Transduction, Calibration, and the Penetrability of Pain

1 Top-Down Effects on Pain

A builder aged 29 came to the accident and emergency department having jumped down on to a 15 cm nail. As the smallest movement of the nail was painful he was sedated with fentanyl and midazolam. The nail was then pulled out from below. When his boot was removed a miraculous cure appeared to have taken place. Despite entering proximal to the steel toecap the nail had penetrated between the toes: the foot was entirely uninjured. (Fisher, Hassan, & O’Connor 1995)

This vignette presents us with a mystery. Nobody doubts that the poor builder was in severe pain. Yet while his reputation on the job site was irreparably damaged, physically speaking he was completely unharmed.

How does this sort of thing happen? Here is a tempting story—one that I think is broadly correct, though the details will come to matter quite a bit. Having jumped down, our builder saw the nail sticking through his boot. (As the picture accompanying the case report makes clear, the sight would have been a gruesome one.) He thus formed the belief that he was grievously injured, despite the lack of substantial nociceptive input from his foot. That belief was a purely cognitive state. Yet it caused him to have certain perceptual experiences commensurate with it: pain followed. Call this a ‘top-down’ explanation of the builder’s pain: it posits that states like beliefs (that we normally think of as relatively high up in some hierarchy of cognitive states) end up affecting perceptual states (that we normally think of as lower down in the same hierarchy).

The reason why this explanation is so natural is that top-down effects on pain are so common. Both Gligorov (2017) and Shevlin and Friesen (2021) have recently called attention to placebo analgesia as a putative case of top-down influence.¹ Tell someone that the inert pill they are about to receive will diminish their pain, and often enough it will do just that. Conversely, emphasizing that a procedure is likely to hurt tends to make it more painful. Both effects are evoked so reliably that they affect clinical practice: placebo effects are so easy to evoke that placebo control has become a standard against which claims of drug effectiveness must be measured, while nocebo effects raise serious practical issues for informed consent.²

Hypnosis similarly has a notable (if variable) influence on pain perception (Patterson 2004; Patterson & Jensen 2003). Importantly, hypnosis has been used to ameliorate the pain of labor, burn debridement, and bone marrow aspiration—all intense pains that are often resistant to other interventions. Yet hypnosis is, on the face of it, just talk. The diminution of pain can be more dramatic still. It has been long known that many severe injuries are initially painless, often for a period of hours. Wall argues (conclusively in my opinion) that this is an adaptive response: that the role of pain is to limit motion, and the downward suppression is elicited by appraisal that the circumstances are such that “treatment of the injury does not have the highest biological priority” (Wall 1979b: 298). Belief about the need to escape to safety can thus also modulate the pain of severe injury (Wall 1979a; 1979b; 2000).³

In addition to these dramatic effects, there is considerable evidence for more complex modulation of pain. Work on the “biopsychosocial” (Gatchel, Peng, Peters, Fuchs, & Turk 2007) or “biopsychomotor” (Sullivan 2008) models of pain suggests that pain is influenced not only by physical processes but also by social and psychological mediators—cognitive processes par excellence. Felt control Staub, Tursky, and Schwartz (1971) and self-efficacy (Litt 1988) over painful stimuli is known to diminish the intensity of pain, and this appears to be modulated by a suite of cognitive factors (Thompson 1981). Physiotherapists have emphasized the role of false beliefs about the origins of pain in the persistence of chronic pain, and hence the role of patient education in diminishing pain (Butler & Moseley 2013; Moseley 2002; Moseley & Butler 2015). Social and contextual factors also play an important role in moderating placebo analgesia (Atlas 2021).

Hence Shevlin and Friesen conclude that “. . . it appears that one’s beliefs and expectations with regards to the treatment one is receiving, as well as one’s awareness of the administration, have a significant impact on one’s immediate experience of pain relief” (2021: 8). What do these top-down influences show. Shevlin and Friesen (2021) have suggested that effects like these are evidence for something like cognitive penetrability of pain.

Cognitive penetrability is philosophically contentious notion. Much of what follows will be concerned with exploring different definitions. But we need a place to start, and I think the definition given by Stokes (2013) captures a common low-commitment starting point:

A perceptual experience E is cognitively penetrated if and only if (1) E is causally dependent upon some cognitive state C and (2) the causal link between E and C is internal and mental. (Stokes 2013: 650; compare also Stokes 2012: 479)

Clause 1 is straightforward, as is the first conjunct of clause 2. If I desire a drink of water and turn my head to look for the glass, that changes my perceptual experience, but not in a way anyone worries about. One may complain about the boundaries of ‘internal’—if I think about magpie attacks and get scared and then get indigestion, that’s all within the boundaries of the skin—but leave it for now.

