Finding Useful Concepts of Representation in Cognitive Neuroscience: A new tactic for addressing dynamical critiques of representational models of cognition, action and perception

A dissertation submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Philosophy of the McMicken College of Arts and Science by Jonathan Martin

1 Abstract

Since Timothy van Gelder's 1995 paper “What Might Cognition be if not Computation?” there has been a growing distrust of representational accounts of cognition, most notably from proponents of relatively new programs like dynamical systems theory and ecological/embodied approaches to cognition. Some advocates of these programs have argued that the concept of

“representation” is deeply flawed – does little explanatory work, prejudices what is researched and how results are interpreted, and loses sight of the complex interactions that hold between agents and their environments. In this dissertation, I will not try to challenge these claims.

Instead, I will argue that responses from defenders of representational modeling often fail to adequately meet these challenges, underestimate their insights, and have a tendency to react by redefining “representation” in such a way that it loses its explanatory significance. Although I believe that the ultimate shape and utility of representational models of cognition will only be revealed in the course of doing science, I will advocate a new tack for defending representational modeling. I begin by acknowledging that representational terminology is indeed used inconsistently, and sometimes without warrant or explanatory power. Next, I examine cases from the cognitive neuroscience to try to show that we can distinguish between explanations whose representational features are closely tied to their explanatory ambitions and those that are not. I will argue that the representationalist should emphasize that information-processing solutions are contingent strategies available to organisms for producing adaptive behavior – as are varieties of synergistic agent-environmental coupling – and, therefore, there should be ways of marshaling evidence for or against the hypothesis that any particular cognitive/perceptual capacity is the result of a representational solution. She can

2 then point out that explanatory representational models in neuroscience provide concrete examples of the kinds of empirical considerations and observations that can be used to motivate attributions of representational significance. They do so by bringing together disparate nervous systems and their components under reoccurring principles of anatomical organization, processing strategies, encoding methods, etc., and these produce testable empirical predictions, suggest interventions, unify phenomena as instances of a more general pattern, and exemplify other commonly lauded scientific virtues. A defense of representational modeling should demonstrate that some of our best explanations in cognitive neuroscience do make central use of representational theorizing for securing their explanatory goals. This can be done while also admitting that there may be many cases where organisms adopted non- representational solutions to navigating their environment. Adjusting metaphysical definitions of “representation” to include even apparently non-representational processes is unconvincing and fails to appreciate the reasons that animate representational explanations.

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4 Chapter 1: Introduction

In attempting to clarify or discover the explanatory function of information-processing models, it will be helpful to first get a grasp of why this historically central aspect of cognitive science has recently come under fire. For purposes of brevity, I will assume a basic familiarity with the two major programs have been paramount among representational approaches to cognition – Classical Cognitive Science and Connectionism – although I will provide explanations and examples of these views. I should also note that I will use terms like “information processing,” “computation” and “representation” interchangeably, without implying any particular computational architecture –from classical Symbol Systems models, to production systems and Good Old-Fashioned Artificial Intelligence, to Connectionist Artificial Neural

Networks, to neuroscientists’ claim to find “central representation of X” in some neural structure or activity. While the posited mechanisms differ, what these research programs all have in common is that they all rely heavily on the idea that at least much of cognition, action, and perception can be explained in terms of how information is taken-in, stored, and transformed mechanistically (usually in the brain). Criticism of various proposed representational posits have been a lively part of cognitive science for some time now; debates about the appropriate computational architecture for modeling cognition occupied computationalists and connectionist researchers throughout the 1980s and 90s. But, more recently, the status of representational explanations in general has been increasingly called into question.

The majority of skepticism about representations has come from proponents of

5 embodied, dynamical, and ecological approaches to cognition. And, while each of these approaches is home to sometimes quite distinct sets of commitments and research interests within their own communities, the most pressing challenges to representation stem from a set of claims and observations that are common to all three approaches. Accordingly, anti- representationalist accounts often advocate a hybrid of these approaches to replace traditional representational models. In what follows, we will look at the reasons that motivate some theorists to question whether or not cognition is best explained in terms of representation, information-processing, or computation, and I will identify the main theoretical commitments and emphases that unite anti-representationalist arguments across these approaches using exemplar cases from empirical work. I will try to show that the challenge posed by this framework to traditional modeling is a serious one; not only does a survey of the cases give us good reasons to think that it may be possible to model much of cognition, action, and perception without representation or information-processing, but there seem to be good arguments for believing that representational assumptions often leads us astray. I begin with this exposition because I think that, for the most part, advocates defending representation quite often fail to appreciate the force of these arguments and, consequently, do not address them adequately. I will then try to formulate how I think the defender of representation can take their opponents arguments seriously while offering an argument in favor of some representational modeling that is grounded in neuroscientific practice. But let us start by looking first at where all this skepticism comes from.

6 Chapter 2: Tightly-Coupled Cognition and Dynamical Arguments Against Representation

Arguments against representation-laden models have come from a variety of sources.

Within these distinct theoretical frameworks, those who advocate abandoning representational models often rely on a common set of premises. When these premises are employed for antirepresentational purposes, I will call such views Tightly Coupled Cognition (TCC). The following claims are common to what I am labeling TCC approaches:

C1: In (at least) many cases of perception and action, the environment is informationally rich enough to govern an agent's successful navigation of a cognitive task. C2: There are models whose characterization of certain organism-environment couplings provide satisfactory explanations of the accomplishment of a perceptual or cognitive task that do not implicate representation or information processing. C3: The class of dynamical systems taken as paradigmatic cases by TCC involve such intimate organism-environmental couplings that they lack the kind of isolatable, interpretable states to which representational explanations typically ascribe informational content.

C1-C3 should be read as empirical claims made on the basis of existing models and explanations. Each can, on its own, provide some reason for thinking that traditional representational models are not necessary for understanding at least some substantial portion of perceptual and cognitive phenomena. Taken individually, or as a group, they are insufficient to prove that representational modeling in all cases is ill-advised. Still, it is not necessary (or fair) to require that advocates of TCC demonstrate such a strong thesis conclusively in order to see the force of arguments made on the basis of C1-C3. As I will argue, the empirical results that support these claims are reason enough to shake one's confidence in the received view.

There are a few important dimensions to claim C1 that ought to be pointed out before proceeding. First, C1 is partly – and most explicitly – a claim about the nature of the

7 environment. Specifically, it requires of some cognitive agent's environment – usually some causal features of objects proximal to the agent in question – that it contains information that can be exploited toward the execution of some action without calling upon some additional informational resource. It is important to note, however, that the notion of information here is often interestingly agent-centric: a room with the lights off may not be informationally rich enough to support basketball-passing behavior, while turning on the lights might endow the agent's environment with sufficient information to guide successful basketball-passing. After the informational interface between the agent, the ambient light, and the recipient of the pass has been restored, information about the recipient's distance, trajectory etc. relative to the agent can now be used to guide the force and direction of his/her toss. The sense of informational richness operating here emphasizes the idea that, relative to some system capable of making use of regularities in the environment in the right way, sufficient information needed to guide the pass was, in some important sense, in the light. The importance of this assumption about the environment will become clearer when we look at individual cases of supposed exploitation of information by an agent-environment system.

The second claim, C2, is a statement about the current empirical situation as regards explanations of cognition, perception, and action; namely, that there are models and explanations on offer that are satisfactory but do not rely on any (even tacit) notion of representation. It also relies heavily upon the notion of agent-environmental coupling, a concept that will get some more serious unpacking as we look at examples. When looking at C2, however, it will be important to note that some of the models on offer in TCC-style accounts – especially those that rely heavily on dynamical or covering-law style explanations – have been

8 looked at as redescriptions rather than explanations of psychological phenomena. In some cases, these objections do bring important philosophical and pre-theoretical questions into the light, but there are also many cases where it is much clearer that the psychological phenomenon is being explained rather than characterized with a new conceptual apparatus. In addition to this worry, C2 invites philosophical questions such as “what are you counting as cognition?” and “by what criteria are we to call a specific explanation of cognitive activity satisfactory?” But, if representational cognitive science is to get a grip on whatever special contribution representation plays in explanations, it ought to – in light of the empirical support for C2 – have a better reply than dismissing all non-representational work as unrelated to cognition or lacking in explanatory power merely because it is non-representational.

Finally, C3 invites a discussion of a few of the concepts that will end up being central to much of this chapter. For one thing, C3 makes use of a notion of “intimate organism- environmental coupling” that is common to C1-C3 and will come into sharper focus as we look at the novel conception of cognitive activity that motivates much of TCC. In contrast to what

Susan Hurley (1998) has dubbed the “classical sandwich” model that has dominated both classical and traditional (especially feed-forward) connectionist explanations – in which

“cognitive activity” was seen predominantly as some computational task to be performed in between afferent sensory inputs for the production of motor outputs – TCC avails itself of more recent insights in the way cognition can be viewed as taking place across systems composed of nervous systems, bodies, and their surroundings. Instead of seeing the brain as a central locus of control, a view emerges in which the nervous system, body, and environment are engaged in reciprocal interplay of causal interactions for which no one element is best viewed as the

9 “controller.”

A system is said to be self-organized when it undergoes a transition from a state of disorder toward the formation of an ordered and coherent whole despite lacking any centralized system of governance or control. C3 is also motivated by claims that aspects of both nervous system activity and active bodily engagement in an environment exhibit the signatures of self- organized systems – also known as interaction-dominant systems. In interaction-dominant systems it is the continuous interaction of the individual components of the system that gives rise to the order within the system's overall dynamics, while these global features of organization oftentimes simultaneously condition the contribution and dynamics of the individual parts, thus maintaining organization (Kelso 15-17). To take a simple example, think of a toilet flushing: the water molecules closest to the drainpipe are pulled down by suction. The surrounding water molecules are then pulled toward the drain, with friction between the individual molecules slowing the movement of the molecules more and more the further away they are from the center. The differences in momentum of the molecules as you move from the center of the suction forms the familiar vortex pattern as molecules pushed to the outside are gradually pulled toward the center. The resulting vortex of the mass of water molecules itself begins to carry the individual molecules along, rotating like a solid body – carrying the molecules along by the fluid motion of the entire mass of liquid. In this process, the individual behavior of the molecules gives rise to a global order or pattern that constrains the degrees of freedom of their individual motions, spontaneously organizing their behavior. In cases like this the continuous feedback between the parts and the whole render the contribution of individual system components highly context-dependent – each part's activity both determines and is

10 determined by the simultaneous activity of the other components, and the behavior of the components in the interaction-dominant system are quite different than in isolation. This dependence on context makes trouble for assigning a determinate function or role to the individual parts of a interaction-dominant system – a difficulty that undermines certain varieties of mechanistic characterizations of cognitive function (Anderson et al., 3).

Compare this to component-dominant systems; systems “composed of a series of parts, each of which has a particular role that it fulfills,” often characterized by the kind of rigid, task- specific cognitive architectures that populate classical and connectionist models of cognitive processing (2). Component-dominant systems often decompose neatly into functional units of a larger mechanism, where (in the context of some cognitive task) each functional component engages in a context-invariant operation that contributes in a systematic way to the end result.

Examples of such systems include things such as pocket calculators, window A/C units, and the algorithms executed in production system artificial intelligence. In each of these examples, systems were designed so that the interactions of each part are orderly and linear, minimizing the interactions among parts except where such interaction has a systematic role in the system's behavior – the CPU in a computer's retrieving some instructions from the computer's

RAM. Each component of component-dominant systems has a determinate function, regardless of the context, even if the global behavior of the system is versatile and can function in a variety of contexts (such as a personal computer). By way of contrast, in interaction-dominant systems the context of the global dynamics itself is being determined and determining the behavior of the parts – parts whose contribution to the system would be altered dramatically if the context changes.

11 Humans and other animals are remarkably flexible with regard to changing contexts, and the effects of context-dependence on cognition can be observed experimentally. To take one example, children's performance in predicting the movement of a balance scale fitted with weights can has been shown to be highly dependent on a number of factors; providing or withdrawing feedback, altering the range of permissible answers, allowing the children to hold the weights first etc. all seem to alter children's perception of the balance beam as well as the strategies they adopt for making their prediction (Kloos and Van Orden, 253-257). But, as Heidi

Kloos and Guy Van Orden argue, “[context] effects permeate the brain and body well below the level of trial judgments in particular tasks...[as] each instantaneous muscle flex and each pattern of rhythmic cortical firing creates a context for every other muscle flex and cortical firing (262).”

If this suggestion is right, then (at least many) of the biological systems that facilitate perception and action will have components whose contribution is simultaneously determining and determined by the environmental and bodily contexts, making mischief for any project of ascribing roles to these components. In light of this, C3 suggests that the types of information processing roles ordinarily associated with representational explanations are just the kinds of determinate roles that will likely be lost in models that take into account the mutual constraints between parts and wholes in interaction-dominant dynamical systems. With C1-C3 in mind, we should now be ready to look at individual instances of TCC-style explanations in action.

