PP tells us there are three ways you make you predictions match sensory input: 1. Change your underlying models and their predictions based on what you see. 2. Change your perception to fit with what you predicted. 3. Act on the world to bring the two into alignment.
I would clarify that #1 and #2 happen together. Given a large difference between prediction and observation, a confident prediction somewhat overwrites the perception (which helps us deal with noisy data), but the prediction is weakened, too.
And #3 is, of course, something I argued against in my other reply.
You meet cyan skinned people. If they’re blunt, you perceive that as nastiness. If they’re tactful, you perceive that as dishonesty. You literally see facial twitches and hear notes that aren’t there, PP making confirmation bias propagate all the way down to your basic senses.
Right, this makes sense.
If they’re actually nice, your brain gets a prediction error signal and tries to correct it with action. You taunt to provoke nastiness, or become intimidating to provoke dishonesty. You grow ever more confident in your excellent intuition with regards to those cyan bastards.
Why do you believe this?
I can believe that, in social circumstances, people act so as to make their predictions get confirmed, because this is important to group status. For example, (subconsciously) socially engineering a situation where the cyan-skinned person is trapped in a catch 22, where no matter what they do, you’ll be able to fit it into your narrative.
What I don’t believe in is a general mechanism whereby you act so as to confirm your predictions.
I already stated several reasons in my other comment. First, this does not follow easily from the bayes-net-like mechanisms of perceptual PP theory. They minimize prediction error in a totally different sense, reactively weakening parts of models which resulted in poor predictions, and strengthening models which had strong predictions. This offers no mechanism by which actions would be optimized in a way such that we proactively minimize prediction error thru our actions.
Second, it doesn’t fit, by and large, with human behavior. Humans are curious infovores; a better model would be that we actively plan to maximize prediction error, seeking out novel stimulus by steering toward parts of the state-space where our current predictive ability is poor. (Both of these models are poor, but the information-loving model is better.) Give a human a random doodad and they’ll fiddle with it by doing things to see what will happen. I think people make a sign error, thinking PP predicts info-loving behavior because this maximizes learning, which intuitively might sound like minimizing prediction error. But it’s quite the opposite: maximizing learning means planning to maximize prediction error.
Third, the activity of any highly competent agent will naturally be highly predictable to that agent, so it’s easy to think that it’s “minimizing prediction error” by following probable lines of action. This explains away a lot of examples of “minimizing prediction error”, in that we don’t need to posit any separate mechanism to explain what’s going on. A highly competent agent isn’t necessarily actively minimizing prediction error, just because it’s managed to steer things into a predictable state. It’s got other goals.
Furthermore, anything which attempts to maintain any kind of homeostasis will express behaviors which can naturally be described as “reducing errors”—we put on a sweater when it’s too cold, take it off when it’s too hot, etc. If we’re any good at maintaining our homeostasis, this broadly looks sorta like minimizing prediction error (because statistically, we’re typically closer to our homeostatic set point), but it’s not.
This is why confirmation bias is the mother of all bias. CB doesn’t just conveniently ignore conflicting data. It reinforces itself in your explicit beliefs, in unconscious intuition, in raw perception, AND in action. It can grow from nothing and become impossible to dislodge.
I consider this to be on shaky grounds. Perceptual PP theory is abstracted from the math of bayesian networks, which avoid self-reinforcing beliefs like this. As I mentioned earlier, #1 and #2 happen simultaneously. So the top-down theories should weaken, even as they impose themselves tyrannically on perception. A self-reinforcing feedback loop requires a more complicated explanation.
On the other hand, this can happen in loopy bayesian networks, when approximate inference is done via loopy belief prop. For example, there’s a formal result that Gaussian bayes nets end up with the correct mean-value beliefs, but with too high confidence.
So, maybe.
But loopy belief prop is just one approximate inference method for bayes nets, and it makes sense that evolution would fine-tune the inference of the brain to perform quite well at perceptual tasks. This could include adjustments to account for the predictable biases of loopy belief propagation, EG artificially decreasing confidence to make it closer to what it should be.
