Commentary on Andy Clark's target article on predictive coding

Block, Ned

Siegel, Susanna

English

PhilPapers

cognitively sophisticated moves as inferring the (hidden) causes ofour current observations, and using that hypothesis to predictfuture observations, both as we passively monitor and activelyintervene in the world. It is theory laden and model-rich.We have no trouble believing that a fundamental part of ourexquisite attunement to environmental contingencies involvessensitivity to (and the ability to make use of) inter- and cross-modal correlations in sensory signals. Sensitivity to temporal andspatial (e.g., across the retina) correlations could underwritemany functional advantages, including the ones Clark highlights,such as reducing sensory bandwidth and drawing attention tosalient departures from expectations. In this sense we shareClark’s belief that predictive1 coding is likely to be a ubiquitousand fundamental principle of brain operation; neural nets areespecially good at computing correlations.However, we don’t think that evidence for predictive1 codingwarrants a belief in predictive2 coding. And it is only frompredictive2 coding that many of Clark’s larger implications follow.Clark makes the move from predictive1 coding to predictive2coding largely by relying on an innovative account of binocularrivalry offered by Hohwy et al. (2008). In Clark’s somewhat sim-pliﬁed version of their proposal, the experienced alternationbetween seeing the face stimulus presented to one eye and thehouse stimulus presented to the other is explained by a knowl-edge-driven alternation between rival hypotheses (face at locationx, house at location x) neither of which can account for all of theobservations. According to Clark, the reason the images don’t fuseand lead to a visual steady-state is because we know that faces andhouses can’t coexist that way. If this knowledge-driven account isthe correct way to understand something as perceptually basic asbinocular rivalry, then predictive2 coding can begin to look like aplausible, multilevel and unifying explanation of perception,action and cognition: perception is cognitive and inferential; infer-ence perceptual; and all of it is active.But while the predictive2 coding model of binocular rivalry maybe consistent with much of the data, it is far from the only possibleexplanation of the phenomenon. Here is an outline of a reasonablepredictive1 coding account: Given the generally high-level ofcross-correlation in the inputs of our two eyes, the left eyesignal would predict1 greater correlation with the right eye thanis currently in evidence; this would weaken the inputs associatedwith the left eye, unmasking the inputs associated with the righteye, which would predict1 cross-correlated left eye signals . . .and so on. However far this particular proposal could be taken,the point is one can account for the phenomenon with low-level, knowledge-free, redundancy-reducing inhibitory inter-actions between the eyes (see, e.g., Tong et al. 2006). After all,binocular rivalry also occurs with orthogonal diffraction gratings,indicating that high-level knowledge of what is visually possibleneedn’t be the driver of the visual oscillation; humans don’thave high-level knowledge about the inconsistency of orthogonalgratings. In general, although not every pair of stimuli induce bis-table perceptions, the distinction between those that do and thosethat don’t appears to have little to do with knowledge (see Blake[2001] for a review). Adopting a predictive2 coding account is atheoretical choice not necessitated by the evidence. It is hardlyan inconsequential choice.Using predictive2 coding as a GUT of brain function, as Clarkproposes, is problematic for several reasons. The ﬁrst problem iswith the very idea of a grand uniﬁed theory of brain function.There is every reason to think that there can be no granduniﬁed theory of brain function because there is every reason tothink that an organ as complex as the brain functions accordingto diverse principles. It is easy to imagine knowledge-richpredictive2 coding processes employed in generating expectationsthat we will confront a jar of mustard upon opening the refriger-ator door, while knowledge-free predictive1 coding processes willbe used to alleviate the redundancy of sensory information. Weshould be skeptical of any GUT of brain function. There is alsoa problem more speciﬁc to predictive2 coding as a brain GUT.Taking all of our experience and cognition to be the result ofhigh-level, knowledge-rich predictive2 coding makes it seem asif the world that we experience and think about is a projectionof our minds. Western philosophy has been down this lonelyand unproductive road many times. It would be a shame if thespotlight that Clark helpfully shines on this innovative work inneuroscience were to lead us back there.Attention and perceptual adaptationdoi:10.1017/S0140525X12002245Ned Blocka and Susanna Siegel Q1baDepartment of Philosophy, New York University, New York, NY 10003;bDepartment