Case 1: Harrison and Richardson's Self-Assembling Quadruped

A good first case can be found in an experiment described in Steven J. Harrison and

Michael J. Richardson's 2009 paper “Horsing Around: Spontaneous Four-legged Coordination,”

12 as it presents a case of a dynamical treatment of a perception-action system that exhibits a number of the tell-tale features of a TCC-style model. Synchronization is a conspicuously common occurrence in biological organisms. It is found at multiple levels of cellular organization in the form of circadian rhythms in intact and cultured tissues (Winfree, 1967), in networks of neurons integrating auditory and motor signaling (Mayville et al., 2002), the lighting of groups of East-Asian fireflies (Buck, 1988), menstrual cycles in groups of women (McClintock, 1971), amongst many other examples. Similarly, synchronous oscillations of this kind re-occur in the stereotyped gait patterns of organisms, from two-legged animals such as humans to hexapodal arthropods. Moreover, as the authors note, the “stable gait patterns of quadrupeds are still observed even when the central or peripheral nervous systems of the creatures being examined are removed (Harrison and Richardson, 1).” Inspired by this observation, as well as Haken, Kelso and Bunz's (1985) use of dynamical methods for modeling other areas in coordination dynamics, Harrison and Richardson proposed treating observable regularities in walking behavior as “the result of self-organization” and emerging from “nonlinear, yet lawful, multilevel interactions that occur among the many perceptual-motor components of biological movement systems (1).”

To test this, they devised an experiment: two subjects were asked to walk on a treadmill together under various coupling conditions, predicting that stable four-legged coordination patterns would be produced spontaneously and without the subjects' knowledge, and that the degree to which the subjects were coupled would determine the form of the coordination pattern adopted. On top of this, they predicted that the speed of the treadmill could act as a control parameter for the entire person-person-treadmill system, determining when critical

13 shifts in the overall dynamics of the system underwent qualitative changes. 12 participants who knew nothing of the purposes of the experiment were put into 6 pairs. After each was observed for a control condition of no coupling (condition NC) each was asked to walk on a treadmill .75 meters apart from each other in a line, with both subjects facing the same direction. In this configuration, the pairs were monitored in 3 coupling conditions. In the first of these conditions, the rear subject was asked to maintain the .75-meter distance behind his partner. Because this distance was to be maintained on the basis of his visual appraisal, this was known as the “visual coupling” or “VC” condition. The second condition involved purely mechanical coupling (MC), as the rear subject was blindfolded and attached to his partner by a fairly solid .75-meter foam block. The final condition was a combined visual and mechanical coupling (VMC), that simply removed the blindfold from the rear subject – restoring the influence of visual information.

In 32 trials with each participant – 4 trials for each coupling variety – limb coordination patterns were measured in terms of the relative phase of each limb in relation to a reference limb (here, the right limb of the front participant). The relative phase (Ф) of the limbs – the temporal relationship between their oscillatory cycles – was sampled via recordings of the movements of the upper and lower surfaces of each participant's legs, with the left-front and hind-back legs' movement compared to the front-right “target” limb. Looking at these patterns in limb movement, each limb could be appraised in terms of its peak extension's alignment with some portion of the target limb's oscillatory cycle at that time, represented as some value within a range of 0°-360°. On this basis, each limb's oscillation's “mean relative phase (MФ) and standard deviation of relative phase (SDФ) were used to assess the form and stability of the coordination (2),” with any two limbs being phase-locked when SDФ<20°. This analysis was

14 simplified by the observation of the fact that any individual participant's legs were consistently locked in an anti-phase pattern – whenever the right leg was fully flexed the left leg would be fully extended. Because of this consistent lateral symmetry, a single collective variable could be used to portray the overall four-legged dynamical system: the relative phase of the back and front right limbs. In individual trials phase-locked patterns were categorized into one of four stereotyped gaits, reflecting the total pattern of the four-legged dynamical system: pace (PACE), trot (TROT), lateral walk (LATW), or diagonal walk (DIAW), respectively., with each gait type corresponding to some range of the values for the order parameter of the front and back right limbs (MФBR), where an order parameter is the value that determines when the system will undergo a transition from one stable pattern to a qualitatively different one (I'll return to this in more detail in my discussion of Case 3). With the general conceptual set-up in mind, I will now move on to analyze the results and explanations in light of the three central claims of TCC.

As Harrison and Richardson report, their prediction that visual and mechanical coupling would “result in the spontaneous synchronization of the leg movements of the front and back participants” was corroborated by their observations; relative to the non-coupling condition,

“VC, MC, and VMC couplings each [resulted] in an increase in the observed frequency of phase locked trials (3).” As was expected, an increase in the amount of coupling was correlated with a higher frequency of phase-locked locomotion, with NC-pairings attaining phase-locked coordination (presumably by chance) in only 8% of trials, compared to 40% for VC, 63% for MC, and 77% in VMC.

What's more, there were a number of interesting features of this coordinated behavior.

For one thing, the observation of this significant increase in phase-locked four-legged

15 locomotion seems to indicate that coupling between the two participants, whether through mechanical connection or visual information, served as a basis for stabilizing the overall dynamics of the person-person-treadmill system. Furthermore, altering the speed of the treadmill decreased overall stability of the observed gaits; walking was phase-locked almost twice as often as jogging – in support of the view that speed could be treated as a control parameter for the system (a result that coincides nicely, as we will see later in this chapter, with other research in coordination dynamics). But perhaps most interesting is the fact that there were two gait patterns that were observed almost to the exclusion of all others – PACE and

TROT – and that “the relative frequency of these patterns varied as a function of coupling (3),” with the only exception occurring in the non-coupled trials. In other words, across participants, coupling resulted spontaneously in two preferred patterns of synchronous oscillatory behavior.

And, not only was this synchronization selective of two major overall patterns, the patterns were typical gaits for biological quadrupeds, with different coupling conditions resulting in different preferred gaits.

Presenting this case as a first example of one type of TCC-style account involves examining the way the various phenomena in “Horsing Around” were explained. Explanations of the various regularities observed across subjects were couched in terms of the flow or disruption of flow of mechanical or visual information across the system. For example, when it was noted that visual coupling seemed to stabilize different gait types in different coupling set- ups – stabilizing PACE in VC and TROT in VCM – it was concluded that the block's obscuring of the front subject's legs in the VCM condition disrupted the flow of leg-movement information to the rear participant. Because only the subject’s shoulders could be observed in VCM, the back

16 subject had only the mechanical information imposed by the foam block's connection combined with the visual 180°-out-of-phase movement of the front subject's shoulders, favoring anti- phase coupling between the rear and front right legs (6).

As we saw above, the general regularities received similar types of explanation; certain ways of hooking up the person-person-treadmill system resulted in the generation of stable quadrupedal coordination and the form and stability of the system's dynamics could be captured by (MФBR). There are a number of key points to be brought out here. While any individual case would not be sufficient to establish C1, the spontaneous generation of the pseudo-quadruped provides a case-in-point in support of C1. There are a couple of things to note here. As it is my first exemplar case for TCC, I will shelve the more nuanced aspects of answering the objection that the coordination involved in the quadruped is a sufficiently cognitive task to ground C1. It is certainly a case of a perception-action system, and the integration of sensory information with limb position in successful walking (under any constraints) seems like a case of such an action-perception circuit. But, if such an appeal to tradition is legitimate, the guidance of locomotion has certainly been treated as within the purview of cognitive, perceptual, and computational theorizing, as over a century of literature on the role of central pattern generators (CPGs) in the nervous system attests (e.g. Brown, 1911;

Guertin, 2009). Harrison and Richardson's results, insofar as they continue to be borne out empirically, have a solid place in that discussion; if the informational coupling between the participants and the treadmill are sufficient for quadrupedal locomotion, then the putative explanatory need to posit internal information and processing structures such as CPGs might be diminished. After all, because the pseudo-quadruped (unlike a horse) lacks a spinal cord

17 maintaining the connection between the various limbs and quadrupedal locomotion is certainly not an innate faculty of humans, it seems that innate pattern generating mechanisms are, at least, not required for certain forms of quadrupedal locomotion. Thus, in these explanations, mechanical and visual information are, in a way, of a single kind – avenues for the maintenance and regulation of oscillatory synchronizations between limbs and the spinal cord in biological quadrupeds might be just another way of maintaining informational coupling of this kind (or so one might argue). This offloading of processing responsibility onto the agent-environment system itself highlights both the second important aspect of C1 and begins to underscore C2. If the quadrupedal coordination can be maintained – as well as modeled, predicted, and controlled on the basis of the dynamics of this system of oscillators – then, for the same reason that it diminished the explanatory necessity of in-born processing mechanisms such as CPGs, we have reason to believe that it is the dynamic flow of information across the system that is required to explain the coordinated behavior. That is, the environment is informationally rich enough, relative to the coupled system in question, to govern successful navigation of a perception-action system (C1). The dynamics of the quadrupedal system are determined, in some strong sense, by the informational coupling of the two participants (whose relative phase is the order parameter) while simultaneously being determined by the speed of the treadmill

(the control parameter). The informational flow between these elements are sufficient to govern the behavior (C1) and, at least as far as we've seen so far, there does not seem to be any need to posit internal information processing states in order to explain this regularity (C2).

This case can also be used to provide evidence for C3. While the flow of information through the pseudo-quadruped plays a vital role in explaining the differences in the preferred

18 gait type and stability adopted, there is no clear role for interpreting any of the individual components of the person-person-treadmill system as having isolatable information-bearing functions. Though the system established in the experimental condition has a global coherence and tendency toward self-organization, like many self-organized systems, each of the parts of the system are continuously affecting and being affected by one another and, thus, the assigning a particular representational content or functional role to the individual elements of the system becomes difficult. In fact, these interaction-dominant dynamics are largely responsible for the way information-flow through the system makes its main contribution: the quadrupedal system's tendency to settle into specific patterns of locomotion is determined by the visual and mechanical sources of information, but the treadmill speed is simultaneously conditioning the visual and mechanical information available.

Case 2: Lee and Reddish's Ecological Optics

Research that goes under the heading of “ecological psychology” aims to understand the basis of real-time coordination of organisms moving about in an environment. Usually, such work emphasizes the exploitation of information made available relative to an agent actively engaged in coordinated behavior. James Gibson, one of the founders of the ecological school, famously rejected the received view in the psychology of perception in arguing that perception is primarily an active process of exploring and making use of information-bearing regularities in an environment, rather than a process of passive reception of sensory data for use in the construction of internal mental models. Gibson's The Perception of the Visual World (1950), The

Senses Considered as Perceptual Systems (1966), and his The Ecological Approach to Visual

19 Perception (1979) provide a novel conceptual framework for studying how agents actively sample the information contained in their surroundings in real time. His development of concepts such as ambient optic array, surface texture, affordances, and optic flow (amongst others) calls attention to how structural regularities in an environment themselves carry information, and these regularities can be picked up directly by agents – relieving the brain of the task of constructing appropriate information via informationally impoverished sensory inputs. Similar to the way the role of information-flow through the pseudo-quadruped reduces the temptation to invoke neural pattern generators, Gibson argued that paying attention to the way active agents explore and pick up on information in their surroundings reduces the need to implicate internal constructions of models of the environment – what Anthony Chemero calls

“mental gymnastics (Chemero, 2009)”.

If none of this sounds all that radical today, it is sufficient merely to remind oneself of the kinds of accounts of vision being produced by more mainstream cognitive scientists. David

Marr's computational approach to vision provides a classic example of the kind of mental gymnastics ecological psychologists reject. Marr (1982) proposed that explaining 3-D vision be approached as an information processing task; early visual processing begins with a 2-D image projected onto each retina that must be transformed by a hierarchically organized series of specialized processing stages. On Marr's model, early stages of visual processing include the construction of a “primal sketch” where basic intensity values for each retinal cell are represented in terms of their relative positions in the 2-D field (44-60). From this sketch, Marr hypothesizes, the sketch is run through filters that make use of contrasts in light intensity (zero- crossings) to derive a map of boundaries representing edges and regions between them.

20 Following these early processing stages, a second series of computations on this representation produce a further representation that includes features such as the texture of surfaces etc. The final stage in the proposed hierarchy involves using the disparity between the two eyes inputs to construct a 3-D image of the visual scene.

This is, of course, an admittedly oversimplified introduction to Marr's account, but there are three key points that will allow me to put the view into a starker contrast with the ecological picture. First, on Marr's view one of the primary functions of binocular vision is the production of a 3-D representation of the visual scene. Secondly, in order to represent the scene accurately, visual processing mechanisms must be able to rely on either hard-wired or learned assumptions that are either implicitly represented in the overall architecture of the visual system or explicitly represented in the computational algorithms that underlie each stage of visual processing. Take, for example, the fact that under ordinary conditions each eye receives a retinal image within some small range of disparity, because they are a fixed distance from each other. The way the visual system is arranged allows for this disparity to be determined by a computational comparison of the two visual representations produced. This disparity is then used, on this view, to produce the 3-D visual representation by properly integrating information from the visual processes downstream of each eye (99-100). And, finally, it is important to note the way that

Marr views the retinal image as informationally impoverished; the retinal image by itself does not provide the information required to produce certain behaviors (here, to allow for the binocular appreciation of depth). For this something extra is needed – for example, the computational rules or assumptions Marr suggests are embodied in our visual machinery to reliably use certain patterns in light intensity and contrast using two 2-D images to infer edges

21 and boundaries in the actual 3-D environment.

