Investigation of the Sense of Agency in Social Cognition, Based on
Frameworks of Predictive Coding and Active Inference:
A simulation Study on Multimodal Imitative Interaction
Wataru Ohata and Jun Tani
Cognitive Neurorobotics Research Unit (contact: wataru.ohata@oist.jp)

ABSTRACT
When agents interact socially, conflicts are inevitable. Although how social agents can resolve such problems autonomously has not been
determined, dynamic characteristics of agency may cast light on the underlying mechanism. In this work, we employed predictive coding andactive inference as theoretical frameworks and studied the sense of agency (SoA) in social interaction. Hypothesizing that complexity regulationof agent’s model should affect the strength of the sense of agency and the interaction, we evaluated it by conducting simulation experiments ofmultimodal imitative interaction between a humanoid robot and a human. The results showed that with tight regulation, the agent showed a weakSoA, and with loose regulation, the agent exerted a strong SoA[1].
Keywords: sense of agency, predictive coding, active inference, multimodal perception, human-robot interaction, recurrent neural network, variational Bayes

MOTIVATIONEXPERIMENTAL DESIGN

In social interactions,- humans sometimes cooperate while at other times conflic.
What determines the type of interaction and how?
- how their agency is characterized?

The robot and the human imitate each other following a probabilistic finite
state machine (PFSM) in simulation.

- Composed of two states- Transition with three primitive movements(A, B, and C)- Deterministic and probabilistic transision

APPROACH AND HYPOTHESIS:
Theoretical framework - Predictive Coding for perception- Active Inference for action generation

Figure 3. A diagram of a probabilistic
finite state machine

An example of visuo-proprioceptive
sequence based on the PFSM

Perception is achieved by maximizing the evidence lower bound(ELBO) of generative model (a.k.a. free-energy minimization)(𝑞(#) is approximate posterior)

HYPOTHESIS:
Regulation of the complexity should- affect the strength of the agent’s sense of agency (SoA)- have a significant impact on social interaction- Top row: joint angle trajectories- Bottom row: latent
representation of visual images
Figure 4. An example of visuo-proprioceptive sequence.

When the robot and the human make different predictions at theprobabilistic state transition, for example, B for the robot and C for
the human:BB

The feeling that “I am the one gererating this action” basedon the match between the agent’s intention in acting andthe outcome
MODEL
Imitative interaction between a humanoid robot and a human

Figure 1. A schematic of the cycle of prediction withconditional prior and posterior inference

Characteristics:- Variational Bayes Recurrent
Neural Network (PV-RNN[2])- Multimodal§ Vision (CNN-based)§ Proprioception (joint angle)- Perception and action
generation through the cycle of§ Prediction with prior§ Posterior inference

Online posteior inference by iterative ELBO maximization
using back-propagation through time

CStrong SoA

Keep its intention
C

Weak SoA

Modify its intention

RESULTS
Simulation results with different conditions: tight and loose regulation
ATIGHT regulation:-B

Roconstruction error was minimized
well
Prediction was modified accordingly

LOOSE regulation:-Roconstruction error was NOT
minimized well
Prediction was NOT modified much

Figure 5. A comparison of tight regulation (A) and loose regulation (B). Reconstruction of the past observation and the future prediction
at time step 150 (top) and at time step 180 (bottom). Solid lines represent prediction outputs, and dashed lines represent observation.
The shadowed area indicates the window for the posterior inference.

CONCLUSION AND FUTURE WORKS-TIGHT complexity regulation → STRONG sense of agency
LOOSE complexity regulation → WEAK sense of agency
Next step is scaling up to physical experiments.
Dynamic change of SoA leads to turn-takings in social interaction?

REFERENCES
Figure 2. A schematic of posterior inference. At each sensory time step the network iterates (A) forward computation and (B) posterior
update inside a window for the immediate past using back-propgation through time such that the ELBO is maximazed. At next time step,
the window shifts (C) and the above iteration repeats.

[1] Ohata, W., & Tani, J. (2020). Investigation of the Sense of Agency in Social Cognition, based on frameworks of
Predictive Coding and Active Inference: A simulation study on multimodal imitative interaction. Frontiers inNeurorobotics.
[2] Ahmadi, A., & Tani, J. (2019). A novel predictive-coding-inspired variational rnn model for online prediction andrecognition. Neural computation, 31(11), 2025-2074.