The second conjunct of clause 2 of is less clear, and hence where most of the action has been. Since Pylyshyn (1984), one of the key escape clauses for putative cases of penetration has been the effect of attention. Merely attending to one stimulus rather than another might change how well one of them is processed, or even if one raises to the level of consciousness at all. The idea is usually that this is merely an inner analogue of turning one’s head. Penetration demands something more: that the effect on perception is in virtue of, and specific to, the content of our beliefs and desires.

Now, most of the debate around cognitive penetrability has been around the penetration of visual perception. This has been a fraught debate. For what it’s worth, I am convinced by recent critiques arguing that evidence in favor of penetration is of relatively poor quality (Firestone & Scholl 2016; Machery 2015). Clause 2 of Stokes’s definition gives a lot of wiggle room for those who would be skeptical: attentional effects are easy to find and easier to posit. Furthermore, as Firestone and Scholl (2016) point out, the more plausible cases of visual penetrability are (by and large) fairly subtle and fairly rare. Much of the debate thus ends up focusing on the details of a handful of cases.

As Shevlin and Friesen point out, pain is different: as detailed above, pretty much everybody accepts that there are top-down influences, so the only question is whether they fit the definition. Shevlin and Friesen also note that the case of pain (and bodily sensations more generally) might not generalize to sensations like vision, because there is “a fundamental functional and even architectural distinction to be drawn between these two families of mental states, perhaps reflecting distinct evolutionary histories” (2021: 14). So pain is its own case, and it seems that we do have good prima facie evidence that there are top-down effects that don’t just amount to (e.g.) someone paying more or less attention to the pain they’re in.

The case of pain is not entirely clear-cut, however. There is quite a lot about pain perception that doesn’t fit the penetrability story. So, for example, a great deal of chronic pain persists despite the sincere and accurate beliefs of those affected that there is no tissue damage (or any other issue outside that of the pain system). Similarly, just having the powerful desire that you not be in pain does not seem to affect pain in the slightest. Consider, by way of concrete example, phantom limb pain. Patients know they do not have an arm, yet that belief seems powerless to stop the feeling of pain. This seems to be precisely analogous to visual illusions: knowing that the two lines in the Müller-Lyer illusion are in fact the same length doesn’t keep them from looking the way they do. This would suggest that we ought to draw the same conclusion from phantom limb pain: pain processing is in fact modular and impenetrable due to its encapsulation.⁴

So it seems like there is some real debate about whether the top-down influences seen on pain really count as cognitive penetration in the full sense of the term. The present paper will argue that this debate is live because the broad, interesting top-down influences on pain actually belong to another phenomenon altogether, which I will call transducer calibration. Like cognitive penetration, transducer calibration allows for complex effects on perceptual processes that are ultimately driven by a variety of different cognitive states—but these effects do do not (I claim) threaten the traditional architectural distinctions that penetrability is meant to threaten. For, as opponents of penetrability argue, the effects are on transducers, which are traditionally outside the cognitive system—except that appeal to transducers is usually meant to end the argument, whereas I will show that transducer calibration is complex, potentially widespread, and far more interesting than opponents of cognitive penetration have typically credited it with being.

The argument is thus dialectically complex, and will take a bit of time to unfold. Before I begin, two important caveats. First, the goal of the paper is not really to adjudicate between different debates about what cognitive penetrability is; I need enough to show that transducer calibration isn’t penetration, and that should be true on a variety of accounts. One of the major advances of this literature in the past 40 years has been to disentangle various criteria that were jointly proposed for penetration. One of the minor aims of the paper is to show that this disentanglement opens the way for the identification of other, equally interesting, phenomena.

Second, I do not intend my account to cover all putative top-down effects on pain. So, for example, in an elegant series of studies Wiech et al. (2014) show that base rate information affects the judgment of shocks as of low or high intensity. Both Shevlin and Friesen (2021) and Casser and Clarke (2022) both discuss this, and come down on different sides. The dynamic replicates that of traditional debates about vision: one can read Wiech et al. as showing either penetration of an early perceptual process or the effect of knowledge on postperceptual decision-making. Similarly, everyone agrees that there are complex and widespread influences of emotion, anticipation, and personality on reactions to pain; some of the effects of placebo analgesia (for example) can certainly be attributed to those (Wiech, Ploner, & Tracey 2008). The claim of the paper is rather than insofar as there are many of these large, striking effects on pain, they belong to the phenomenon of transducer calibration.