Contrast Marr's view with how Gibson might approach the same phenomenon. A person walks through a room. At every moment, a certain pattern in the optic array – the pattern of light impinging upon the retina – is entering the eye, projected off the surrounding surfaces.

But, unlike Marr's approach to vision, Gibson reverses the story by looking outward at the information contained in the optic array itself. As the person moves through the room, Gibson claims, the optic array will constantly change as light projected from the various surfaces of the room flow over her retina. The shifting pattern in a moving agent's optic array is known as optic flow. But, though the optic flow is constantly shifting as the person moves, there will be structural regularities that persist throughout her movement. Optic flow is a particular variety of the kind of shifting patterns in an optic array that exhibit reliable structural invariances as an organism moves about its environment and, as such, presents a potentially rich source of information for the visual control of coordination. These regularities are the structure given to the light by the surfaces of the room itself and contain an abundance of information. That is, as one moves through a space, the forms projected in the optic array shift, but there are certain geometrical relationships that are preserved. And, since specific shapes of objects will deform in a law-like way in the optic array, regularities in the deformation of forms in the optic flow provide a method for determining the layout of surfaces in one's surroundings. For example, certain surfaces – say, the top of a rectangular desk – reflect a trapezoidal projection that slowly deforms from its original shape as she approaches, but maintains its trapezoidal form (or a rectangle if she looks at it from directly above). This provides information about the size and shape of the desk, as well as the distances of its corners relative to the observer. In addition to

22 this, the texture of the desk's surface will also systematically affect the optic array, allowing our agent to glean information about the material and texture of the surface. As the subject's head moves from side to side, closer surfaces will scroll across the visual array more quickly than those further back, due to parallax, providing information about room depth and the spatial arrangement of the surfaces. Compare this hypothetical person's situation, as Gibson does, to one where the subject is in a fog-filled room where the fog scatters the light uniformly; the optic array available to this subject would be unstructured, and therefore, would be lacking in information for the subject. This comparison, hopefully, makes it clearer how, for Gibson, the information available to active perceivers is literally in the light to be discovered, due to the systematic regularities in how surfaces reflect light and the way optic arrays shift as the subject moves through her environment (Gibson, 1979). The optic flow field, pioneered by Gibson, can be visualized as a circular space with a central point from which texture elements radiate toward the periphery (when one moves forward), expanding in apparent size as you get closer to the surfaces you are approaching. As your point of observation comes to intersect different portions of the structured light, the surfaces' projections in your optical flow field will expand at different rates, depending on their size and distance. The relative rate that projected surfaces expand in the optic array as the subject moves forward makes available information about the distance of objects, as objects further in the distance will appear to expand within the optic array at a slower rate.

In his 1980 paper “The Optic Flow Field: The Foundation of Vision,” David Lee argues that optic flow provides a unique point of departure from the standard models of bodily coordination that had dominated earlier research. As he describes, the wide range of rhythmic

23 patterns observed in walking and other similar behaviors suggested that coordinated rhythmic behavior is unlikely to result from simple mechanical properties of limbs. Instead, some argued,

“there must exist in the central nervous system exact 'formulae of movement'” that generate rhythmic patterns via a plan or program (Lee, 1980, 169). However, if these internal neural schemata were inflexible – merely reproducing some fixed sequence of muscular contractions – then we would not observe the kind of robustness against changes in external forces and conditions, and thus there could not be a 1:1 correspondence between the motor routines and the eventual pattern of motion. And, as cases like the pseudo-quadruped attest, there is evidence to suggest that the interplay between body and environment itself can establish self- regulating systems capable of stable, flexible coordination. Following Gibson (1958), Lee proposed that a “comprehensive theory of perceptuo-motor coordination must address the question: What types of information are required in controlling movement relative to the environment (170)?” And, in the same Gibsonian spirit, rather than seeking the information in programmed motor routines in the nervous system, he suggested that a significant part of that information is contained in the light available to moving organisms via optic flow.

Paying attention to optic flow allows researchers to take into account the way that regularities in this expansion outward can be utilized in perceptual navigation of an environment. This is exactly what David Lee and Paul Reddish (1981) did when they developed a model of gannet diving where a single optic flow parameter tau (τ) is directly perceived and exploited in guiding a complex behavior. Gannets are sea birds that dive from heights of up to

30 meters, catching fish deep under water by plunging into the water at speeds of up to 54 miles per hour (Lee and Reddish, 1981, 293). In diving, they assume a wing position that allows

24 them to steer – maintaining the appropriate speed and direction of descent – followed by a split-second retraction of the wings into a streamlined configuration before hitting the ocean's surface. The resulting behavior is one that balances accuracy in controlling the maneuver with a minimized chance of breaking the birds' extended wings upon collision. Accomplishing this feat requires the gannet to somehow “keep track of its time-to-contact with the water,” and one obvious way to do this would be for the bird to track “its height, velocity and acceleration...[and] compute the time-to-contact (293).” Or, as Anthony Chemero suggests, one might think it uses some more extravagantly representational process: “using a stored representation of the expected size of the prey fish, compute distance from the surface of the water; then compute time to contact with the surface from this distance, using internally represented laws of motion (Chemero, 2009, 123).” Instead, Lee and Reddish suggested a more elegant solution: perhaps, they suggested, the gannets are able to extract time-to-contact information from the rate of optic flow expansion on their retina.

In approaching the Gannet's coordination problem ecologically, Lee and Reddish discovered a crucial optical parameter, τ (tau). τ can be understood by thinking about the way that texture elements spread out from the center of the optic flow field. Assuming that a gannet is diving at a constant velocity (from any height), τ is the ratio between the size of a particular texture element in the optic flow field to the rate at which that element expands as it flows away from the center. The authors hypothesized that recognizing the optical flow parameter τ would put it in direct contact with the information needed to accurately perceive its time-to- contact when diving at a constant velocity, and thus could provide enough guidance for appropriate timing when pulling in the wings. In order to test this hypothesis, Lee and Reddish

25 took video recordings of the gannets' diving for fish and plotted a curve relating time-to-contact with the duration of a dive for the 55 recorded dives. Remarkably, of the various equations proposed (representing various strategies for guiding the behavior), the curve predicted by assuming the birds adopted the τ-strategy was the best fit for their observations. And, because at a constant velocity τ does accurately specify time-to-contact, an alternative strategy that computes the time-to-contact via the bird's detection of its height and velocity could also be developed that would fit the data equally well. However, the τ-strategy is a much simpler explanation than those that posit a computational solution to determining time-to-contact.

Secondly, detecting its own height and velocity with sufficient accuracy to produce this behavior seems an unlikely perceptual capacity in the case of gannets. And, finally, other alternatives such as timing based on a constant velocity produce curves that predict the data points gathered comparatively poorly. This, along with later uses of τ to explain other types of coordination such as the aerial docking of hummingbirds (Lee, Reddish, and Rand 1991), support the τ-strategy hypothesis in the case of diving gannets (among other visually coordinated behaviors).

Lee and Reddish's gannet case is informative because the kind of perception at issue is, I hope, of a more obviously perceptual/cognitive flavor than that of Case 1. And, as was the case with CPGs, the kind of visual perception involved has certainly been treated as within the purview of cognitive psychology elsewhere. But, most importantly for my purposes, the τ- strategy model supports all three of the major claims I attribute to TCC.

First, both ecological optics in general and Lee and Reddish's work in particular are quite explicitly committed to the truth of C1. The reason that the perception of τ is compelling in

26 explanations of complex behavioral coordination is that any agent subject to optic flow has potential access to a source of information about time-to-contact with various features of the environment that is unmediated and easily exploited for guiding movement. That is, relative to a moving perceiver, the local environment is informationally rich in terms of the very sort of information one would hope to have access to in attempted to avoid collisions, land safely (if flying), hit a target, etc. While this, and related cases, are clearly not strong enough on their own to fully establish C1, such explanations provide an existence proof for the claim that, in at least some situations, there is a real sense in which the environment can be informationally rich

– providing enough information to govern successful coordination – as well as giving us exemplar models for characterizing the perception of this information.

Second, like the pseudo-quadruped, whether or not Lee and Reddish's research on optic flow supports C2 depends, in part, on whether or not one considers the behavior at issue sufficiently perceptual or cognitive. It is certainly an instance of an explanation that embraces

TCC's general emphasis on the way that perceptual information becomes available as a result of intimate interactions between the perceiver and the environment; the very notion of optic flow is one that presupposes an active agent embedded in a dynamic environment. As to whether or not the implication of τ-perception is sufficiently cognitive to support C2, I again will rely on the suggestion that earlier work on similar perceptual capacities have been treated as within the proper domain of cognitive science; computational work in visual perception such as Marr's, in fact, also included discussions of optic flow, the perception of depth, and the governance of landing behavior in real time.

Thirds, there is no doubt that Lee and Reddish's model is a case that supports C3. The

27 gannet case is a single instance of what has come to be known as “General Tau Theory,” in which Lee et al. claim optic flow as “allows the emergence of information in the Gibsonian sense, where a property of the optic flow specifies a property of the environment-agent system

(EAS)” whose mathematical characterization is couched explicitly in the terminology of coupled dynamical systems (Lee et al., 2009). General Tau Theory has been generalized to a wide variety of perceptual coordination scenarios, but a common theme is the treatment of agents and their surroundings as a coupled unit whose dynamics govern the successful perceptual negotiation of a coordination problem. For those who are still not satisfied that the above two cases are within the proper scope of the cognitive or perceptual, my final exemplar of TCC-style research is focused specifically on a more cognitive activity: problem solving.

Case 3: Entropy and Problem Solving

Some have argued that one can acknowledge the insights, explanations, and promise of the various nonrepresentational approaches to cognition, but that as researchers move up the ladder of cognitive sophistication there will be an increasing need to posit representations. As a matter of fact, the point where representational explanations find their footing has been suggested to delineate the boundary between mere biological responsiveness to the environment and cognition proper (Adams and Aizawa, 2008). Still, the “mark of the cognitive”

– the set of necessary and sufficient conditions that make phenomena like learning, memory, perception, thinking etc. cognitive processes – remains as elusive as ever. A more practical projection of the point where nonrepresentational models will be found inadequate is provided by Clark and Toribio's concept of “representation-hungry” problems (1994). There are many

28 features of particular perceptual and cognitive tasks that might suggest that they are representation-hungry, but there are two major markers to look out for: first, the task might involve coping with invisible, temporally/spatially distant, non-existent, or counterfactual objects or events (a predator's expectation that the prey it witnessed scampering into one end of a hollow log will emerge at the other end, or a child's fear of the monster her bed). Or, alternatively, the task might involve calling upon informational resources that are absent in the stimulus environment (recognizing that a photograph is of Leon Trotsky, or that your child looks suspiciously like the milkman). For the concept's proponents, identifying tasks that appear representation-hungry can act as a rule-of-thumb, indicating that embodied interaction with the environment will be unable to account for the processes responsible for a cognitive system's skillful behavior. And, if the environment seems insufficient to explain the ability, then the next place to find the missing contribution would likely be in the nervous system.

Stephen, Dixon, and Isenhower's 2009 study of puzzle solving provides an example of the application of dynamical and complex systems approaches to a paradigmatically representation-hungry cognitive phenomenon (Stephen, Dixon, and Isenhower, 2009).

Specifically, the authors try to explain the adoption of new strategies by subjects solving a puzzle using methods applicable to a wide variety of self-organizing dynamical systems. In my explication of Case 1 I focused mainly on interaction-dominant dynamics and self-organization, but I didn't venture much into the mathematics lurking in the theoretical background. Because the pseudo-quadruped is already familiar, I will take this opportunity to briefly introduce some of the key mathematical concepts that figure in Case 3 and form the theoretical backbone of dynamical explanations generally.

29 Recall from Case 1, the pseudo-quadruped's locomotion tended to settle into distinct gaits whose “stability” could be manipulated by controlling the speed of locomotion. This tendency is an instance of what are known as “attractors” in the language of dynamical systems theory (DST). Understanding attractors requires that we think of systems like the pseudo- quadruped as a dynamical system – a system that undergoes change over time along dimensions that can be assigned numerical values and whose changes are dependent on previous state of the system. The particular set of values for a system at any time is the system's

“phase,” and the set of all possible assignments of these values define the system's “phase space” – a geometrical representation in which every coordinate within the space corresponds to a particular phase that system could be in, with one dimension in the phase space corresponding to each of the system's variables. Understanding the dynamics of a system involves finding a differential equation (or set of them) whose solution specifies a coordinate in the phase space for each value representing time from an initial state (time=0) onward, plotting for each initial position in the space a trajectory through the phase space that predicts the observed behavior of the system being modeled. Because the trajectory describes the system's evolution over time, the line formed by the points in a trajectory allows one to visualize change in system variables as movement through space. Attractors are regions of the phase space that

“pull” trajectories that pass sufficiently nearby in the phase space toward them; that is, trajectories converge toward, and tend to return to, the attractor. Returning to Case 1, Harrison and Richardson captured the relative phase of the pseudo-quadruped's front and hind legs by comparing the times at which front and back limbs were fully extended. Being a relatively simple set of coupled oscillators, this single variable could capture the entire phase of the

30 pseudo-quadruped system; that is, relative phase is the system's order parameter (the value/values that determine the layout of the attractors in the phase space). And, finally, there is the control parameter. The control parameter is a system value that, after reaching a certain critical threshold in the phase space, is pulled into the range of a different attractor wherein the system's behavior undergoes a qualitative shift in its overall dynamics. This, you will recall, was the kind of change observed in the pseudo-quadruped; synchronized oscillatory locomotion patterns (such as PACE and TROT) were observed less and less often as the speed of the treadmill increased, suggesting that stable gaits could be maintained only while the value of the control parameter (treadmill speed) remained within the basin of attraction for that gait. In the language of DST, stability could be maintained only within a range of values and, once the speed reached a critical value, the system is pulled into a new attractor, resulting in a new organization of the system's behavior. This is known as a “phase transition.” With these concepts in hand, we can now look at how these mathematical tools were put to use in Case 3.

