A prediction engine

Judith Copithorne image

Judith Copithorne image

I have just discovered a wonderful source of ideas about the mind, Open MIND (here), a collection of essays and papers edited by Metzinger and Windt. I ran across mention of it in Derek Bownd’s blog (here). The particular paper that Bownd points to is “Embodied Prediction” by Andy Clark.

LibraryClark argues that we look at the mind backwards. The everyday way we view the working of the brain is: the sensory input is used to create a model of the world which prompts a plan of action used to create an action. He argues for the opposite – action forces the nature of sensory input we seek, that sensory input is used to correct an existing model and it is all done by predicting. The mind is a predicting machine; the process is referred to as PP (predictive processing). “Predictive processing plausibly represents the last and most radical step in this retreat from the passive, input-dominated view of the flow of neural processing. According to this emerging class of models, naturally intelligent systems (humans and other animals) do not passively await sensory stimulation. Instead, they are constantly active, trying to predict the streams of sensory stimulation before they arrive.” Rather than the bottom-up flow of sensory information, the theory has a top-down flow of the current model of the world (in effect what the incoming sensory data should look like). All that is feed back upwards is the error corrections where the incoming sensory data is different from what is expected. This seems a faster, more reliable, more efficient system than the one in the more conventional theory. The only effort needed is to deal with the surprises in the incoming data. Prediction errors are the only sensory information that is yet to be explained, the only place where the work of perception is required for most of the time.

Clark doesn’t make much of it, but he has a neat way of understanding attention. Much of our eye movements and posture movements are seen as ways of selecting the nature of the next sensory input. “Action is not so much a response to an input as a neat and efficient way of selecting the next “input”, and thereby driving a rolling cycle.” As the brain seeks certain information (because of uncertainty, the task at hand, or other reasons), it will work harder to solve the error corrections pertaining to that particular information. Action will be driven towards examining the source of that information. Unimportant and small error corrections may be ignored if they are not important to current tasks. This looks like an excellent description of the focus of attention to me.

Conceptually, this implies a striking reversal, in that the driving sensory signal is really just providing corrective feedback on the emerging top-down predictions. As ever-active prediction engines, these kinds of minds are not, fundamentally, in the business of solving puzzles given to them as inputs. Rather, they are in the business of keeping us one step ahead of the game, poised to act and actively eliciting the sensory flows that keep us viable and fulfilled. If this is on track, then just about every aspect of the passive forward-flowing model is false. We are not passive cognitive couch potatoes so much as proactive predictavores, forever trying to stay one step ahead of the incoming waves of sensory stimulation.

The prediction process is also postulated for motor control. We predict the sensory input which will happen during an action and that information flows from top down and error correction controls the accuracy of the movement. The predicted sensory consequences of our actions causes the actions. “The perceptual and motor systems should not be regarded as separate but instead as a single active inference machine that tries to predict its sensory input in all domains: visual, auditory, somatosensory, interoceptive and, in the case of the motor system, proprioceptive. …This erases any fundamental computational line between perception and the control of action. There remains, to be sure, an obvious (and important) difference in direction of fit. Perception here matches neural hypotheses to sensory inputs, and involves “predicting the present”; while action brings unfolding proprioceptive inputs into line with neural predictions. …Perception and action here follow the same basic logic and are implemented using the same computational strategy. In each case, the systemic imperative remains the same: the reduction of ongoing prediction error.

This theory is comfortable when I think of conversational language. Unlike much of perception and control of movement, language is conducted more in the light of conscious awareness. It is (almost) possible to have a feel of a prediction of what is going to be said when listening and to only have work to do in understanding when there is a surprise mismatch between the expected and the heard word. And when talking, it is without much effort until your tongue makes a slip and has to be corrected.

I am looking forward to browsing through openMIND now that I know it exists.


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