Turn-Taking Mechanisms in Imitative Interaction: Robotic Social Interaction Based on the Free Energy Principle

Abstract

This study explains how the leader-follower relationship and turn-taking could develop in a dyadic imitative interaction by conducting robotic simulation experiments based on the free energy principle. Our prior study showed that introducing a parameter during the model training phase can determine leader and follower roles for subsequent imitative interactions. The parameter is defined as w, the so-called meta-prior, and is a weighting factor used to regulate the complexity term versus the accuracy term when minimizing the free energy. This can be read as sensory attenuation, in which the robot’s prior beliefs about action are less sensitive to sensory evidence. The current extended study examines the possibility that the leader-follower relationship shifts depending on changes in w during the interaction phase. We identified a phase space structure with three distinct types of behavioral coordination using comprehensive simulation experiments with sweeps of w of both robots during the interaction. Ignoring behavior in which the robots follow their own intention was observed in the region in which both ws were set to large values. One robot leading, followed by the other robot was observed when one w was set larger and the other was set smaller. Spontaneous, random turn-taking between the leader and the follower was observed when both ws were set at smaller or intermediate values. Finally, we examined a case of slowly oscillating w in anti-phase between the two agents during the interaction. The simulation experiment resulted in turn-taking in which the leader-follower relationship switched during determined sequences, accompanied by periodic shifts of ws. An analysis using transfer entropy found that the direction of information flow between the two agents also shifted along with turn-taking. Herein, we discuss qualitative differences between random/spontaneous turn-taking and agreed-upon sequential turn-taking by reviewing both synthetic and empirical studies.

Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy

“…For example, some studies focusing on hierarchical representations did not assume sequential data because of using a variational auto-encoder (78,85,86) and did not use stochastic dynamics in RNN (42). Although there is research investigating internal representation using PV-RNN, the previous studies used lower-order probability (e.g., target state and signal noise) and did not consider explicitly higher-order probabilistic variables such as transition bias (55)(56)(57)(58)(59)(60). The current result showing that artificial neural network models can acquire hierarchical Bayesian representations in a selforganizing manner is a crucial step to understanding underlying mechanisms for embedding the hierarchical Bayesian model into the brain system through developmental learning.…”

“…Therefore, PV-RNN can be considered a powerful tool for investigating the Bayesian brain hypothesis. Indeed, PV-RNN was useful for modeling uncertainty estimations (55), goaloriented behavior (56), sensory attenuation (57), and social interaction (58)(59)(60).…”

Synergizing habits and goals with variational Bayes

“…For example, if you have experience with a particular object, your brain will use that experience to generate predictions about what the object should look like, and these predictions will rapidly adjust if the object changes in some way (e.g., if it moves or changes color). By rapidly updating its internal models of the world in this way, the brain can maintain a stable and accurate representation of the environment (Ahmadi & Tani, 2019;Wirkuttis et al, 2023).…”

“…Active inference (AIf) (Friston et al, 2016(Friston et al, , 2017 has recently been combined with neural networks (a.k.a. deep AIf) to solve more challenging tasks including simulated decision problems (Ueltzhöffer, 2018;Millidge, 2020;Fountas et al, 2020;Mazzaglia et al, 2021) and planning with real robots (Ahmadi & Tani, 2019;QueiSSer et al, 2021;Matsumoto et al, 2022;Wirkuttis et al, 2023).…”

Bio-Inspired Intelligent Systems: Negotiations between Minimum Manifest Task Entropy and Maximum Latent System Entropy in Changing Environments

“…In particular, this paper introduces a new perspective of how first principles can be applied in systems that encompass natural and artificial intelligence. This new perspective goes beyond the state-of-the-art of formulations based on first principles and entropy dynamics [23][24][25][26].…”