Stephen et al. (2009) proposes that “cognitive restructuring” might be accounted for by treating it as an instance of the kind of spontaneous self-organization that has been observed in energetic systems in other fields, such as fluid dynamics. Cognitive restructuring occurs when a cognitive system reorganizes its activity towards a new conception or understanding of a cognitive or perceptual task. To take a toy example, imagine that a subject is asked to determine the number of tridecagons in a series of pictures containing tridecagons (13-sided polygons) and tetradecagons (14-sides) at various angles and that he is unable to tell the difference at a quick glance. The subject might begin by counting the edges of each polygon before, after a few tries, he notices that all of the tetradecagons' edges run parallel to an edge on the other side

31 while the tridecagons' edges all run parallel to a vertex on their opposite side. Counting tridecagons is now a much simpler and less time-consuming activity. Acquiring this new conceptualization, the subject has undergone cognitive restructuring, gaining insight into the problem by altering the way he sees the relevant stimuli and adopting a new problem-solving strategy. But how can cognitive restructuring occur “without prior knowledge of [the] new structure's impending form...[i.e.] without the guiding influence of an external or internal intelligent agent (Stephen et al., 1811)”? Their approach to this question itself involves a kind of cognitive reconstruction, but the basic principles should be familiar by now – reconceiving the phenomenon as a phase transition in a complex self-organizing dynamical system arising out of coupling between brain, body, and environment and governed by the laws of thermodynamics.

This conception leads to some immediate predictions. Because cognitive structures are organized, the system should be far from equilibrium – the even distribution of matter and energy whose homogeneity precludes structure. Maintaining organization of this kind requires energy. Some of the energy introduced to the system through interaction with its environment will be used to maintain system organization, while the unused energy will perturb it. This perturbation increases the system's entropy – the information-theoretic measure of disorder.

Self-organization occurs when the dynamics of the system's parts settle into an organized pattern that mitigates the entropy-increasing effects of energy flow through the system and, thus, allows the system to maintain a stable configuration (Stephen and Dixon, 81-84). If this stability is overcome by increases in entropy the system's organization will be disrupted, its components' behavior will be freed from the constraints imposed by their former organization and resume the kind of disorderly interactions from which (sometimes) a new ordered state can

32 emerge. In DST's terms, configurations that maintain stable organization are the attractors of a self-organizing dynamical system's phase space. Thus, if cognitive restructuring is the result of this kind of system, subjects' adoption of a new strategy should be predicted by increases of entropy toward some critical threshold followed by a rapid decrease in entropy as a new cognitive structure is spontaneously assembled. As you will see, this is exactly what they observed.

Stephen et al. asked subjects to perform a gear-system puzzle that had previously been used in other experiments. Presented with an image of interlocking gears, the participants were observed trying to determine if rotating a first gear (the “driving gear”) in a certain direction would cause a later gear (the “target gear”) to rotate left, right, or jam (when more than one adjoining gears' teeth pushed it in opposite directions at the same time). The subjects were encouraged to speak aloud as they solved the problem, and were video-taped, while their finger motions were recorded by a motion tracker. Most began solving the puzzle by using their finger

(though a small percent used their head, or eyes) to follow the path of interactions from the driving gear to the target – simulating with their body the physics of the system. This approach was called “force tracing.” Most people who employed this strategy would eventually discover that, because every gear turns its neighbor in the opposite direction, they could just alternate clockwise to counterclockwise from the driving gear to the target. This second approach was called “alternation,” and was described by the author's as involving a restructuring of the conception of the problem from attending to the physics of the gear-system to a higher-order property of the system: alternation (Stephen et al, 1812-1814). Because the participants never saw the gears actually moving, the authors hypothesize that the alternating movement of the

33 subjects' fingers (eyes, or head) in force-tracing supplied a source in their environment for picking up on information about this higher-order property of gear-systems. That is, the motions of the force-tracing provide the basis for the reorganization of the system into its new structure via the dynamic interaction between perception, action, and environment.

Because participants were given multiple trials on the gear-system paradigm, one could see for any individual how many trials were completed before the alternation strategy was adopted. This data allowed the authors to determine the “risk set” for each trial – the set of subjects who had not yet switched to alternation – and the “hazard” - the probability for each trial of cognitive restructuring occurring (the proportion of the subjects who switch strategies at that trial compared to those who remain in the risk set). These metrics made it possible to plot the increase in overall hazard throughout the 35 trials and to analyze this in terms of a few predictors for the “event” (cognitive restructuring). This analysis used the motion tracking data taken from the subjects as they performed the task that was then correlated with the predictors. The first, mean line length, was determined by the length of time the subjects exhibited force tracing behavior. Longer mean line lengths were described as indicating subjects' remaining in one of the attractors for longer periods. This would indicate a maintenance of the cognitive system's organization – a prerequisite for later undergoing a phase transition. The other predictors were the peak entropy – the highest levels of entropy calculated from motion tracking information – on previous trials. This metric serves as an index for discovering the sudden increase of entropy that their hypothesis predicts will immediately precede cognitive restructuring. And, finally, they incorporated prior entropy – the entropy of the previous trial – allowing them to detect the rapid decrease in entropy that marks a system's settling into an

34 attractor of stable organization. What Stephen and his colleagues found was that their dynamical model was able to predict a subject's cognitive reconstruction based entirely on a pattern that applies generally to phase transitions in self-organizing dynamical systems: for systems whose phase space trajectory remains stable around an attractor long enough to indicate organization, rapidly increasing entropy predicts critical instability and, subsequently, a phase transition wherein its trajectory makes a sudden course toward stability within another attractor.

A second set of trials further supported their model. Because their account treats cognitive systems as an instance of a larger pattern in complex dynamical systems, the phase transition gets the same kind of explanation as phase transitions in other thermodynamic systems, such as convection rolls in heated fluids (Lorenz, 1963). This would mean that it is the entropy-diffusing dynamics of the organized states around the attractors that explains periods of stability and the destabilizing perturbations of increased entropy that induce the critical instability that precedes phase transition. Stephens et al. tested this implication by manipulating the system's entropy directly by running a second series of trials where the window displaying the gear-system would unpredictably shift its position on the computer screen. This manipulation required that the subjects constantly reorient their gaze and finger movements, introducing additional energy to the cognitive system, resulting in increased entropy. If anything, one might expect that this manipulation of the task environment would delay the discovery of the alternation approach. But, as their model predicted, the addition of extra entropy to the cognitive system increased the likelihood of discovering the alternation strategy.

Stephen et al.'s explanation of cognitive restructuring has the virtue of illuminating a

35 number of aspects common to TCC-style accounts. First, it provides a framework for discussing the mathematical concepts that populate (and motivate) a good deal of TCC-style research, including my two earlier cases; although I left much of it in the background to attend to other features of TCC. This mathematical apparatus is important to understanding TCC because it puts additional theoretical flesh on the bones of my earlier discussions of oscillation, self- organization, and coupled dynamical systems. And, as you can probably already tell, Case 3 supports the three core claims I've attributed to TCC. Consistent with C1, Stephen et al.'s model explains skillful discovery of a solution to a cognitive task purely by way of reference to information available in the environment. For example, alternation in gear-systems like those depicted in the task is a real higher-order property of their physical organization that is relevant to understanding and predicting their behavior. As Stephen and Dixon describe it, “[f]orce tracing...involves the bodily action of moving the forefinger along a trajectory available in the visual display of the gear system (Stephen and Dixon, 86; emphasis mine).” The alternating motion that results from this bodily movement puts the cognitive system in contact with the higher-order property of the gear system and, they argue, “serve as the dynamical interactions through which a new structure self-organizes (86).” On this view, the interplay between information in the environment is there to be discovered, and it is the bodily engagement with the environment that puts the cognitive system in (direct) touch with the relevant information.

Case 3 also supports C2: it involves a straightforward explanation of a phenomenon that might look, at first blush, to be an archetype of the kind of representation-hungry tasks that warrant being called “cognitive.” As it happens, much of the discussion of this phenomenon does just this, dubbing the process “representational change” and casting it in terms of one

36 representation of the task's being replaced by another. In fact, Stephen et al. themselves describe the process this way on occasion (including the title of their 2009 article), although

Stephen and Dixon take pains to distance themselves from this language:

The work reviewed above strikes a contrast with more conventional approaches...In the conventional interpretations of cognition as a symbolic or mental phenomenon, computations play a major role in structuring impoverished sensory inputs and structuring actions around preexisting representations (Fodor, 2000). We propose that...dynamic interactions between an organism and its environment are sufficient to generate new cognitive structure. Indeed, we recognize that the term “representation” may no longer apply to our description of cognitive structure. (95; emphasis mine)

This quote is as near an explicit endorsement of C2 as one is likely to see. But we don't need to take their word for it; looking at their explanation of cognitive restructuring is enough. The phenomenon is without a doubt a cognitive phenomenon (what else would it be?). Their model predicts and explains it without recourse to internal information processing or representation of the task domain: it subsumes it under covering-law style explanations as an instance of a more general regularity.

The support that models like this lend to C3 is similar to the types of arguments that we have seen before. On the view presented, cognitive systems are open thermodynamic systems participating in constant exchanges of energy with their environment. Stephen and Dixon point out that this process is highly interaction dominant – a feature that, as I argued earlier, makes it difficult to assign a determinate function (such as being a representational vehicle for x) because the contribution of the micro-level components of the system to the macro-level behavior is perpetually in flux. This is crucial, because this aspect of the cognitive system's dynamics is an essential component of their account: “interaction-dominance leaves it open to reorganization and sensitive to what new structure the environment might afford.

37 Environmental structure is the groundwork upon which the reorganization occurs (95).”

Explanations that cite this kind of intimate coupling between the agent and the immediate environment as the basis for the organization for behavior carry real weight in motivating C3, because it seems to leave little foothold for the kinds of mechanisms (presumably) necessary for information processing accounts.

The three cases I have examined here represent a small sampling of a growing body of embodied, ecological, and dynamical research on cognition, perception, and coordination – far too small to portray the range of theoretical commitments guiding this kind of work. Despite this diversity, I have tried to show that skepticism and eliminativism about information processing accounts are motivated by a few common claims and that my account of TCC approaches captures this overlap.

38 Chapter 3: The Revised Job-Description Challenge and the Heterogeneity of Representation in Cognitive Neuroscience

The project of providing defense of representation that can stand up to the challenges raised by TCC is immediately met with a few important complications. It also will involve saying something about the purposes representations are meant to serve in explanations of cognition.

In this chapter I will begin by looking at these problems, as they directly inform the approach I will take in the next chapter for clarifying the explanatory functions and motivations of representation.

Problem 1: Running Away

I hope that the last chapter's review of TCC-style accounts has provided a sense of the dramatic alternatives to representational cognitive science on offer from new conceptions and models of cognition – especially in areas that have been, or might be presumed to be, the very kind of representation-hungry problems most suited to representational explanation. Whether or not you think that TCC has the resources to mount a full replacement of information processing and representation, I have tried to convince you that there are clear instances where TCC-style explanations are addressing the same kinds of cognitive and perceptual phenomena as have been the subject of representationalist treatments. Proponents of information processing approaches to cognitive science can no longer make the argument that theirs is “the only game in town,” as Jerry Fodor famously alleged of his own computational hypothesis (Fodor, 1975), and they shouldn't get away with doing so; the fact that the theory

Fodor was defending is barely one of the games in town, as I see it, should also tell us

39 something. Nor can advocates of representational approaches retreat – ignoring the challenges implicit in TCC-style cognitive science – either by refusing to acknowledge them or by redefining

“representation” so that it covers even exemplars of TCC-style explanations. In the first instance, ignoring the implications of TCC would be not only dishonest but, worse, would hamper the process of mutual constraint that often characterizes the relationship between psychological and neuroscientific models, experiments, and explanations. This kind of neglect of

TCC's import most often takes the form of denying the explanatory relevance of TCC-style research to cognition – relegating the scope of dynamical models, for example, to discovering patterns in motor coordination. This kind of response suggests that the representationalist can hold out hope that non-representational dynamical approaches will provide some insight into low-level motor coordination, but that cognition, perception, and action will remain forever out of reach without representations. This hope – that the division between the theoretical and mathematical apparatus of TCC and representational approaches will coincide with a division of explanatory scope – is increasingly hard to swallow. Results from cases like 1-3 in the last chapter have guiding implications for cognitive neuroscience and neurobiology. If, for example, central pattern generators (CPGs) exist in the nervous system, then knowing that self-organized stable locomotion can arise out of physical coupling alone presents a very different picture of the kind of work you should expect CPGs to be doing. That is, while the pseudo-quadruped case should not give us any pause in recognizing the impressive and growing evidence of specialized neural networks in the spinal cord that produce rhythmic motor patterns (see Guertin [2013] for a review), the extent to which the dynamics of bodily and environmental interaction influence or are relied upon by these neural mechanisms is the subject of ongoing empirical

40 investigation. For example, Taga et al. (1991) presents a neural model of real time walking that incorporates CPGs, but rejects the picture of the body as “responding to the commands of the nervous system in a master-slave manner”; instead, they offer a model where motor patterns

“emerge spontaneously from the cooperation of the system's components,” positing a mechanism for locomotion “generated from the interactions of...neural, musculo-skeletal, and sensory systems under variable constraints of the environment (147).” There is no escaping the fact that TCC-inspired models and results are of empirical relevance to domains that overlap with more traditional explanations in cognitive science. Again, this by no means suggests that representationalism is doomed, but it does mean that TCC and representational approaches cannot be treated merely as “non-overlapping magisteria (Gould, 1999).”