My argument will proceed as follows. I begin by arguing that the downward effects on pain for which there are good evidence are downward effects on the transducers of pain, rather than on any modular processing of pain. This will come in two steps. In the conceptual step (Section 2), I distinguish transducers from early perception proper; this will also flesh out what’s normally at stake in discussions of cognitive penetrability. The empirical step (Section 3) reviews the pathways for downward modulation, and argue that these fit best with a story on which transducers are modulated. The idea that there might be nontrivial modulation of transducers is neither familiar nor obvious; in the course of defending against some objections (Section 4), I’ll flesh out why transducer calibration is a distinct and interesting class of downward influence. Finally (Section 5) I’ll step back to suggest that transducer calibration may be more widespread than one might suppose, and suggest that this might have several interesting consequences for thinking about cognitive architecture more broadly.

2. Cognitive Penetration and Cognitive Architecture

The broad idea that cognition might influence perception is an old one and appears in many guises (Stokes 2013). The current debate—and, arguably, widespread use of the term itself—arises from Pylyshyn (1984)’s defense of a computational theory of cognition. In what follows, I will lean liberally on Pylyshyn’s (1984) and (1999), both of which present the idea of cognitive penetrability in the course of arguing against it.

The architectural picture in the background of Pylyshyn’s arguments has been remarkably influential. It is closely related to Fodor (1983)’s defense of a modular theory of mind, which has been arguably even more influential within philosophy. Note that everything from this point out will primarily be of interest to those who adopt a broadly computationalist theory about the mind. Cognitive penetrability sometimes comes up in the course of arguing against computationalism, by showing that the architectural boundaries drawn below are illegitimate. I’m not doing that here. If that is your goal, you should probably look to someone other than Pylyshyn for an architectural story within which to frame the debate.

Suppose I gaze upon a panda, recognize it as such, and reflect that if pandas weren’t so cute, they would have gone extinct long ago. Pylyshyn identifies three important stages in this process.

First, there is an initial step of transduction, in which light from the panda falling on the retina is transformed into a suitable symbolic representation. This is a single step from the computational point of view: a special sort of primitive operation that connects inside to outside.

Next, there are multiple symbolic steps. In these, computational processes—sensitive, by definition, only to the formal/syntactic/computational properties of symbols rather than their contents—take symbols in, manipulate them, and pass the resulting symbols on to other processes. Much of cognitive science is concerned with these steps: the particular ways in which edges and other features are extracted, combined with stored information, and used to categorize objects. Finally, there are the intentional steps: ones that involve beliefs about the cuteness and haplessness of pandas, and that are combined with other beliefs in an inferential, truth-preserving way.

Each step, says Pylyshyn, requires a different sort of explanation. The explanation of transducers is an engineering problem. As Pylyshyn puts it, “Like all primitive operations of the functional architecture, the transducer fundamentally is a physical process: that is, its behavior is explainable…in terms of is intrinsic properties—physical, chemical, biological, and so on” (Pylyshyn 1984: 148). Explaining transducers is not part of cognitive science proper, he says. Rather, cognitive scientists assume appropriate transducers. The explanatory task for cognitive science starts when the world is translated into symbols and ends when symbols get transduced back into action. Similarly, intentional transitions can be explained by the meanings and truth-conditions of the contents involved. Here we can rely on logic and personal-level psychology to explain success and failure.

Hence cognitive science (says Pylyshyn) is primarily concerned with the symbolic, computational step in the middle. It is here that all of the classic work gets done—the determination of algorithms, the delineation of representational formats and the postulation of computational primitives and modules. The fundamental distinction between the symbolic and the inferential, Pylyshyn insists, lies in the nature of the state-transitions. Computational explanations take symbols and transform them according to rules defined only on their formal properties (however that is understood). This is not constrained by, and often fails to follow, the rational norms of inference that govern the intentional step. Indeed, Pylyshyn (1999) often goes to lengths to emphasize ways that early vision fails to make rational inferences. Discussing the Kanizsa amodal completion figure, for example, Pylyshyn notes that the seen completion is neither the simplest nor the most likely. Hence whatever is going on in early vision “follows complex principles of its own—that are generally not rational principles, such as semantic coherence or even something like maximum likelihood” (1999: 345).