of Philosophy, Harvard University, Cambridge, MA 02138.ned.block@nyu.edu ssiegel@fas.harvard.eduhttp://www.nyu.edu/gsas/dept/philo/faculty/block/http://www.people.fas.harvard.edu/∼ssiegel/Abstract: Clark advertises the predictive coding (PC) framework asapplying to a wide range of phenomena, including attention. We arguethat for many attentional phenomena, the predictive coding pictureeither makes false predictions, or else it offers no distinctive explanationof those phenomena, thereby reducing its explanatory power.According to the predictive coding view, at every level of thevisual/cortical hierarchy, there are two kinds of units: error unitsand representation units. Representations propagate downwardin the visual hierarchy whereas error signals propagate upward.Error in this sense might be better called “discrepancy,” since itis the discrepancy between what the visual system predicts (at agiven level) and what is represented at that level. Clark advertisesthe predictive coding (PC) framework as applying to a wide rangeof phenomena, including attention, which Clark says “is achievedby altering the gain (the ‘volume,’ to use a common analogy) onthe error-units” (sect. 2.3, para. 6). We argue that for many atten-tional phenomena, the predictive coding picture either makesfalse predictions, or else it offers no distinctive explanation ofthose phenomena, thereby reducing its explanatory power.Consider a basic result in this area (Carrasco et al. 2004), whichis that attention increases perceived contrast by enhancing “therepresentation of a stimulus in a manner akin to boosting its phys-ical contrast” (Ling & Carrasco 2006, p. 1243). A cross-modalstudy using auditory attention-attractors (Störmer et al. 2009)showed that the contrast-boosting effect correlated with increasedactivity in early stages of visual processing that are sensitive todifferences in contrast among stimuli. The larger the corticaleffect, the larger the effect on perceivers’ judgments. Increasingthe contrast of a stimulus has an effect on the magnitude of per-ceptual adaptation to that stimulus, causing greater threshold acti-vation in the tilt after-effect and longer recovery time. Ling andCarrasco (2006) showed that attending to a stimulus while adapt-ing to that stimulus has the same effect as increasing the contrastof the adapting stimulus. After attending to the adaptor (70% con-trast), the contrast sensitivity of all observers was equivalent to theeffect of adapting to a 81–84% contrast adaptor.How do these results look from a PC perspective? Suppose thatat time t1, the perceiver is not attending to the left side of spacebut nonetheless sees a striped grid on the left with apparent con-trast of 70%. Because there is no movement or other change, attime t2, the visual system predicts that the patch will continue at70%. But at t2 the perceiver attends to the patch, raising theapparent contrast to, say, 82%. Now at t2 there is an error, a dis-crepancy between what is predicted and what is “observed.” Sincethe PC view says attention is turning up the volume on the errorrepresentations, it predicts that at t3 the signal (the representedcontrast) should rise even higher than 82%. But that doesnot happen.Commentary/Andy Clark: Predictive brains, situated agents, and the future of cognitive scienceBEHAVIORAL AND BRAIN SCIENCES (2013) 36:4 25There are two important lessons. First, the initial changes dueFig.1-B/Wonline,B/Winprintto attending come before there is an error (at t2 in the example),so the PC viewpoint cannot explain them. Second, the PC viewmakes the false prediction that the changes due to attendingwill be magniﬁed.Sometimes PC theorists assume the error signal is equal to theinput. Perhaps this identiﬁcation makes some sense if the percei-ver’s visual system has no “expectations,” say because the eyeshave just opened. But once the eyes have opened and things inthe environment are seen, it makes no sense to take the errorsignal to be the sensory input.The PC picture also seems to lack a distinctive explanation ofwhy attention increases spatial acuity. Yeshurun and Carrasco(1998) showed that increased attention can be detrimental to per-formance when resolution was already on the border of too highfor the scale of the texture, increasing acuity to the point wherethe subject does not see the forest for the trees. Too little atten-tion can also be detrimental, making it harder to see the trees. Yes-hurun and Carrasco varied resolution of perception by presentingtextured squares (such as the one in Fig. 1) at different eccentri-cities (the more foveal, the better the resolution). But they alsovaried resolution by manipulating the focus of spatial attention:With the eyes focused at the center, they attracted attention tothe left or to the right. Combining contributions to resolutionfrom eccentricity and attention, they found that there was anoptimal level of resolution for detecting the square, with detectionfalling off on both ends. Single cell recordings in monkey visualcortex reveal