelihood Value as the Sense of Agency
Online Learning Algorithms
Scientifically Testable Hypothesis for the SoA Attribution Task
Learning Dynamics of the SGD-type Subject
Experimental Protocol to Quantify the Sense of Agency
Participants
Statistical Analysis
Learning Curve Analysis
RESULTS
DISCUSSION
ETHICS STATEMENT
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