One of the important ways in which the symbolic is not rational is that it cannot take into account all available information. Rational inference is isotropic: it can (in principle) take into account any belief whatsoever, so long as that belief bears a semantic relationship to the topic at hand. As Fodor puts it:

By saying that confirmation is isotropic, I mean that the facts relevant to the confirmation of a scientific hypothesis may be drawn from anywhere in the field of previously established empirical. . . truths. Crudely: everything that the scientist knows is, in principle, relevant to determining what else he ought to believe. In principle, our botany constrains our astronomy, if only we could think of ways to make them connect. (Fodor 1983: 105)

Conversely, early vision appears to be strongly anisotropic: even things you know very well don’t seem to influence vision. We know that the two lines of the Müller-Lyer illusion are the same length, yet we cannot bring this belief to bear. The lines still look unequal: a vivid demonstration of the the anisotropy of early vision.

Finally, the distinction between the symbolic and the intentional also explains why Pylyshyn is so concerned with cognitive penetrability. Pylyshyn takes the “essence” of cognitive penetration to be “an influence that is coherent or quasi-rational when the meaning of the representation is taken into account” (Pylyshyn 1999: 365). Yet Pylyshyn’s story relies on their being some orderly division between the merely symbolic and the intentional.⁵ Cognitive penetrability happens when some belief influences a computational, symbolic bit—that normally only cares about symbols—in virtue of its semantic content.

As he puts it “The criterion proposed in this section is a direct consequence of a view that might be called the basic assumption of cognitive science. . . that there are at least three, distinct, independent levels at which we can find explanatory principles in cognitive psychology” (Pylyshyn 1984: 133). Or, put another way, early vision is just defined as the anisotropic bits. If they can be affected by belief, then they are isotropic after all. After all, belief-formation is isotropic, so once you get one belief affecting early vision you get them all. The jig is up.⁶

In an excellent discussion, Burnston and Cohen (2015) argue that debates about modularity and cognitive penetrability have tended to move away from the broad idea anisotropy to a more restrictive conception of modularity—one on which, roughly speaking, modules only have access to information from earlier processing areas. So, for example, Deroy (2013: 93) says that cognitive impenetrability holds just in case “. . . perception depends only on its own rules of processing rules [sic] and on the kind of input it receives, and is independent from higher cognitive contents.” Burnston and Cohen (2015) point out that this restrictive requirement is precisely what generates a lot of the friction around particular cases, particularly any that involve what they call ‘integrative’ cross-modal processes.⁷

Here I closely follow Burnston and Cohen (2015)’s excellent discussion of the debate. They claim—and I find this very plausible—that all putative instances of visual penetration involve influence from information that is semantically coherent but still anisotropic. One of the standard examples of cognitive penetrability is meant to be effects of the typical color of an object on perceived color of instances: that cutouts of a heart are seen as more red than other neutral shapes, or that a picture of a neutral-gray banana will be seen as slightly yellow (Delk & Fillenbaum 1965). Yet the perceived color of bananas is affected by knowledge about the characteristic color of bananas, that is still only one piece of information in the whole doxastic system that is relevant. Note that this feature is, in some sense, independent of whether you think that these experimental effects actually occur: the point is rather than even the most plausible candidates for penetration are still anisotropic.

Even if the influence is semantically coherent, then, the threatened collapse of the distinction between central and modular systems does not occur. Limited, isotropic cognitive influence is thus compatible with Pylyshyn’s project of distinguishing isotropic central inference from anisotropic symbolic processing (Pylyshyn 1984: 330). Conversely, the idea of fully isotropic cognitive influence is hard to understand structurally because, as Burnston and Cohen (2015) note, it is hard to see how representations in one format could be arbitrarily translated to influence representations in different, lower-level formats.

The need to distinguish merely symbolic anisotropic early visual processes also shapes the constraints on what would count as penetrability. Mere shifts of attention won’t count, Pylyshyn thinks, because those can be explained purely in symbolic terms. If I believe the panda is moving, and I redirect my attention, then what I’m really doing is something like translating a belief about space back into symbolic terms, and feeding that symbolic representation back into some earlier symbol-using processes. While attention is the most salient escape clause, the more important thing about penetrability—captured by Stokes’ clause 2—is that it involves some kind of influence in virtue of the meaning of the penetrating relationship. As long as meaning isn’t involved, then the division between the intentional and the merely symbolic can be maintained, and the computationalist project survives.