My point isn’t that you’re outright wrong about this one, it just seems like it’s not a strong prediction of the model.
What I don’t believe in is a general mechanism whereby you act so as to confirm your predictions.
I had understood (via one-sentence summary, so lossy in the extreme) that this was approximately how motor control worked. Is this a wrong understanding? If not, what separates the motor control mechanism from the perception mechanism?
Quoting from that, and responding:
I would clarify that #1 and #2 happen together. Given a large difference between prediction and observation, a confident prediction somewhat overwrites the perception (which helps us deal with noisy data), but the prediction is weakened, too.
And #3 is, of course, something I argued against in my other reply.
Right, this makes sense.
Why do you believe this?
I can believe that, in social circumstances, people act so as to make their predictions get confirmed, because this is important to group status. For example, (subconsciously) socially engineering a situation where the cyan-skinned person is trapped in a catch 22, where no matter what they do, you’ll be able to fit it into your narrative.
What I don’t believe in is a general mechanism whereby you act so as to confirm your predictions.
I already stated several reasons in my other comment. First, this does not follow easily from the bayes-net-like mechanisms of perceptual PP theory. They minimize prediction error in a totally different sense, reactively weakening parts of models which resulted in poor predictions, and strengthening models which had strong predictions. This offers no mechanism by which actions would be optimized in a way such that we proactively minimize prediction error thru our actions.
Second, it doesn’t fit, by and large, with human behavior. Humans are curious infovores; a better model would be that we actively plan to maximize prediction error, seeking out novel stimulus by steering toward parts of the state-space where our current predictive ability is poor. (Both of these models are poor, but the information-loving model is better.) Give a human a random doodad and they’ll fiddle with it by doing things to see what will happen. I think people make a sign error, thinking PP predicts info-loving behavior because this maximizes learning, which intuitively might sound like minimizing prediction error. But it’s quite the opposite: maximizing learning means planning to maximize prediction error.
Third, the activity of any highly competent agent will naturally be highly predictable to that agent, so it’s easy to think that it’s “minimizing prediction error” by following probable lines of action. This explains away a lot of examples of “minimizing prediction error”, in that we don’t need to posit any separate mechanism to explain what’s going on. A highly competent agent isn’t necessarily actively minimizing prediction error, just because it’s managed to steer things into a predictable state. It’s got other goals.
Furthermore, anything which attempts to maintain any kind of homeostasis will express behaviors which can naturally be described as “reducing errors”—we put on a sweater when it’s too cold, take it off when it’s too hot, etc. If we’re any good at maintaining our homeostasis, this broadly looks sorta like minimizing prediction error (because statistically, we’re typically closer to our homeostatic set point), but it’s not.
I consider this to be on shaky grounds. Perceptual PP theory is abstracted from the math of bayesian networks, which avoid self-reinforcing beliefs like this. As I mentioned earlier, #1 and #2 happen simultaneously. So the top-down theories should weaken, even as they impose themselves tyrannically on perception. A self-reinforcing feedback loop requires a more complicated explanation.
On the other hand, this can happen in loopy bayesian networks, when approximate inference is done via loopy belief prop. For example, there’s a formal result that Gaussian bayes nets end up with the correct mean-value beliefs, but with too high confidence.
So, maybe.
But loopy belief prop is just one approximate inference method for bayes nets, and it makes sense that evolution would fine-tune the inference of the brain to perform quite well at perceptual tasks. This could include adjustments to account for the predictable biases of loopy belief propagation, EG artificially decreasing confidence to make it closer to what it should be.
My point isn’t that you’re outright wrong about this one, it just seems like it’s not a strong prediction of the model.
I had understood (via one-sentence summary, so lossy in the extreme) that this was approximately how motor control worked. Is this a wrong understanding? If not, what separates the motor control mechanism from the perception mechanism?