Andy Clark does a fascinating discussion and analysis
of predictive processing, which turns the traditional picture of perception on its head. The embodied mind model, which seems to me completely compelling, shows the stark inadequacy of most brain centered models of mind and cognition. I pass on the end of his introduction and the closing paragraph of the essay. (This essay is just one of many on a fascinating website , Open Mind
, that has posted 39 essays (edited by Thomas Metzinger and Jennifer Windt) by contributors who are both junior and senior members of the academic philosophy of mind field.
Predictive processing plausibly represents the last and most radical step in a 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. Before an “input” arrives on the scene, these pro-active cognitive systems are already busy predicting its most probable shape and implications. Systems like this are already (and almost constantly) poised to act, and all they need to process are any sensed deviations from the predicted state. It is these calculated deviations from predicted states (known as prediction errors) that thus bear much of the information-processing burden, informing us of what is salient and newsworthy within the dense sensory barrage. The extensive use of top-down probabilistic prediction here provides an effective means of avoiding the kinds of “representational bottleneck” feared by early opponents of representation-heavy—but feed-forward dominated—forms of processing. Instead, the downward flow of prediction now does most of the computational “heavy-lifting”, allowing moment-by-moment processing to focus only on the newsworthy departures signified by salient prediction errors. Such economy and preparedness is biologically attractive, and neatly sidesteps the many processing bottlenecks associated with more passive models of the flow of information.
Action itself...then needs to be reconceived. 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. These hyperactive systems are constantly predicting their own upcoming states, and actively moving so as to bring some of them into being. We thus act so as to bring forth the evolving streams of sensory information that keep us viable (keeping us fed, warm, and watered) and that serve our increasingly recondite ends. PP thus implements a comprehensive reversal of the traditional (bottom-up, forward-flowing) schema. The largest contributor to ongoing neural response, if PP is correct, is the ceaseless anticipatory buzz of downwards-flowing neural prediction that drives both perception and action. Incoming sensory information is just one further factor perturbing those restless pro-active seas. Within those seas, percepts and actions emerge via a recurrent cascade of sub-personal predictions forged from unconscious expectations spanning multiple spatial and temporal scales.
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.
Conclusion: Towards a mature science of the embodied mind
By self-organizing around prediction error, and by learning a generative rather than a merely discriminative (i.e., pattern-classifying) model, these approaches realize many of the goals of previous work in artificial neural networks, robotics, dynamical systems theory, and classical cognitive science. They self-organize around prediction error signals, perform unsupervised learning using a multi-level architecture, and acquire a satisfying grip—courtesy of the problem decompositions enabled by their hierarchical form—upon structural relations within a domain. They do this, moreover, in ways that are firmly grounded in the patterns of sensorimotor experience that structure learning, using continuous, non-linguaform, inner encodings (probability density functions and probabilistic inference). Precision-based restructuring of patterns of effective connectivity then allow us to nest simplicity within complexity, and to make as much (or as little) use of body and world as task and context dictate.
This is encouraging. It might even be that models in this broad ballpark offer us a first glimpse of the shape of a fundamental and unified science of the embodied mind.
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