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SOMs can be used for preprocessing in reinforcement learning, simplifying their high-dimensional input via their winner-take-all characteristics.

However, since standard SOMs do not get any goal-dependent input, they focus on globally strongest features (statistically most predictive latent variables) and under-emphasize features which would be relevant for the task.

The model due to Weber and Triesch combines SOM- or K-Means-like learning of features with prediction error feedback as in reinforcement learning. The model is thus able to learn relevant and disregard irrelevant features.

Weber and Triesch's model learns task-relevant features.

However, a brain region like the SC, which serves a very general task, cannot specialize in one task—it has to serve all goals that the system has.

It therefore should change its behavior depending on the task. Attention is one mechanism which might determine how to change behavior in a given situation.

If the goal is predictive of the input, then a purely unsupervised algorithm could take a representation of the goal as just another input.