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Chalk et al. hypothesize that biological cognitive agents learn a generative model of sensory input and rewards for actions.

Soltani and Wang only consider percepts and reward. They do not model any generative causes behind the two.

In Chalk et al.'s model, low-level sensory neurons are responsible for calculating the probabilities of high-level hidden variables given certain features being present or not. Other neurons are then responsible for predicting the rewards of different actions depending on the presumed state of those hidden variables.

In Chalk et al.'s model, neurons update their parameters online, ie. during the task. In one condition of their experiments, only neurons predicting reward are updated, in others, perceptual neurons are updated as well. Reward prediction was better when perceptual responses were tuned as well.

Cognitive science must not only provide predictive generative models predicting natural cognitive behavior within a normative framework, but also tie in these models with theories on how the necessary computations are realised.

In predictive coding, a model iterates the following steps:

  • assume values for latent variables,
  • predict sensory input (through a generative model),
  • observe prediction error,
  • adapt assumptions to minimize the error.

Given a generative model, it can be possible to find the most likely cause (or causes) of a sensation even if the causes interact in complex ways.

The goal of generative models is `to learn representations that are economical to describe but allow the input to be reconstructed accurately'.

Not sure how the goal of a generative model can be learning anything. However, it is clear that describing the parameters must require less information than describing the sensation.

Since combinatorial and sparse codes are known to be efficient, it would make sense if the states of generative models would usually be encoded in them.

That would preclude redundant population codes—except if we use the notion of redundancy in our idea of efficiency.