A predictive processing framework for joint action and communication

Humans act together to achieve feats they could never achieve alone and communicate to ensure alignment of meaning and understanding across different individuals. Explaining the unique human joint action and communication abilities poses an enormous challenge because it requires a systematic account of how people go beyond their own individual perceptions, thoughts, and needs to achieve joint outcomes and align their understanding. Here, we advance a new theoretical framework for explaining joint action and communication. It builds upon influential predictive processing architectures, extending them from individual cognition to multiagent, interactive settings. We assume that joint action and communication involve using and updating agent-neutral models that enable co-agents to predict collective outcomes of interactions regardless of who achieved them. This contrasts with previous frameworks postulating that agent-specific models predict action outcomes for self and others. We discuss three key claims derived from our framework: 1) Co-agents use agent-neutral predictive frameworks during joint action; 2) Co-agents update agent-neutral models interactively by shaping others' predictions through verbal and non-verbal communication; and 3) Agent-neutral models enable dynamic role allocation during joint action. We highlight how these three claims stem from our proposal, what evidence currently favors or disfavors them, and what novel experiments could be conducted to test them further. Our agent-neutral predictive processing framework will provide a new perspective for understanding the individual basis of human sociality, which closely links theories of joint action and communication to principles of computational neuroscience.