Error signals


Again, more of the Firth podcast (here) that was the subject of the last few posts.

It’s interesting that engineers have a very different way of looking at the world than psychologists. Psychologists tend to have a loop which says there’s perception, signals come in about the world, you interpret them, and then you act. The perception is the input and the act is the output. Engineers look at it completely the other way around. They say you act upon the world, you put something into the world—that’s the input, is acting upon the world. And then something happens—which is the output—that enables you to decide what to do next.

And I think this captures this much more active way of thinking about the world which engineers have. Whereas the more passive view of psychologists where somehow you have a perception which you can somehow work out what’s going on, it’s very much the other way around. We have to act in order to create—to make the world send us back information which helps us to interpret what it is.

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The dopamine signal is a prediction error. So, basically if something unexpectedly nice happens, then you get a shot of dopamine; and so, the dopamine neurons become more active. And if you expect something nice to happen and it does happen, there’s no response; because there’s not an error. If we expect it to happen and it doesn’t happen, then the activity goes down. So, that’s a negative error.

There may be a another way to look at this – a feedback loop does not have a beginning and a end. It is circular. Three components are interacting: the sensory data, the predictive model, and the action commands. Start with the sensory data – the data arrives, it is compared with the model, where it does not match it forces a change to the action commands. OR- Start with the action commands – the commands are given, they are used to create a predictive model of what will happen, the model is compared with the resulting sensory data, where it doesn’t match it results in changes the action commands. OR – Start with the model – keep it accurate by fine-tuning the action/prediction side and the sensing/perception side so that they match. The problem of how to understand a feedback loop is classic and there are good engineering formulas covering the subject (think op-amps, servo mechanisms and the like).

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