The second retreat – redefinition of “representation” to cover paradigmatic TCC phenomena – is no better. William Bechtel, for example, claims to find representations in

Timothy van Gelder's Watt Governor example (Bechtel, 1998). By defining representation roughly as any “reliable covariance which carries information for later use by a consumer” he secures representational status to the system's arm angle at the expense of robbing the concept of representation of explanatory power; tagging aspects of the dynamical system as representations does not tell us or predict anything new. Similarly, Markman and Dietrich developed the concept of “mediating states” – “internal states of a system that carry information which is used by the system in the furtherance of its goals (Markman and Dietrich,

140).” Mediating states are meant to serve as a less problematic interpretation of representation – representations the whole family can agree on. The problem is that, on a vague enough construal of “goals,” everybody agrees that those exist and have explanatory use;

41 representation then becomes ubiquitous, occurring in nearly every process in evolved biological systems as well as many artifacts. This implication makes it unclear why cognitive science would have any special interest in mediating states, and would render TCC-style arguments against the utility of representations incoherent, as TCC's proposed replacements for representation would themselves be representational on this definition. That cannot be right; if representational posits are anything at all, they must be causal entities/processes of a mechanism that explain cognitive phenomena in virtue of being representations. Not only are much of the explanatory apparatus of TCC difficult to square with mechanisms of this sort, but even if this were not the case, it would save representation at the cost of explanatory pay-off – having to both justify adding representational concepts to already existing explanations as well as taking on the Sisyphean task of reconciling this widening of the concept of representation with the representations populating more traditional ontologies.

Problem 2: Symbol-crunching and Propositional Attitude Psychology

In the beginning of the previous chapter, I made a point of noting that I have been using the term “representation” in a very broad sense. My primary reason for this is to distance myself from some of what I see as the more unsavory associations this term has the tendency to evoke. Oftentimes (especially in anti-representationalist company) the terms

“representation” and “information processing” are associated with “computationalism” - specifically, the idea that cognition is best thought of as formal symbol-manipulation by a more- or-less traditional computational system. This brand of computationalism's affinity for traditional computing endows it with a rare virtue amongst representational accounts: the

42 kinds of representations it tasks with explaining cognitive phenomena commit one to a fairly clear empirical claim about the brain's functional organization. Specifically, it suggests that the computational processes underlying cognition are serial/algorithmic formal manipulation over symbols – i.e. operations transform strings of symbols according to rules that are sensitive to their syntactic structure stepwise, with only one such operation occurring at any time. These rule-governed processes typically operate on combinatorial, context-invariant symbols – symbols that can be conjoined with other symbols, deleted, moved about in a string etc., but whose representational content/interpretation remains unaltered across these transformations. David Marr's computational theory of vision, mentioned in the last chapter, is a classic example of a model with this kind of computational architecture. This conception of cognitive systems has its roots in the beginnings of Good Old-Fashioned Artificial Intelligence

(GOFAI) and the beginnings of cognitive science (such as Newell and Simon [1963]) and has come to be known as the “physical symbol system hypothesis” (Newell, 1980). Symbol- crunching approaches to modeling cognition like these garner much of their appeal from the fact that systems with this computational architecture (a PC, for example) are capable of approximating a universal Turing Machine and, thus, can be programmed to perform a wide variety of tasks – mirroring the flexibility and generality of human cognition.

However, as if this were not enough theoretical baggage for the term “representation” to carry with it, there is a further association to address here: the Computational Theory of

Mind (CTM). The CTM denotes family of approaches that attempt to put a symbolic computational architecture to work in legitimating propositional attitude psychology (or “folk psychology”) by outfitting it with a plausible causal basis (for a classical defense, see Fodor and

43 Pylyshyn [1988]). Simply put, proponents of CTM hold that the common-sense mental state ascriptions of propositional attitude psychology successfully predict and explain behavior because they are a more-or-less accurate account of causal/computational economy of human cognition. For instance, one might predict Jerry's taking the bottle labeled “opium” from his cupboard by ascribing to him various propositional attitudes – e.g. he believes that he has a headache, desires that the headache be relieved, believes that opium relieves headaches, and lacks any countervailing beliefs that might prevent him from grabbing the bottle. According to

CTM, when propositional attitude-derived predictions like this succeed it is because they accurately portray the representational states of a computational system (Jerry's brain, or a part of it) and the relevant causal relations between them. This works by supposing that the structure of propositional attitude expressions – such as “believes that opium relieves headaches” – is suitably analogous to the computational causes and effects of symbolic structures in a cognitive system. So, to love that P and to fear that P would correspond to having a string of symbols with the content “P,” although individuated by their having different causes and effects. That is, if a symbolic string expresses “P,” and P is the proposition “the house is overrun with centipedes,” then the symbolic expression of “P” ought to be caused by seeing things such as walls crawling with centipedes, and the folk psychological verbs “to love” and “to fear” denote different causal effects of the symbolic string on behavior and the encoding of other symbolic structures – loving that P might cause one to dance for joy, or to undergo a computational process whose output is a string of symbols expressing “It is time to buy centipede food.” While this is a rather brief review of CTM, I hope I have gotten the basic picture across; CTM is an attempt to legitimate the folk psychological taxonomy of

44 propositional attitude psychology by recruiting the theoretical framework of symbol systems to identify folk psychological kinds and mental processes with more scientifically respectable posits like data structures and formal operations over them. Everyday attributions of mental states representing sentence-structured propositions enable us to predict and explain behavior because cognitive systems actually traffic in sentence-like, linguiform entities that are manipulated according to rules that respect many of the deductive norms of logical inference

(that are themselves symbolic data structures).

There is no essential connection between symbolic information-processing approaches and propositional attitude psychology; Marr's model of vision (1982) is symbolic and rule- governed, but is not populated by propositional attitude psychology's beliefs, desires, etc., and

Stephen Stich's Syntactic Theory of Mind (1983) openly divorces symbolic accounts of cognition from propositional attitude psychology. Still, the reasons I have for distancing myself from both projects highlight important aspects of the approach I will take in this chapter. To begin with, both CTM and symbol manipulation-based models in general are committed to a strong empirical assumption about the nervous system's functional/computational organization. This includes everything from the format of signaling in cells in sensory organs to communication between cortical regions to the encoding and processing structure of both afferent and efferent interneurons. The problem is that this empirical assumption seems increasingly biologically implausible; almost everything known about the anatomy of nervous systems suggests a parallel architecture – cells synapse upon myriad others (~7,000 in humans, on average) forming networks ill-suited to supporting rule-governed, formal symbol manipulation (indeed, formal/algorithmic solutions to cognitive capacities that take a fraction of a second would take

45 ages, given the known conductance velocities of biological neurons). A second concern is a matter of scope. Insofar as symbol-crunching approaches are motivated by their ability to mechanize commonsense, propositional attitude-type pictures of the causal economy underlying behavior, they have difficulties generalizing to most perceptual and cognitive phenomena of interest. Even ignoring the tension between CTM's aspirations to an empirical hypothesis of the contingent, evolved, operations of cognitive systems and propositional attitude psychology's dependence upon a normative assumption of rationality for its psychological attributions to predict anything at all, CTM still seems unequipped to deal with vast swaths of cognitive terrain: a short list would include children, the mentally ill, irrational people, cognitive development, learning/memory consolidation, visual/auditory/olfactory/tactile object recognition, motor coordination, procedural learning, proprioception, and nearly all perception and cognition of non-human organisms.

My final reason for distancing myself from symbolic and CTM approaches to representation is due to the fact that both symbol systems and CTM approaches owe a great deal of influence to Marr's (1982) levels of analysis approach to computational modeling. Marr prescribes viewing information processing tasks at the “computational theory” level (describing the problem to be solved), the “representation” level (describing the inputs and outputs as representations, positing a formal algorithm that maps the former to the latter), and the

“hardware” level (a possible neural implementation of the posited algorithm). In accordance with this picture, symbol systems and CTM models tend to focus on “higher” cognitive problems, writing computer programs that arrive at a solution in a manner consistent with human performance (and, they hope, can be made to elicit some of phenomena observed in

46 human subjects, such as priming etc.). Take, for example, a contemporary symbol system: John

R. Anderson's ACT-R architecture (for review, see Anderson, 2004). I choose ACT-R because it is a production system (a rule-governed, AI program whose “knowledge” consists of a database of symbolic instructions), and production systems are exemplary symbol systems with a long history in cognitive modeling. Altmann and Trafton (2002), for example, used ACT-R to model

“goal-directed cognition” – solving the Tower of Hanoi puzzle. This puzzle consists of a number of disks of increasing size that must be moved one at a time from some position on three pegs to a “goal” position without ever placing a larger disk on a smaller one. In the simulation, the current and goal positions are represented by the program as a numerical structure specifying the identity and location of each disk. Put very simply, the program compares the current state of the puzzle with its goal, and follows an algorithm of stepwise routines (and subroutines), storing sub-goals in memory for later deployment. Examples like this highlight the fact that symbolic models appear best suited for (and are most often put to use in) explaining the kinds of sophisticated cognitive processes that exemplify the unique, and impressive achievements of human cognition – language use and comprehension, rule following/game playing, logical/deductive inference, etc. That is to say, symbolic accounts, unsurprisingly, appear the most compelling when they are applied to the formal symbol manipulation tasks that comprise a very small subset of the cognitive activity of a single species. There are (at least) two major problems that stem from taking these empirical assumptions about cognitive architecture as a framework for explanations of cognition. First, aside from its limitations on scope mentioned earlier, the comprehension of rules and symbols makes up a small corner of human cognitive activity and, therefore, explanatory frameworks that build ready-made solutions to such

47 sophisticated capacities into the representational system itself should be viewed with suspicion; being the small exception, rather than the rule, it might be better to explain how a biological system that seems so unsuited to rule-governed symbol manipulation might have recruited non-symbolic processes to support (or approximate) symbol manipulation. This is in keeping with Andy Clark's slogan about parallel processing architectures (and humans!): “good at Frisbee, bad at logic (Clark, 60).”

The second, and principal, problem is that the cognitive processes most amenable to symbolic accounts are those that are the most divorced from anything currently available to experimental intervention or observation. This feature, I will argue, makes symbolic accounts an inappropriate place to found a defense of representation. Models like Altmann and Trafton's use of ACT-R paint a clear picture of a potential strategy that might be employed in solving the

Tower of Hanoi puzzle. But, this picture is couched in terms of theoretical entities at such a remove from anything accessible to experimental intervention, and so divorced from biologically plausible mechanisms, that there is much room for doubt as to whether the particular computational algorithm employed by ACT-R is interestingly isomorphic to any of the relevant cognitive economy of human cognition. There are just too many ways of being semantically well-behaved within a problem-solving domain to give much currency to any particular model that posits these kinds of theoretical entities. After all, there are available algorithms (in principle, infinitely many) that will yield quite impressive behavior if one is evaluating for high-level chess playing behavior, but there is little to suggest, and good reasons to doubt, that the processes responsible for this behavior offer much insight into human chess playing. Partly, this is because the counterpart of Altmann and Trafton's model – the chess

48 playing program, in this case – cites entities and processes that map rather neatly onto well- understood mechanisms in computer circuitry; programmers exploit this (via compilers) to encode algorithms that are appropriately linked to causal capacities of computer hardware.

