Abstract
Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others – during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions – both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then – in principle – they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa.
Highlights
The term hermeneutics refers to the art of interpreting written texts such as holy scriptures
The treatment above builds on the ideas introduced by (Friston and Frith, 2015); namely, that generalised synchrony e or synchronisation of chaos e provides a formal metaphor for communication and is a natural consequence of active inference
The contribution of the current paper is to show that the same principle, explains the convergent evolution of hierarchical models that generate mutually sympathetic predictions
Summary
The term hermeneutics refers to the art of interpreting written texts such as holy scriptures. The key point here is that the same principle that leads to generalised synchrony applies to the selection or learning of the model generating predictions This learning is the focus of the current paper, which provides an illustrative proof of principle that the hermeneutic cycle can be closed by updating generative models and their predictions to minimise prediction errors. We will see that perceptual learning produces a convergence of the two generative models over time e leading to the emergence of generalised synchrony and implicit communication We offer this as a solution to the problem of hermeneutic inference that can be resolved by neuronally plausible schemes e with the single imperative to minimise prediction error or free energy
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