3. The Descending Control of Pain

Return to pain. The architectural discussions have sharpened what’s at stake: we need to know whether the effect of cognition on pain is an effect of central states on symbolic processing. The nice thing about pain perception is that the mechanisms for the descending control of pain signals have been mapped out and extensively studied. These pathways provide mechanisms by which the cortex can affect incoming pain signals at the spinal level. I start with a brief review of the peripheral pain system, then focus on the mechanisms for descending control.

The skin and viscera contain a variety of nociceptors that feed information to the pain system. Nociceptors themselves are diverse in nature (Kandel, Schwartz, Jessell, et al. 2000: 473ff.), because possible insults to the body are so various. Some nociceptors are sensitive to ranges of heat or cold. Others are sensitive to mechanical or chemical insult. Many nociceptors are free nerve endings, rather than involving specialized organs. The picture is complicated further by the fact that even peripheral nociceptors are sensitive to context: the so-called silent nociceptors in the viscera evoke pain only in the presence of inflammation (McMahon & Koltzenburg 2006: 464). Furthermore, it has long been known that under the right contexts—especially inflammation—ordinarily non-nociceptive receptors like those for touch or stretch can evoke painful responses (McMahon & Koltzenburg 2006: 729ff.).

In addition to differing nociceptors there are also at least two main classes of nociceptive nerve, with mylenated Aδ fibers carrying fast information and nonmylenated C fibers slower, longer-term signals. This motley collection of inputs comes together in the dorsal horn of the spinal cord. The laminar organization of the dorsal circuitry is complex enough to perform various kinds of information processing before signals are passed on to the brain. A crucial feature of this dorsal circuitry is the so-called gate control. First proposed in schematic form by Melzack and Wall (1965), the spinal gate can act as a gain control on inbound signals—a sort of volume knob, if you like. Some inputs (sustained C-fiber firing, for example, or inflammatory compounds) increase the gain. When this goes wrong, one sees phenomena like allodynia, in which otherwise innocuous stimuli like light touch become extremely painful.⁸ Other inputs turn down the volume. Adjacent tactile input fed in by Aβ fibers often does so, which explains why rubbing an injury can make it hurt less. The view I express is schematic, of course; in a review of the current literature, Todd writes

It is now known that the neuronal organisation and synaptic circuitry of the region are far more complex than could have been imagined at the time of Melzack and Wall’s Gate Theory. However, the basic assumption that the superficial dorsal horn modulates nociceptive input is now universally accepted. (Todd 2017: 1)

A further key feature of gate control theory was the assumption of descending spinal pathways to allow for central control of gain. That is, among the inputs that modulate dorsal horn processing are nerves descending from the central nervous system. Central modulation of pain had long been proposed (Fields, Basbaum, & Heinricher 2006: 125). Gate control suggested a concrete mechanism—downward control of gain—by which this could be accomplished.

Several central mechanisms for selective downward modulation have since been elaborated (Fields et al. 2006). Core brainstem structures are the periaqueductal grey (PAG) and the rostral ventromedial medulla (RVM). The PAG is a structure that plays a vital role collecting and integrating information about a variety of stressors, forming an interface between brain and bodily responses (Benarroch 2012). The PAG affects the RVM directly, and the RVM in turn sends descending tracts to the dorsal horn circuitry that is implicated in gate control. The RVM contains both ‘ON’ and ‘OFF’ cells that, respectively, enhance and inhibit the dorsal spinal gate. An interesting feature of the RVM circuitry is that it is tonically active—there is always some descending control.

Cognitive modulation of pain is affected by a variety of different frontal circuits (Kong et al. 2013). As Fields et al. (2006) note, the connectivity between PAG and the rest of the brain gives straightforward way in which beliefs and desires can influence pain perception directly. The PAG also receives both ascending input from the periphery and downward input from the frontal lobe and amygdala. Some of these effects are attentional, while others are more direct. For example, they note that “contextual cues acquire the power to either increase or decrease the activity of nociceptive dorsal horn neurons in the absence of a noxious stimulus” through appropriate conditioning, and that this pathway may explain many well-known expectancy effects (Fields et al. 2006: 137).