These “models” suggest processes – like assigning entire board positions numerical evaluations subject to brute-force comparison – that are unlikely to be employed by human players. There is no analogous theoretical connection between the posits of symbolic cognitive science of higher cognition and the biological brain as currently understood and, given the staggering array of available computational solutions, any particular model can expect to be greeted as mere speculation. Unmoored from Marr's “hardware” level as they are, how possibly stories like ACT-R lack the resources to justify much confidence in its representational ontology as a framework for understanding human cognition. Of course, that is not to say that the only posits worth our respect must be intimately tied to well-characterized neural mechanisms; the levels of organization in human brains currently subject to inspection – molecular- and ligand-level interventions, single-cell recordings of small numbers of neurons and the comparatively massive scale tracked by fMRI and lesion studies – straddle a chasm of levels of organization of likely cognitive interest. My pessimism about symbolic accounts aside, this disconnect between what is computationally possible and neurologically plausible gives me reason enough to separate the project of defending the explanatory value of representational accounts of cognition from the symbolic approaches that have dominated many research programs in the history of cognitive science. Specifically, symbolic approaches, coming prepackaged with resources for modeling the kinds of rational inference typical of (some) adult humans, provide a poor basis for an account of representation. Given that the striking similarities that appear to

49 obtain between the mechanisms of all nervous systems – molecular, cellular, and in larger anatomical organization – it would be surprising that representational solutions to cognitive, perceptual, or coordination problems occur only in human nervous systems, if they occur anywhere.

A New Take on the Job Description Challenge

Taking TCC seriously compels would-be defenders of treating aspects of nervous system function as representational to recognize that information processing (symbolic, connectionist, or otherwise) is among the possible solutions that evolved biological systems might have

“discovered” for coping with their environments. Or, as Cliff and Noble (1997) present it: “if evolution did produce a design that used internal representations, how would we recognize it?

(1170)” Consequently, any particular model proposed is under important empirical constraints; namely, to provide empirical motivation for treating aspects of the relevant system's causal economy as trafficking in representations. Providing an account of this kind of empirical motivation, as I see it, is what has been lacking in defenses of representation like those mentioned in the first section of this chapter. The sort of account I am imagining would have to provide evidence of some distinct explanatory purchase gained by viewing cognitive systems representationally. At a minimum, I am seeking accounts that satisfy the following 3 desiderata:

A) It must be able to cite models or explanations that make explicit use of “representation,” “information processing” or related terms. B) The models cited must explain something that is not already explicable in other terms. C) Allegedly representational processes must play functional roles in a system that are recognizably “representational” (i.e. their treatment as representational must elucidate aspects of the system's organization/causal economy such that it provides an empirically motivated way of distinguishing between systems whose evolution “discovered” representational solutions to cognitive/perceptual problems from those that did not.)

50 In a way, the method I will employ in looking at the cases in the next chapter is similar to

William Ramsey's “job description challenge” – requiring “some unique role or set of causal relations that warrants our saying some structure or state serves a representational function

(Ramsey, 27, 2007).” Like Ramsey, the method I will adopt focuses on conditions that warrant the use (by the relevant scientists, as A demands) of representational notions – pointing us in the right direction without requiring that I begin at the outset with a theory of representation, complete with necessary and sufficient conditions. Many cognitive scientists and philosophers take the opposite approach: beginning with a theory (conditions for being a representation), and then looking around for entities that meet these desiderata. This might help refine our metaphysical intuitions about what it is to be a representation, but tells us little about the explanatory purposes of scientific attributions of representational status which is, as I see it, what is needed to defend their use against arguments for their elimination. Desideratum B further narrows the field of potential examples but, in particular, bars attributing representational significance to phenomena where it does not pay any explanatory dividends

(as I accused Bechtel of doing with his representational analysis of the Watt governor).

Desideratum C asks that any defense of representation worth its salt must be able to identify supposedly representational processes that play explanatory roles consistent with being called

“representations” (even if they are scientific terms of art that revise our everyday use of such terms). Ramsey's discussion of the job description challenge provides an illuminating example, asking us to imagine a biological process characterized as a pump but that, we are told,

“functions as a pump...by absorbing some chemical compound, and nothing more (28).”

Because the process involved is so unlike what would ordinarily be described as “pumping,”

51 there is little insight into the process gained by characterizing this process as a pump rather than, say, a sponge. Because, in this example, the nature of the mechanism seems easy enough to explain without invoking either “pump” or “sponge” terminology, the issue may appear to be largely a matter of aesthetic preference. But, as we will soon see, the neuroscientific literature, from introductory textbooks to specialist journals, is full of references to alleged representations. Far from being merely terminological, such indiscriminate use of representational terms have real consequences for explanatory practice.

Desiderata A-C address the shortcomings I have identified in the recent debates over representation, and suggest a new approach. Recalling part 1 of this chapter, most of the defenses on offer consist entirely of philosophical arguments from intuitions, analogies to artifacts, and definitions (often at the cost of explanatory power). Desiderata A and B suggest that, instead, defenses rely on real scientific models and explanations whose representational posits can be shown to produce novel explanatory results. B and C ask of such an account that, not only must the models explain something otherwise not understood, but that the representational posits involved must do explanatory work as a result of participating in processes that deserve being called “representational.” While I will not begin with a full-blown theory of representation, we can avail ourselves of some of the few areas of theoretical consensus regarding representations. For instance, it is widely agreed that representation requires reliable covariation between some state or feature of the environment (including the body/brain) and states or features of a mechanism but that mere correlation of this sort is not sufficient. In addition, it is commonly thought that reliable covariation must be exploited by a mechanism to allow for states or processes to stand-in for or carry information about the

52 entities or features in question. This additional requirement is often thought to involve consumer mechanisms recruiting producer mechanisms whose activity reliably correlates with the relevant features to govern adaptive behavior in a way that is consistent with interpreting the producer mechanism's activity as carrying information about the entity or feature being represented. And, finally, it is widely held that any putative instance of representation must, in virtue of being given a semantic interpretation, must be accompanied by an account of how might possibly misrepresent such-and-such. Keep these intuitions in mind as we proceed.

I criticized many existing computational models of citing recognizably representational processes that, due to their theoretical distance from observable causal mechanisms and their tendency to focus on higher cognition, look too speculative to motivate a strong commitment to their ontology and, in any event, are unsuited to clarifying the general explanatory purposes of representational explanations. Together with this criticism, my take on the job description challenge suggests looking to the cognitive neuroscience of relatively simple nervous systems, because it is here that we find allegedly representational models that are the most closely tied to (and empirically motivated by) well-characterized neural mechanisms and causal processes amenable to experimental observation and intervention. My pursuit of this strategy will begin with a brief discussion of some of the problems surrounding representation in cognitive neuroscience, as they provide insight into the motivations and explanatory purposes of representational concepts.

The Heterogeneity of “Representation” in Cognitive Neuroscience:

53 Cognitive neuroscience is replete with references to representations, information- processing, and related terms. Flipping through an introductory neuroscience textbook reveals descriptions of synaptic transmission as the mechanism by which “neurons communicate with each other...[making] it possible for circuits of neurons to gather sensory information, make plans, and initiate behaviors (Carlson, 53).” A review of neuroscientific journals yields countless examples of apparently representational characterizations of neural processing: the identification of orientation-detecting cells in the cat's striate cortex (Hubel and Wiesel, 1959), spatial auditory maps as the coding scheme in the barn owl's inferior colliculus (Konishi, 2003) and “representation of interaural time difference” in this structure (Wagner et al., 1987), representation of pressure, flutter, and vibration via “modality maps within primate somatosensory cortex (Friedman et al., 2004),” the role of topographic mapping in “accurate representation” of olfactory stimuli (Imai et al., 2010), the “code for stimulus direction” amongst assemblies of giant interneurons within the thoracic ganglia of the cockroach (Camhi and Levy, 1989), or the application of Shannon information theory to communication between neurons (Borst and Theunissen, 1999). Authors write of “population coding” amongst populations of neurons forming “response vectors...corresponding to all possible identity- preserving transformations” in object recognition (DiCarlo et al., 417) as well as the significance of “firing rate envelope and spike timing” in the “representation of a multisensory domain (Di

Lorenzo et al, 1).” It is a commonplace to read researchers in artificial neuron networks explain a trained network's behavior in terms of “distributed representations” stored in the synaptic weightings connecting individual units in the network. These are, obviously, only a handful of examples among many, but even this brief survey makes clear that representational concepts

54 operate within an impressively diverse range of explanatory domains in neuroscience. Matching this diversity of subject matter are the kinds of representational notions being employed.

Already a few distinct categories emerge (although there is overlap, no doubt and not all appear on the above list):

Receptor-type – orientation detecting cells, pressure, flutter, and vibration detection Spatial-type – topographic mapping, population coding, response vectors, intercellular signaling Temporal-type – timing of spike trains, phase and amplitude coding, representation of interaural time difference, summation

For purposes of illustration, I will look at a few examples of each type.

Receptor-type Representation:

Receptor-type characterizations tend to focus on the histological structure, location, and sensitivities of sensory cells. Take, for example, the range of mechanosensory cell types that are known to innervate the skin of many mammals. These cell types are frequently treated as responding to distinct varieties of stimuli (pressure, vibration, etc.). This is often inferred from features like: typical depth within the skin, the types of mechanical stimuli that tend to produce action potentials (APs), cell structure, how quickly APs cease after the initial stimulus

(rate of adaptation), and the maximum distance of skin displacement for driving APs (receptive field). Take Pacinian corpuscles: sensory cells that terminate deep in the skin (and elsewhere) in a capsule with onion-like layers filled with a fluid containing sodium ions. When sufficient pressure is applied to the skin, the deformation of the capsule opens channels that allow enough sodium ions to diffuse to prompt a series of APs. The cell's adaptation is relatively rapid, and APs discontinue until the pressure is released and the resulting motion of the fluid can trigger a second string of APs. These anatomical and electrophysiological features, along with

55 their deep position in the skin, seem to be responsible both for the Pacinian corpuscle's responding most readily to relatively high frequency vibration as well as the onset and offset of firm pressure (Bell et al., 1994). These properties, when combined with some assumptions and other empirical considerations about the kinds of stimuli that might commonly elicit these responses outside the laboratory, are also responsible for grounding claims about the type of sensory information these cells provide (via the thalamus) to the somatosensory cortex.

Treating the activity of the Pacinian corpuscle as a receptor-type representation carries with it theoretical implications that inform how the activity of Pacinian corpuscle afferents and their

“consumer mechanisms” are interpreted. While the empirical details that guide the functional interpretation of receptors differ from instance to instance, the types of considerations mentioned above are typical of receptor-type representational ascriptions.

Spatial-type Representation:

Spatial-type representations, as I am using the term, are comparatively more varied in kind. Spatial-type representations are spatial in the sense that they focus on spatially distributed patterns of activity and on the functional significance of the spatial organization observed in biological nervous systems. The variation in spatial-type representational attributions is seen both in the kinds of entities and processes deemed relevant and in the observations that motivate their use. Perhaps the most famous examples of spatial-type concepts are the presence of topographic maps in the sensory and motor cortices of many animals. Topographic mapping is said to occur when the spatial arrangement of cortical neurons preserves pertinent relationships in the organization of cells at the sensory or muscular

56 surface, with early maps “in processing sequences...likely to reflect the order of receptor arrays more closely,” while later maps are often thought to distort more straightforward isomorphisms toward specialized functional purposes (Kaas, 108). And, while most paradigmatic cases of topographic mapping preserve spatial arrangements of sensory surfaces

(like the retinotopic maps found in the lateral geniculate nucleus and V1, or the “homuncular” organization of the somatosensory cortex), there also appears to be a “'tonotopic' map ordered by frequency that projects to the auditory cortex” and a mapping between cells with similar preferred chemical stimuli in “the olfactory epithelium...onto specific modules of the olfactory bulb...regardless of their spatial location (Thivierge and Marcus, 251).” There is still disagreement regarding the functional contribution (or lack thereof) of topographic maps, but their prevalence has led many to the conclusion that they must confer some computational/processing advantage, such as integrating contextual information or

“representing various...aspects of stimuli that would depend not on physical proximity, but proximity in more abstract perceptual dimensions, such as motion, orientation, or color (253).”

Another spatial-type representational notion is Churchland and Sejnowski's (1992) connectionist-inspired view that “representation is vector coding, and processing is vector- vector transformation (172).” This view assigns spatial-type representational import to a few distinct attributes of artificial neuron networks (ANNs) and, by optimistic extension, to networks of biological neurons. First, vectors are an ordered series of n values (graded values between 0 and 1) where n is the number of the relevant set of units with values being an idealized level of activation in a neuron (corresponding to average firing rate, perhaps). These vectors are found at many points in the ANN: activation vectors occur at the input/sensory

57 layer, across sets of hidden units, and at the output/motor layer. Second, each connection between the ANN's units has either a positive value between 0 and 1 or a negative value between 0 and -1 called its “weighting.” Weightings are idealizations of the overall excitatory

(positive) or inhibitory (negative) influence of a neuron on another at the synapse, abstracted from the particulars of synaptic transmission (excitatory/inhibitory neurotransmitters, long- term potentiation/depression, the effects of myelination etc.). The values of these weightings determine the output vector produced by any input vector. ANN training typically involves encoding various kinds of inputs into a vector format appropriate to the input layer and compiling them into a training set. If the network is being trained for facial recognition, for example, then the input layer might act as a large artificial retina, with each pixel in the image input as a activation value (corresponding to pixel brightness) for a single input unit.

