> This gestalt isn't just for biological organisms, but any system for which its decision making engages with representations of the external environment unified with a self-representation to form a coherent representation of a persistent entity engaged with an external world.

This doesn't seem quite right, or at least underspecified. We can talk about this stuff concretely these days, at least in the context of digital systems. E.g. i can draw up a diagram of a system that takes in some camera and audio data (and tactile, proprioceptive, etc.), tokenizes it then runs that + past state data through some autoregressive VLM to drive an inference process. The state being passed around can be written out analytically for a given trained model - the external and internal environmental representations, the linear algebra that transforms them into latent action representations, the process by which that is transformed into control signals. It seems difficult to claim that the computational process that implements this has any more or less of a gestalt then one multiplying two matrices together. So it's not just the existence of certain representations or computational loops that seems to lead to possessing a gestalt.

> It seems difficult to claim that the computational process that implements this has any more or less of a gestalt then one multiplying two matrices together. So it's not just the existence of certain representations or computational loops that seems to lead to possessing a gestalt.

I've thought a lot about what is lacking in modern VLMs that preclude consciousness. In my view the difference is that their talk of "self" is a simulacrum of the real thing. Current models are feed forward and so self-talk is driven by some parameter that turns on when the network detects context that possibly references the model, and this parameter drives downstream self-talk. It's a very good simulacrum, but it is a far cry from a model with recurrent self-reference around which the inference process is organized. The richness of the self-model in a hypothetical recurrent network with capabilities of modern LMs is much greater than the parameter on/off representation in feed forward networks.