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Embodied Prediction Andy Clark Embodied Prediction Andy Clark Versions of the “predictive brain” hypothesis rank among the most promising and Author the most conceptually challenging visions ever to emerge from computational and cognitive neuroscience. In this paper, I briefly introduce (section 1 the most rad! Andy Clark ical and comprehensive of these visions—the account of “active inference”, or “ac! andy.clark1 ed.ac.uk tion-oriented predictive processing” (Clark 2013a , developed by #arl $riston and colleagues. In section 2, I isolate and discuss four of the frame%ork&s most provoc! 2niversity of Edinburgh ative claims: (i that the core flo% of information is top-do%n, not bottom-up, %ith Edinburgh, 2nited #ingdom the forward flo% of sensory information replaced by the forward flo% of prediction error; (ii that motor control is )ust more top-do%n sensory prediction( (iii that ef! Commentator ference copies, and distinct “controllers”, can be replaced by top-do%n predic! tions; and (iv that cost functions can fruitfully be replaced by predictions. *ork! /ichael /adary ing together, these four claims offer a tantali+ing glimpse of a ne%, integrated madary1uni-main+.de frame%ork for understanding perception, action, embodiment, and the nature of 3ohannes 4utenberg!2niversit5t human e,perience. I end (section 3 by sketching %hat may be the most important /ain+, 4ermany aspect of the emerging vie%' its ability to embed the use of fast and frugal solu! tions (as highlighted by much %ork in robotics and embodied cognition %ithin an .ditors over-arching scheme that includes more structured, kno%ledge!intensive strategies, combining these fluently and continuously as task and conte,t dictate. 6homas /et+inger met+inger 1uni!main+.de Keywords Active inference - Embodied cognition - /otor control - 0rediction - 0rediction er! 3ohannes 4utenberg!2niversit5t ror /ain+, 4ermany 3ennifer /. *indt )ennifer.%indt1monash.edu /onash 2niversity /elbourne, Australia 1 Mind turned upside down? PP (Predictive processing; for this terminology, tinuities and other factors) gave 'ay to detected see Clark 2013a) turns a traditional picture of features such as &lobs, edges, &ars, +/ero-cross" perception on its head !ccording to that once" ings,, and lines, 'hich in turn gave 'ay to de" standard picture (Marr 1982), perceptual pro" tected surface orientations leading ultimately cessing is dominated &y the forward flo' of in" (though this step 'as al'ays going to &e pro&" formation transduced from various sensory re" lematic) to a three"dimensional model of the ceptors As information flo's forward, a pro" visual scene 0arly perception is here seen as gressively richer picture of the real"'orld scene &uilding to'ards a comple1 'orld model &y a is constructed (he process of construction feedfor'ard process of evidence accumulation 'ould involve the use of stored kno'ledge of (raditional perceptual neuroscience follo'ed various kinds, and the for'ard flo' of informa" suit, 'ith visual corte1 (the most-studied e1" tion 'as su&)ect to modulation and nuancing by ample) &eing +traditionally vie'ed as a hier" top-do'n (mostly attentional) effects But the archy of neural feature detectors, 'ith neural &asic picture remained one in 'hich perception population responses being driven by bottom-up 'as fundamentally a process of +&ottom-up fea" stimulus features, (0gner et al 2010, p 16601) ture detection, -n Marr’s theory of vision, de" (his 'as a vie' of the perceiving &rain as pass" tected intensities (arising from surface discon" ive and stimulus-driven, taking energetic inputs Clark, A. (789: . .mbodied 0rediction. In 6. /et+inger ; 3. /. *indt (.ds . Open MIND: <(6 . $rankfurt am /ain' /I=> 4roup. doi' 10.9::87?@<AB@:A:<899: 1 | 79 %%%.open!mind.net from the senses and turning them into a coher" cessing. -nstead, the do'n'ard flo' of predic" ent percept &y a kind of step"'ise &uild-up tion no' does most of the computational moving from the simplest features to the more +heavy"lifting”, allo'ing moment"&y-moment comple13 from simple intensities up to lines and processing to focus only on the ne's'orthy de" edges and on to comple1 meaningful shapes, ac" partures signified &y salient (that is, high-preci" cumulating structure and comple1ity along the sion—see section 3) prediction errors 5uch eco" 'ay in a kind of Lego-block fashion nomy and preparedness is &iologically attract" 5uch vie's may &e contrasted 'ith the in" ive, and neatly sidesteps the many processing creasingly active vie's that have &een pursued &ottlenecks associated with more passive models over the past several decades of neuroscientific of the flo' of information and computational research (hese vie's (Bal" !ction itself (more on this shortly) then lard 1991; Churchland et al 1994; Ballard et al needs to &e reconceived !ction