Additionally, vectors at the output layer must be given an interpretation; in this instance, one might interpret output unit 1 as “face” if its activation value is 1 and “not a face” if its value is 0, unit 2 as “male” if 0 and “female” if 1, while the activation values for units 3 through 7 are read as names of individuals in the training set (see Cottrell and Metcalfe, 1991). ANN training usually consists of repeated presentation of the training set of input vectors, each time comparing the output vector to the “correct” answer, and incrementally modifying connection weightings to the desired output. This process eventuates in a set of weightings that maps

100% of the training vectors to their appropriate output vector. However, because rote association between vectors in the training set does not imply any competence at the task, ANN training is considered successful only when the set of weightings formed in training enable the network to generalize to new stimuli, performing nearly as well when presented with novel

58 inputs. Continuing with Cottrell and Metcalfe's example, a well-trained network might be able to assign correct genders and names for familiar faces from the training set even when partially obscured or when seen from a new angle, or correctly assess gender and face-hood in pictures of individuals not contained in the training set.

Let's look at how Churchland and Sejnowski explain a successfully trained ANN's performance. For them, a trained network's set of synaptic weightings are best thought of as a partitioning of a activation vector space – an n-dimensional space where n is the number of hidden unit connections with each coordinate in that space corresponding to a unique hidden unit activation vector. This partitioning carves the state space into distinct regions formed around prototype representations – the statistical average of each type relevant to the task – that act as attractors, transforming any input vectors sufficiently close to a prototype (such as face/male/Dan) to the appropriate output. The partitioning around prototypes can be thought of as the “concepts” an ANN learns in its training, with the network's connection weightings serving to group distinct input vectors into groups that reflect their similarity along a number of task-relevant dimensions. The result of this partitioning is that task-relevant similarity in, say, the faces presented to the ANN will result in similar activations vectors in the hidden units – the more similar two faces are with respect to the features the network was trained for, the more similar the hidden unit activation that results. As the authors note, this explanation implicates both explicit and implicit representation. Explicit representation, for them, consists of patterns of activation across a network, transforming one vector to another in a semantically well- behaved way (as defined by the interpretation of the input/output vectors and the assigned task). This process, though, depends crucially on the information implicitly represented in the

59 complete set of synaptic weightings, as they determine the function that maps input vectors to outputs, partitioning the hidden unit vector space along the relevant dimensions of similarity.

Importantly, though, the entire set of connection weightings are involved in producing the appropriate behavior, because every transformation from input to output is mediated by every unit and connection in the network. This is the sense in which information stored in the network is implicit: because the ANN's “knowledge” consists in the distributed configuration of weightings, there is no way to interpret any particular connection as encoding particular information. Both the explicit and the implicit kinds of representation that Churchland and

Sejnowski endorse look like spatial-type representations: locating representation in spatially distributed patterns of activity, paying close attention to how various principles of (artificial) biological organization might confer a processing advantage.

If Churchland and Sejnowski's vector processing account seems too closely tied to artificial approximations of biological nervous systems, some broadly similar spatial-type representational approaches in biological neuroscience require a brief mention. One example is an attempt by DiCarlo and Cox (2007) to account for how brains are capable of visual object recognition in light of the “invariance problem.” The invariance problem for object recognition arises from the fact that “that any individual object can produce an infinite set of different images on the retina, due to variation in object position, scale, pose and illumination, and the presence of visual clutter (333),” but can still be recognized as the same object. The authors take their inspiration from known aspects of the neural architecture and connectivity of the rhesus monkey's inferior temporal cortex (IT), as well as on processing speed limitations

(objects can sometimes be recognized in < 300ms). Their proposed framework should sound

60 very familiar: they suggest that ventral visual stream solves the problem of object invariance by way of “processes that progressively transform that retinal representation into a new form of representation” where these representations can be “conceptualized as a high-dimensional extension of a simple three-dimensional Cartesian space in which each axis of the space is the response of one retinal ganglion cell (334).” Within this space, they argue, specific objects are represented as “a continuous, low-dimensional, curved surface inside the retinal image space called an object ‘manifold’ (334).” While I cannot go further into the differences between the vector transformation and the object manifold accounts of (some) neural representation here, they both exemplify common aspects of spatial-type representational accounts.

A final example of spatial-type representations can be found in what Stephen Grossberg

(2007) calls “laminar computing” in a review of its recent development. Laminar computing approaches attempt to generate models that incorporate the “fact that the cerebral cortex is organized into layered circuits (usually six main layers) which undergo characteristic bottom-up, top-down, and horizontal interactions (82).” This emphasis on common forms of cortical organization informs the development of accounts of processes such as visual object recognition that integrate known properties of neural architecture with considerations about the role of attention, competitive processing, context sensitivity, hierarchical feedback, learning, and development in mechanistic models that conform to established design principles of neural circuitry. Grossberg argues that laminar computing's characteristic commitment to constructing biologically constrained models of cognitive information processing “embodies a novel way to compute which exhibits three major new computational properties...[that] allow the fast, but stable, autonomous self-organization that is characteristic of cortical development

61 and life-long learning (84).” These computational properties include a hybridization of feedforward and feedback computing, citing how feedforward and recurrent connectivity and competition between coalitions of neurons at different cortical layers could support both fast feedforward recognition of unambiguous stimuli and, with ambiguous stimuli, recruit feedback loops to resolve uncertainty by enhancing contrast, artificially amplifying the most active

(winning) coalitions and suppressing weaker (losing) ones, as well as allowing contextual information to support or suppress various of the competing groups. These same intracortical feedback loops can “synchronously store” the winning activation patterns “without losing...sensitivity to amplitude differences in the input pattern,” a property he claims combines the “stability of digital computing and the sensitivity of analog computing (85, emphasis in original).” Many of the concepts employed in laminar computing accounts (feedforward processing, recurrent feedback, context sensitivity etc.) would be just as home in connectionist accounts. However, its representational commitments, and their empirical motivation, are sufficiently distinct to warrant their inclusion as a further spatial-type representational account in neuroscience.

Temporal-type Representation:

Luckily, for brevity's sake, the final type in our tentative taxonomy of representational concepts in cognitive neuroscience is relatively uniform. Temporal-type representational concepts implicate temporal features of neural activity in the nervous system's information processing, transmission, and storage. Often referred to as “encoding,” the archetypal temporal-type representational concept attributes representational importance to patterns in

62 the firing of APs (spike trains) of individual neurons. There are two main theories of neural coding: rate coding and temporal coding. Rate coding views changes in the average firing rate as the relevant information-carrying property. Usually this means correlating the spiking frequency of specific neurons with salient variables in a stimulus. Examples include correlations such as “the strength at which an innervated muscle is flexed” and “the ‘firing rate,’ the average number of APs per unit time (Gerstner et al., 12740),” or attempts to correlate firing rates of somatosensory neurons with the frequency of “mechanical vibrations applied to the fingertips (Salinas et al, 5514).” Many correlations of this sort, both linear and non-linear, are on offer and a number of more sophisticated rate coding approaches have been developed

(notably, the addition of information theory). Temporal coding, on the other hand, looks at factors like variation in spiking frequency and the role of spike timing in information transmission and representation. A nice example of this approach to coding is the correlation between specific odorants and “odor-specific temporal response patterns in [projection neurons] in the antennal lobe of the locust” with odor-identity-dependent patterns of oscillatory synchronization (Laurent et al., 3842). Despite the variation within both of these approaches, temporal-type representational models are all generally committed to finding ways that properties of spike trains might be made to correlate systematically with features of the environment. In many ways, T-style representational accounts are motivated similarly to those of receptor-type representation; the correlation between properties of the environment and properties of certain mechanisms are thought to allow the information processing or information-bearing role of those mechanisms. Still, despite this similarity, discussions of

63 temporal-type representations are common enough, and theoretically distinct enough, to be treated as an additional kind.

I am certain that there are representational concepts in cognitive neuroscience that escape clear inclusion within any of the three categories of this taxonomy. The point is, though, just to point out how many different kinds of representation, and theoretical assumptions, answer to terms like “represent,” “representation,” “information processing,” and “code.”

While not a problem in itself, this conceptual heterogeneity in contemporary neuroscience betrays a deeper truth: there is just no consensus in neuroscience about the meaning of

“representation” or about its explanatory utility. Despite this, I will argue, there is reason to look for a better understanding of representational explanations, and neuroscience is the appropriate place to look.

Why Neuroscience? and Why Representation Matters:

Even though there is no clear understanding about what they mean by

“representation”, cognitive neuroscientists often tacitly assume that their job is ultimately to identify representational processes and their functional contributions to behavior. There is nothing wrong with background assumptions of this kind – science would be hard-pressed to get anywhere without them. But there are numerous examples from neuroscience where representational significance is attributed to biological processes where it seems as though no representational concepts are needed. In some cases, it seems as though neuroscientists claim that a certain biological process “represents X” when all they really mean is something like

“responds to X,” “caused by X,” or “has activity that correlates with X.” Other times, phrases

64 like “sensors which are close to each other on the body tend to be represented in close areas of the cortex,” or that claims like “a common feature of [sensory] feature maps is that the representation scale is non-uniform” are supported by facts like “the cortical area given over to touch sensors in the lips and fingers is proportionately much greater than that given over to the back” suggest that “represented” here is a claim about the relative cortical real estate occupied by specific groups of sensory cells (Plumbly, 305). Ramsey (2007) claims that what he calls the

“receptor notion” of representation is common to various trends in cognitive neuroscience. The discovery of preferential firing in single cells for specific types of stimuli such as Hubel and

Wiesel's “edge detectors,” information theoretic approaches, and the formation of prototype representations in connectionist models, he claims, all “begin with the assumption that neurons function as representations” and look for a reliable causal covariation between neural activity and some regularities in their stimuli (120-121). Once this causal covariation is established, he argues, this plausibly necessary condition is commonly assumed to be taken as sufficient for treating the neural activity as carrying information about the stimulus. Treating such covariation as a sufficient condition, he argues, renders the receptor notion too promiscuous.

The “iron traces employed in the propulsion mechanism of certain anaerobic bacteria...are reliably pulled in the direction of magnetic North...toward oxygen-free water” and “immune system [responses] (such as the production of antibodies)” both involve mechanisms that exploit law-like causal regularities to adaptive purposes (123-125). But, he argues, examples like these do not seem intuitively representational, but the receptor notion seems unable to disqualify their inclusion in our representational taxonomy. More to the point, mere causal covariation would include far too many examples that failed desiderata A-C above. Immune

65 system responses, for example, do not appear to provoke representational treatments from immunologists, are already explicable in other terms, and they function in ways that are not recognizably representational.

It is important to understand why this exercise is more than an attempt to resolve an ongoing dispute about the use of words. After all, because of the influence of computational metaphors and models in psychology and artificial intelligence, it might be just a sociological phenomenon that neuroscientists feel compelled to use the term “representation” where their counterparts in other areas of biology do not. Maybe so, but I think the issue gets at some deeper theoretical assumptions about the functional organization and evolutionary purposes of nervous systems, and representational assumptions exert real repercussions on scientific practice. Freeman and Skarda (1990) argue that their research on the behavioral correlates of the olfactory system was misled for over a decade by the assumption that the “spatial patterns” in the olfactory bulb “served to represent the odorant with which [they] correlated it, and that the pattern was like a search image that served to symbolize the presence or absence of the odorant that the system was looking for (376).” The authors claim that their assumption that bursts of activity in the olfactory bulb would be a reflection of the stimulus caused them to

“[misinterpret] the data (377).” Treating patterns of neural activity as representations inclined the authors to ask questions about “pattern invariance and storage capacity” and to view stimuli information theoretically as “full information...delivered into the system and...degraded by noise (377-378).” Shedding these representational assumptions, they argue, allowed them to begin asking different questions about the phenomena. For example, they asked “how these patterns could be generated in the first place from less ordered initial conditions,” about the

66 “temporal dynamics of their development,” and began to view the brain as a self-organizing thermodynamic system (377-378). The authors' arguments reveal that the notion of representation they employed was quite diffuse, as I have suggested is common in neuroscience. They describe representational processing as “the manipulation of...symbols according to certain semantic rules,” and argue that the context dependence of activation patterns precludes a representational treatment. Both of these suggest that they are thinking of the symbol systems approach, as representationalist connectionists would happily deny both of these claims. At other times they take issue with information theory, arguing that the signal vs. noise distinction fails to apply to open thermodynamic systems. They also reject connectionist representations as useful only for engineers of connectionist networks. This helps drive the point home: even without an explicit concept of representation, representational assumptions can exert a decisive influence on what neuroscientists pay attention to, their interpretation of the data, and what sorts of questions and answers are considered. This just goes to show that, far from being merely terminological dispute, even vague representational assumptions make a difference to what scientists think they are looking for, what they look at and, ultimately, what they see in their results. Freeman and Skarda have a further worry about taking on unexamined representational assumptions that deserves some attention, although they do not go into much detail. They claim that, in addition to the possibility of misleading research, these assumptions are “seductive and enervating,” giving one the impression of knowing more about how the brain functions than is warranted. In one way, this can be seen in the reverberating effects that even minimal representational assumptions can have on how neuroscientific research is directed and interpreted. Interpreting a mechanism like the Pacinian

67 corpuscle as a receptor for a certain range of vibrations is very likely to influence how the activity of downstream mechanisms are interpreted, how their boundaries are delineated etc.