However, the PAG–RVM axis, while not the only area responsive to or involved in placebo analgesia, appears to be something of a final common pathway through which these influences are funneled (Benedetti et al. 2005; Colloca et al. 2013). There is also direct evidence that the PAG– RVM circuit is a key mechanism in placebo effects. The PAG is one of the classic sites of action of opioids. Levine, Gordon, and Fields (1978) showed that placebo effect is modulated by endogenous opioids using the opioid antagonist naloxone. Functional imaging shows that the effect of naloxone on placebo response is mediated by the PAG–RVM circuit (Eippert et al. 2009a; Linnman et al. 2012). Attentional modulation of pain is also mediated by the PAG (Tracey et al. 2002), as are lesser-known descending inhibitory effects like offset analgesia (Derbyshire & Osborn 2009). Conversely, in rats, activation of on cells in the RVM produces enhanced sensitivity to pain (Neubert, Kincaid, & Heinricher 2004), neuropathic pain is associated with a loss of modulatory cells in the RVM (Leong et al. 2011), and modulating the RVM with lidocaine can both produce or alleviate allodynia depending on context (De Felice et al. 2011). Finally, there is substantial evidence that placebo effects act directly on the spinal circuitry involved in pain. The on and off cells in the RVM project to the dorsal horn of the spinal cord (Fields, Malick, & Burstein 1995). And finally, imaging of placebo analgesia shows that it has direct effects on spinal circuitry (Eippert et al. 2009b).

Return to the question at hand. Given the facts about descending modulation of pain, at what level does that top-down influence appear to be working? It doesn’t appear to be a central effect: placebo analgesia is not a matter of reasoning about what is the case. But nor does it appear to be an influence on some putative modular early processing either. The effects on the brainstem are already much lower down than what we think of as typical cognitive processing. The PAG is a midbrain structure that is otherwise involved in stereotyped behavior such as mating behavior. The medulla is typically associated with basic non-cognitive physiological functions: breathing, heart rate, vomiting, and the like. More importantly, neither seems to be involved in processing ascending information about pain: what is affected is not inbound processing.

Instead, the most plausible understanding of downward modulation is (I claim) that it targets the transducer for pain. That is, we ought to treat the peripheral systems up to and including the dorsal circuitry as the transducer for pain. Dorsal circuitry is well-posed to serve as part of a transducer. Its function is to make something meaningful from diverse inputs, including both neural and non-neural information that ranges across different spatial and temporal scales. The most proximal inputs don’t mean much on their own: it is only in context that this input means anything relevant to pain. Dorsal circuitry brings together that context and sends on an already-processed signal.⁹ The most peripheral sensory organs convey a lot of noisy information, much of it redundant; sending everything up to the brain would waste bandwidth. Better to process locally and send a consistent signal both up to the brain and back out to local circuits for immediate response.

Further support for the view of the dorsal horn as a computationally complex system comes from quantitative facts about the dorsal horn itself. Nearly all of the pain-related neurons in the superficial dorsal laminae are interneurons, with only a small fraction projecting onward to the brain. Overall there are about half as many inbound afferent inputs as there are interneurons, and about thirty times fewer projection neurons.¹⁰ Computationally speaking this setup exhibits a pattern of dimensionality expansion and reduction that is the hallmark of complex computation elsewhere in the nervous system. Furthermore, the interneurons themselves include a mix of excitatory and inhibitory neurons, each of which in turn include of complex and heterogenous subpopulations, creating further opportunity for complex local processing (Browne et al. 2020; Peirs & Seal 2016). Facts such as these lead Braz et al. to conclude that “The dorsal horn of the spinal cord is not merely a ‘relay station’ where primary afferents engage the projection neurons. Rather it is the locus of incredible integration, where sensory information is subjected to local and supraspinally derived excitatory and inhibitory regulation” (Braz et al. 2014: 526).

In conclusion, the mechanisms involved appear to funnel a variety of different influences from across the brain down through a single common pathway. That in turn affects spinal gating mechanisms and hence the inbound nociceptive signal. The effect on the inbound signal is at the spinal level, and occurs before the information fans out to the rest of the brain (which it does very widely in the case of pain). That means, however, that the dorsal circuitry would appear to be the primary place where information about insults to the body are translated into a common code and sent on for further analysis by the cognitive system.

Hence we have a possibility that was not anticipated in the original debate (for good reasons, as I’ll discuss shortly): downward modulation affects transducers for pain in a systematic, computationally complex way. Is this a sensible position? Is it meaningfully different from thinking about cognitive penetrability as such? I think the answer to both questions is yes; I’ll spell out why in the course of responding to some common objections.

4. A Defense of Transducer Calibration

Pylyshyn’s original setup left little room for the possibility of effects on transducers, in part because transducers are boring, basic units. As such, one might object to the proposal in several ways, none of which succeed.