is not so much 1997) stress the active search for task"relevant a response to an input as a neat and efficient information )ust-in-time for use -n addition, 'ay of selecting the ne1t +input,, and there&y huge industries of 'ork on intrinsic neural driving a rolling cycle (hese hyperactive sys" activity, the +resting state, and the +default tems are constantly predicting their o'n up" mode, (for a revie', see 8aichle 9 5nyder coming states, and actively moving so as to 2007) have dra'n our attention to the ceaseless &ring some of them into &eing. ;e thus act so &u// of neural activity that takes place even in as to &ring forth the evolving streams of sensory the absence of ongoing task-specific stimulation, information that keep us viable (keeping us fed, suggesting that much of the &rain.s 'ork and 'arm, and 'atered) and that serve our increas" activity is in some 'ay ongoing and endogen" ingly recondite ends PP thus implements a ously generated comprehensive reversal of the traditional (&ot" Predictive processing plausi&ly represents tom-up, forward-flo'ing) schema. (he largest the last and most radical step in this retreat contri&utor to ongoing neural response, if PP is from the passive, input-dominated vie' of the correct, is the ceaseless anticipatory &u// of flo' of neural processing. !ccording to this do'n'ards"flo'ing neural prediction that drives emerging class of models, naturally intelligent &oth perception and action -ncoming sensory systems (humans and other animals) do not information is )ust one further factor perturbing passively a'ait sensory stimulation -nstead, those restless pro-active seas ;ithin those seas, they are constantly active, trying to predict the percepts and actions emerge via a recurrent cas" streams of sensory stimulation &efore they ar" cade of su&-personal predictions forged (see &e" rive Before an +input, arrives on the scene, lo') from unconscious e1pectations spanning these pro-active cognitive systems are already multiple spatial and temporal scales &usy predicting its most probable shape and im" Conceptually, this implies a striking re" plications 5ystems like this are already (and al" versal, in that the driving sensory signal is most constantly) poised to act, and all they really )ust providing corrective feed&ack on the need to process are any sensed deviations from emerging top-do'n predictions 1 As ever-active the predicted state -t is these calculated devi" prediction engines, these kinds of minds are not, ations from predicted states (kno'n as predic- fundamentally, in the business of solving pu//les tion errors) that thus bear much of the informa" given to them as inputs 8ather, they are in the tion-processing &urden, informing us of 'hat is &usiness of keeping us one step ahead of the salient and ne's'orthy 'ithin the dense sens" game, poised to act and actively eliciting the ory &arrage (he e1tensive use of top-do'n sensory flo's that keep us viable and fulfilled If probabilistic prediction here provides an effect" this is on track, then )ust about every aspect of ive means of avoiding the kinds of +representa" the passive forward-flo'ing model is false ;e tional &ottleneck, feared &y early opponents are not passive cognitive couch potatoes so (e g., Brooks 1991) of representation-heavy: 1 <or this observation, see <riston (2005), p 825, and the discussion in &ut feed-for'ard dominated—forms of pro" Hoh'y (2013) Clark, A. (789: . .mbodied 0rediction. In 6. /et+inger ; 3. /. *indt (.ds . Open MIND: <(6 . $rankfurt am /ain' /I=> 4roup. doi' 10.9::87?@<AB@:A:<899: 2 | 79 %%%.open!mind.net much as proactive predictavores, forever trying to match input samples 'ith successful predic" to stay one step ahead of the incoming 'aves of tions -nstead, visual signals 'ere processed via sensory stimulation a hierarchical system in 'hich each level tried (in the 'ay )ust sketched) to predict activity at 2 Radical predictive processing the level &elo' it using recurrent (feed&ack) connections -f the feed&ack successfully pre" 5uch models involve a num&er of ?uite radical dicted the lo'er-level activity, no further action claims -n the present treatment, - propose fo" 'as re?uired <ailures to predict enabled tuning cusing upon just four: and revision of the model (initially, )ust a ran" dom set of connection 'eights) generating the 1. (he core flo' of information is top-do'n, not predictions, thus slo'ly delivering kno'ledge of &ottom-up, and the forward flo' of sensory in" the regularities governing the domain -n this formation is replaced &y the for'ard flo' of architecture, forward connections &et'een levels prediction error. carried only the +residual errors, (8ao 9 Bal" 2. Motor control is )ust more top-do'n sensory lard 1999, p 7$) &et'een top-do'n predictions prediction and actual lo'er level activity, 'hile &ack'ard 3.
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