shrinking receptive ﬁelds (the area of space that aneuron responds to) in mid-to-high level vision, speciﬁcally inV4, MT, and LIP, and this shrinkage in receptive ﬁelds is a contri-butor to explaining the increase in acuity (Carrasco 2011).Does the PC framework have a distinctive explanation of atten-tional effects on spatial acuity, in terms of “gain in error-units”? If,due to the level of acuity, one does not see the square, then theprediction of no square will be conﬁrmed, and there will be no dis-crepancy (“error”) to be magniﬁed. Since the gain in error units isthe only distinctive resource of the PC view for explaining atten-tional phenomena, the view seems to have no distinctive expla-nation of this result either. Can the predictive coding point ofview simply borrow Carrasco’s explanation? That explanation isa matter of shrinkage in receptive ﬁelds of neurons in the rep-resentation nodes, not anything to do with prediction error, sothe predictive coding point of view would have to concede thatattention can act directly on representation nodes without adetour through error nodes.Finally, attention to certain items – for example, random dotpatterns –makes them appear larger. Anton-Erxleben et al.(2007) showed that the size of the effect is inversely related tothe size of the stimulus, explaining the result in terms of receptiveﬁeld shift (such shifts are also observed from single cell recordingsin monkey visual areas; Womelsdorf et al. 2006). This explanationdepends on the retinotopic and therefore roughly spatiotopicorganization common to many visual areas – not on error units.Neurons whose receptive ﬁelds lie on the periphery of thepattern shift their receptive ﬁelds so as to include the pattern,moving the portion of the spatiotopically represented space toinclude the pattern, resulting in the representation of thepattern as occupying a larger area. Here too, predictive codingoffers no distinctive explanation.The facts of attention and adaptation do not ﬁt well with thepredictive coding view or any picture based on how “sensoryneurons should behave” (Lochmann et al. 2012) rather than thefacts of how they do behave. Without a distinctive explanationof these facts, the explanatory promises of predictive coding areoverdrawn.Attention is more than prediction precisiondoi:10.1017/S0140525X12002324Howard Bowman,a Marco Filetti,a Brad Wyble,b andChristian Olivers Q1caCentre for Cognitive Neuroscience and Cognitive Systems, and the School ofComputing, University of Kent at Canterbury, Kent CT2 7NF, United Kingdom;bDepartment of Psychology, Syracuse University, Syracuse, NY 13244;cDepartment of Cognitive Psychology, Faculty of Psychology and Education,VU University Amsterdam, 1081 BT Amsterdam, The Netherlands.H.Bowman@kent.ac.uk M.Filetti@kent.ac.ukbwyble@gmail.com c.n.l.olivers@vu.nlhttp://www.cs.kent.ac.uk/people/staff/hb5/http://www.cs.kent.ac.uk/people/rpg/mf266/www.bradwyble.comhttp://olivers.cogpsy.nlAbstract: A cornerstone of the target article is that, in a predictive codingframework, attention can be modelled by weighting prediction error with ameasure of precision. We argue that this is not a complete explanation,especially in the light of ERP (event-related potentials) data showinglarge evoked responses for frequently presented target stimuli, whichthus are predicted.The target article by Andy Clark champions predictive coding as atheory of brain function. Perception is the domain in which manyof the strongest claims for predictive coding have been made, andwe focus on that faculty. It is important to note that there areother unifying explanations of perception, one being that thebrain is a salience detector, with salience referring broadly to rel-evance to an organism’s goals. These goals reﬂect a short-termtask set (e.g., searching a crowd for a friend’s face), or moreingrained, perhaps innate motivations (e.g., avoiding physicalthreat). A prominent perspective is, exactly, that one role of atten-tion is to locate and direct perception towards, salient stimuli.The target article emphasises the importance of evokedresponses, particularly EEG event-related potentials (ERPs), inadjudicating between theories of perception. The core idea isthat the larger the difference between an incoming stimulus andthe prediction, the larger the prediction error and thus thelarger the evoked response. There are indeed ERPs that areclearly modulated by prediction error, for example, the MismatchFigure 1 (Block & Siegel). A display of one of the textured ﬁgures (the square on the right) used by Yeshurun and Carrasco (1998). Thesquare appeared at varying degrees of eccentricity. With low resolution in peripheral locations, attention improved detection of thesquare; but with high resolution in central locations, attention impaired detection.Commentary/Andy Clark: Predictive brains, situated agents, and the future of cognitive science26 BEHAVIORAL AND BRAIN SCIENCES (2013) 36:4

Attention and perceptual adaptation

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