5. Transducer Calibration

Cognitive penetration is classically presented as the effect of intentional states on sub-intentional processing—mixing memory and desire to stir the dull roots of early vision. The plausible mechanism identified for the downward control of pain, on the other hand, appears to be a mechanism by which intentional states can affect the operation of a transducer, by affecting the activity of something that is effectively treated as a computational primitive from the point of view of central cognition. I have suggested that this downward influence ought to be understood as a distinct process of transducer calibration. Like cognitive penetration, it involves (very broadly speaking) the influence of ‘later’ stages of processing on ‘earlier’ ones—indeed, because it is an influence on transduction, the effect of transducer calibration occurs about as early as possible. The topographic features of transducers demand it.

Understood this way, we can also explain why downward influences on pain would appear to have some features that look like penetrability and others that don’t. On the one hand, there is a sense in which transducer calibration needs to be strongly isotropic. Return to painless injury. As I noted above, Wall (1979a; 1979b; 2000) argued that painless injury is adaptive in cases where escape to safety is paramount. Pain tends to limit motion, which works contrary to escape. So it is adaptive to suppress pain until one is safe. The appraisal of personal safety would appear to be a classic case of an isotropic context, because almost any sort of information is potentially relevant: I am probably safe if I am in a hospital—unless I know that the mysterious slasher is one of the doctors!

So the potential intentional information that can affect transducer calibration is hard to bound. On the other hand, the structural facts about the calibration of the pain system—and in particular the fact that the downward effects appear to go through a bottleneck via the PAG and the RVM—suggest that in order to affect calibration, influences must be filtered down through a single, orderly, well-defined point of intervention. Unlike the all-to-all translation problem envisioned by Burnston (2017), then, the orderliness of transducers suggests a straightforward way in which transducer calibration might occur. This would also explain why you can’t just think your way out of pain: while many beliefs and desires might be potentially relevant to transducer calibration, we should expect the downward filtering process to weight these in a survival-appropriate way.

Where does the shift to cognitive calibration leave debates over cognitive penetrability? That depends what you care about. If the hope was to use top-down influences on pain to defend classic cognitive penetrability in other—especially visual—domains, then I suspect that this will be something of a letdown: Shevlin and Friesen (2021)’s conclusion that this does not generalize is probably correct.

On the other hand, if you are interested in better picture of the architecture of cognition, then I think the role of transducers remains a rich one ripe for further exploration. I conclude with some speculative reflections on the architecture of cognition and the differentiation between different types of top-down influence.

To start, note that the appeal to cognitive calibration provides explanatory resources that are absent from traditional accounts. So, for example, Casser and Clarke have a brief discussion of influences on the spine, setting them aside “merely eliciting pre-inferentual influences” (Casser & Clarke 2022: 14). That is typical of traditional accounts which, following Pylyshyn, ignore transducers. Yet when it comes to the effect of practical assessment on pain, they struggle to find a mechanism by which appraisals of the situation could systematically influence pain. They suggest that there might be other low-level sensory processes that reliably correlate with danger and which could influence pain perception. But they offer no evidence for this, it is difficult to square with other evidence, and does not explain (e.g.) other phenomena like nocebo effects or downward inhibition in the absence of danger. So as it stands, the explanation seems more or less ad hoc. Yet the need for ad hoc explanation stems entirely from the fact that they have tied their hands with respect to the complexity of downward influence on transducers. Once one acknowledges that there are orderly but computationally complex mechanisms by which transducers can modulate pain signals in response to beliefs and desires, one can give more satisfying general explanations that cover a variety of phenomena.

Indeed, while I have focused on pain, I suspect that cases of cognitive calibration might be relatively widespread. Other bodily sensations like hunger and thirst are influenced by anticipation and memory (Kandel et al. 2000: 1007ff.); it is an open question how those effects work. Even the more classic informative modalities might show calibration effects. The eye is adaptively controlled in many ways: where it points, the focus of the lens, and the diameter of the pupil are all at least partially under central control. There is also good evidence that the cochlea has specialized cells that create sound in order to selectively enhance certain frequencies; this is probably under descending control, and the sound produced varies adaptively with task (LeMasurier & Gillespie 2005). Conversely, failures of calibration often explain otherwise puzzling perceptual phenomena. Tinnitus can be caused when aberrant cochlear sounds overwhelm ordinary ones (Noreña 2015). The IASP now recognizes that many pain syndromes may occur solely due to dysfunctions within the calibration of the pain system itself (Kosek et al. 2016).

Transducer calibration, then, is unlikely to be limited just to pains, or even just to bodily sensations. Furthermore, the more complex the picture of transduction one adopts, the more difficult it becomes to draw the transduction/cognition boundary in the first place. That is not to say that it cannot be drawn, but rather that doing so requires taking into account both computational and topological features of computational processes involved in cognition. We cannot just look to the periphery and call it a day.

Indeed, in a curious passage from Pylyshyn that deserves more attention than it has received, he writes:

As a final remark it might be noted that even a post-perceptual decision process can, with time and repetition, become automatized and cognitively impenetrable, and therefore indistinguishable from the encapsulated visual system. Such automatization creates what I have elsewhere (Pylyshyn 1984) referred to as “compiled transducers.” Compiling complex new transducers is a process by which post-perceptual processing can become part of perception. (Pylyshyn 1999: 360)¹⁶

This is something of a striking admission: it suggests that the automatization of perception via the training of cortical networks makes entirely new transducers. Yet this is not implausible either: cortical networks are extremely good at classifying complex inputs—and classification is a natural basis for transduction.¹⁷

Putting aside these complex boundary problems, however, I think that transducer calibration gives us a starting point from which we can usefully taxonomize different types of top-down influence. That is, rather than just divide phenomena into penetration and non-penetration, we can use various topological and computational properties to divide up ways in which cognition might affect other processes.

For starters, recall the fan-in structure of information flow involved in calibration. Transducer calibration appears to have a many-to-one structure: an indefinitely large set of influences is funneled through a final common pathway in order to calibrate a transducer, which in turn broadcasts widely.

We may distinguish other putative top-down influences in the same way. Shifts in attention are often treated as akin to moving one’s head (see, e.g. Siegel 2012: 203). What they have in common is often obscure—but not that both processes involve a one-to-many top-down influence: moving your head changes a great number of things, both on the retina and elsewhere. Similarly, if a ‘shift in attention’ is to be more than an undischarged promise, it must involve a global shift towards some sorts of processing and away from other kinds of processing.

Thinking of attentional effects this way also makes clear that some kinds of competitive processes should not be put in the same category, precisely because they seem to have more limited scope. As Mole (2015) points out, there are attentional phenomena that are clearly different in kind. Suppose that attention sometimes does its work via biasing processes that are interacting by competitive inhibition—think of the role of attention in seeing the Necker cube this way rather than that, or naming a Stroop color rather than reading the word. These processes don’t seem akin to merely shifting one’s head: instead, they appear to be internal effects in the cognitive system themselves. So the one-to-many criterion seems to do real work in disentangling putative defeaters for penetrability claims.

Finally, one might split claims about classic cognitive penetrability into two kinds. On the one hand, there are one-to-one top down effects: beliefs about the color of a banana affect, in a targeted and semantically coherent way, processing of a banana-shape’s color. This seems to me to be the sort of influence that Burnston and Cohen (2015) quite rightly point out is conceptually unproblematic if true: we should expect it to be anisotropic if it occurs, and it is mostly an empirical question about whether it does. On the other hand, there might be many-to-many top-down effects. Pylyshyn’s concern about unrestricted, isotropic top-down influence seems like a concern with this sort of relationship. The picture of top-down influence embodied in recent big-picture predictive coding accounts (Clark 2015; Hohwy 2013) often seems to suggest that there should be this kind of many-many relationship.¹⁸ This is also, of course, the sort of top-down influence that has much more difficulty accounting for failures of penetration. And, of course, one-one and many-many are graded notions: there are likely interesting intermediate cases.

To return to pain and sum up: we began with a question about whether top-down effects on pain ought to count as cognitive penetration. In spelling out the classic picture of penetration, we discovered Pylyshyn’s austere picture of transduction. This combined a topological claim (transducers are the maximally peripheral bits of the cognitive system) with an architectural one (transducers are effectively non-computational). The downward calibration of pain showed us that neither claim was necessary, and both were hard to defend. The debates over penetrability have tended to focus on whether an effect is penetrable, looking to see whether a standard set of defeaters for penetrability holds. The present work belongs in a smaller tradition that suggests that these putative defeaters (like transducer modulation) are in fact architecturally interesting and complex enough to be worthy of sustained attention in their own right.

Furthermore, the calibration of pain provides an excellent, empirically grounded illustration of how the many-to-one calibration process might work. It is notable only in the level of empirical attention it has received, however: it is unlikely to be unique. The real lesson of top-down influences on pain, I suggest, is that questions about what is and isn’t cognitive penetration cannot be answered piecemeal: they require a broad theory about the architecture of the cognitive system as a whole and the and information flows within it. Pain is not special—but, as ever, it is terribly illustrative.