But, I think, their concern here is more psychological. The worry is that calling certain parts or processes of nervous systems “representational” provides one with the illusion of understanding systems that, were we more honest, we would recognize are still very poorly understood. The move that they seem worried about is the kind that is made when, for example, the activity measured in single-cell recordings is correlated with the activity of other cells (or properties of a stimulus etc.) or particular types of lesions are correlated with a loss for some capacity and this is taken to be evidence that the cell or cells represent other cells, the stimulus, information relevant to some ability etc. When taken too seriously, inferences from correlation to representation like this seem to invite carelessly sliding from what is possibly a useful working hypothesis to a false sense of comprehending the functional significance of the activity being measured. Given how little is known about how brains work, the complexity of the interactions involved, and the fact that what can be measured/intervened upon jumps from the very small (molecular/cellular) to the relatively large (fMRI and behavior), it is easy to imagine how poorly justified representational attributions might engender a misguided overconfidence in one's comprehension of the significance of the process in question to behavior or brain function.

So, what now?

The above introduction to cognitive neuroscience's combination of heterogeneity and promiscuity in its application of representational concepts reveals a hodgepodge of posits. Even

68 when grouped by the three rough types employed above, there is a great deal of variation in the entities posited, the empirical considerations that motivate them, and the roles they are meant to play in explanations. Moreover, like Ramsey's sponge analogy, some of the posits dubbed “representational” appear capable of functioning in explanations without being treated as representations, acting as mere correlates, causal relay's etc. Not all representations are created equal and, at least in some cases, calling something representational seems to be prompted more by the assumption that cognitive neuroscientists ought to be identifying representations (or by professional convention) than by any light that the attribution of representational import sheds on the process under investigation. The idea that we might be able to distinguish dubious uses of representational terminology from those that use these terms in a way that facilitates understanding will be the driving force in the discussion of various cases that will comprise the next chapter. I will contrast instances of what I see as dubious representational posits with cases where representational terms do substantial explanatory work. I will argue that developing some basic strategies for distinguishing these two kinds of cases will enable us to develop an account of representation's explanatory use – one that answers ultimately not to philosophical definitions, but to the explanatory ambitions of cognitive neuroscience.

69 Chapter 4: An Analysis of Various Cases of Representation in Cognitive Neuroscience

Last chapter I identified two prevailing reactions to the anti-representationalist arguments emerging from Tightly-Couple Cognition (TCC): refusing to acknowledge TCC's challenge and loosening the scope of the concept “representation” to cover even apparently non-representational explanations. These evasive maneuvers may ease the worries of advocates of representational approaches, ensuring that they can continue to use representational terminology in their favorite explanations, but these replies seem less substantial upon closer scrutiny. If the most prevalent defenses in the literature are any indication, the conviction that cognitive and perceptual behavior is best understood in terms of representational systems rests primarily on tradition. Moreover, the concept-loosening strategy concedes too much of the argument to the anti-representationalist. TCC's anti- representationalist charge is not only that some cognitive and perceptual phenomena can be accounted for without reference to representation, but that the concept of “representation” pulls little explanatory weight, is often unclear, and suggests ways of drawing boundaries around systems that obscures a better conception of the relevant explanatory factors.

Whether the TCC approach does, in fact, become ascendant as the basis for a mature cognitive science is a matter to be decided in the empirical long run. But, if representational models turn out to retain a place in cognitive science, the concept-loosening strategy's recourse to definitional revisions should not be the basis for their continued use. Of course, cognitive scientists can decide to continue using the word “representation” in the face of any empirical developments if they are willing to make the term's application elastic enough but, again, this

70 may come at the cost of ceding the only ground worth defending to the antirepresentationalist;

“representation” would be inert as an explanatory vehicle, as TCC's advocates argue, being consistent with any system regardless of its spatial and causal organization. While an elasticity of this sort is an embarrassingly extreme variety of this strategy, the actual instances of concept-loosening (e.g. Bechtel, 2001; Markman and Dietrich, 2000) available do broaden

“representation” enough to essentially admit defeat in my view. As we saw in chapter 3, when the teleological terminology is unpacked and some intentional loans are discharged, it often becomes clear that ascribing representational status to an evolved biological system (or an artifact) connotes little more than ascribing internal states that are causally relevant to its behavior. This is a complete concession to TCC's accusation that “representation” is not contributing to explanation; it can be agreed by both sides of this debate that there are internal states that are causally implicated in behavior. If this is all that representation is, then its content is consistent with the possibility of a mature cognitive science wherein representational terminology is applied exclusively to phenomena that are already explicable in non- representational terms and thus would be adding nothing to our understanding of cognition.

The evasive and conciliatory quality of the standard defenses of representational modeling is surprising; one might have believed, as I do, that the centrality enjoyed by representational and information-processing concepts following the “cognitive revolution” had been motivated by nontrivial premises or intuitions. As I argued last chapter, trafficking in representations makes up a subset of the available solutions to creatures for getting around in the world. That some now suggest that the capacity to self-organize into stably coupled agent- environment systems may offer an alternative array of non-representational routes seems to

71 have caught more traditional cognitive scientists off guard, forgetting why they thought the representational approach might be a good idea in the first place. Instead of meeting TCC's aspirations of displacement, or outright replacement of representational modeling with a serious investigation of the historical (and continuing) appeal of “representation,” most seem to have either ignored the allegations, claimed representation was just obviously necessary, or tried to build costly a priori fortifications against any future elimination.

Strategy:

This chapter will be an attempt at beginning the analysis I see as having been lacking in the usual responses to TCC's antirepresentationalist arguments. To begin with, I will do my best to avoid evasive tactics. Avoiding an evasive approach requires taking TCC's strongest cases as existence proofs for the possibility of genuine, non-representational, explanations of cognitive phenomena. This means countenancing the prospect of their occasionally, or even frequently, replacing existing representational explanations with non-representational ones. My primary task will be to supply evidence that representational approaches offer a distinctive, and valuable, explanatory apparatus when applied to appropriate cognitive systems. My confining this claim to “appropriate” cognitive systems reflects my contention that it is an empirical question whether or not a representational solution was selected for in the case of any particular cognitive or perceptual capacity. I take this commitment to be modest, asking only that (in contrast to definition-loosening) attributions of representational significance carry with them empirical implications about the types of causal/informational organization one should expect to observe in the relevant systems. That is, if “representation” and its theoretical ilk are

72 not lacking explanatory value, then representational accounts ought to be specially equipped to elucidate relevant patterns in some systems and not others; after all, almost everything is a dynamical system whereas, one might think, representational systems are quite rare by comparison. The burden lies upon the defenders of representational approaches to demonstrate that treating some dynamical systems' as representational provides novel resources for understanding the phenomenon under investigation.

To this end, I will examine several allegedly representational models, confining my survey to cases whose proposed mechanisms are the least biologically speculative. These are taken to be explanations in cognitive neuroscience whose appeal to representational processes occurs at a level constrained by the known biological organization of the relevant neural systems and not at scales of complexity whose dynamics are poorly understood, if at all (as often occurs in accounts of “higher” cognition). However, as you will recall from chapter 3, cognitive neuroscience lays claim to a heterogeneous assortment of uses for representational terminology that is often inconsistent, used carelessly, or misleadingly applies a representational veneer to processes that are explicable in less theoretically-loaded language.

At other times, I will argue, one finds representational posits that are not so obviously misguided and appear to be making significant and novel explanatory contributions. I will attempt to use this inconsistency to my advantage. In this chapter I will search for cases that can answer my revised job description challenge (satisfying desiderata A-C) by comparing various uses of representational terms in a few models from the cognitive neuroscience of relatively simple nervous systems. For purposes of review, I will present these desiderata once again:

73 A) It must be able to cite models or explanations that make explicit use of “representation,” “information processing” or related terms. B) The models cited must explain something that is not already explicable in other terms. C) Allegedly representational processes must play functional roles in a system that are recognizably “representational” (i.e. their treatment as representational must elucidate aspects of the system's organization/causal economy such that it provides an empirically motivated way of distinguishing between systems whose evolution “discovered” representational solutions to cognitive/perceptual problems from those that did not.)

My hope is that teasing apart examples of unwarranted representational treatments from those that impart a unique contribution to our understanding will allow me to develop an account of the empirical motivations and explanatory purposes that have lent representational accounts of cognition such lasting appeal. I will do so by trying first to show that we can use these desiderata to identify representational explanations whose explanatory power would not be significantly impacted by retaining the mechanistic explanation while jettisoning the representational description. Second, we will try to use these considerations to identify explanations where this separation is more difficult by showing that their representational elements are needed to secure some generally accepted explanatory virtues (predictive power, broadening explanatory scope, unification etc.). Connecting these elements with these virtues, I hope, will allow us to better understand the motivations that many feel that representation is a strategy that may have occasionally been adopted by nervous systems in the course of evolution, and that there are definite features that might identify systems where this is the case.

Some Cases of Merely Nominal Uses of Representation:

Many accounts of representation in cognitive science can trace the origins of assigning informational content to Hubel and Wiesel's work on the mammal visual system (1959, 1962),

74 usually in discussions of their discovery of “feature detectors” in the cat's striate cortex.

However, the idea seems to have become ascendant soon after Shannon's (1948) account of information appeared on the scene, and had been gaining a foothold in psychological and neuroscientific circles since Edgar Adrian's (1928) pioneering study of the functional attributes of nerve signal transmission, where now fundamental concepts such as all or nothing firing, rate coding, and desensitization were first discussed in detail. Hubel and Wiesel actually made little written use of phrases like “edge detector” and attended primarily to describing cells' response profiles and receptive fields – correlated mostly with activity within regions of the retinal surface (rather than the object perceived). Wherever the representational picture first emerged, the view that perceptual and cognitive functions are the result of neural messaging/signaling (Adrian (1928), Hubel and Wiesel (1959) etc.) , biological mechanisms for information storage/processing (such as Hebb's (1949) synaptic theory of learning), alongside the somewhat bi-directional influence of what was by the 1960s an increasingly computational psychology have likely all contributed to the heterogeneity characterizing representational terminology in neuroscience that was discussed in chapter 3. I will now look in more detail at specific instances of this heterogeneity, beginning with those that employ what I would call merely “nominal representation.”

Case 1: Spooked Cockroaches

My first example comes from Comer and Dowd's (1993) paper on multisensory processes that guide a stereotyped escape behavior found in cockroaches. As the authors begin, “in vertebrate animals, orienting movements generally depend on information encoded

75 by sensory interneurons and premotor interneurons within 'computational maps'” such as

“orienting movements of the eyes and/or head toward visual cues...and visually triggered reaching movements of the arm (89 [emphasis added]).” They argue that explaining these kinds of behaviors likely involves “determining the neural code by which direction of the movements is specified by the brain (89 [emphasis added]).” Comer and Dowd attempt to apply the

“ensemble coding” approach to relatively simpler nervous systems with processes implicating identifiable cells and their groups, rather than the large, distributed neural populations likely involved in visually guided coordination in humans. In this case, they look at what is known as a wind-triggered escape response found in cockroaches. When an object (like a predator) lunges toward a cockroach it is preceded by a gust of wind. Cockroaches are sensitive to this and will typically turn and run in the opposite direction when a sufficiently sudden or strong wind is felt.

Comer and Dowd's study investigates the primary factors determining turning direction in the escape behavior from the perspective of the cockroach's sensory apparatus. Accordingly, before we can delve into the picture of representation that emerges from this model, a quick introduction to the anatomy is needed.

Two antenna-like sensory organs called cerci extend from the tail end of insects such as cockroaches and crickets. Both cerci have many mechanoreceptive hairs that each are innervated by a cercal sensory neuron and each one sits in an asymmetrical pit that forces it to pivot within a limited range of positions. These pits are distributed across the cercus in columns grouped by the preferred direction of movement conferred by the pit's shape. The receptors themselves appear to have a broadly tuned preferred direction of wind deflection; increasing their rate and amount of firing proportionally with their degree of deflection. The axons of

76 these receptors then project to one of the contralateral giant interneurons (GIs) sandwiched between the cercal receptors on the tail and the thoracic motor centers. The authors' research is focused mainly on the activity of the GIs. They are interested in developing an “encoding” model that not only explains the way that this mediating neural architecture governs the pivot direction in the escape behavior but, later, how this system interfaces with the touch-sensitive cells of the antennae. For now, though, I will shift my attention to the way that representational terminology functions (or fails to function) within their explanation.

Although Dowd and Comer (1993) state that their aim is to elucidate the “neural code” that organizes the cockroach's turning behavior, use of encoding- and information-related terms is sprinkled throughout their account rather infrequently and when it does occur it varies in its explanatory utility. It is worthwhile to look at a few of these in some detail. For example, one section entitled “Central Representation of Wind-Sensory Information” details the anatomy and organization of the GI axons, their grouping, and the methods previously used to study their structure. It is mentioned that intracellular recording has shown each GI to be “wind sensitive...[and] five of the seven” on each side of the organism to be “directionally selective in their response to wind – i.e. they fire more impulses, and at shorter latencies when wind arrives from a preferred direction (91).” Aside from this, and the inference that “presumably, then, the relative activity of different GIs might be a code for wind direction (91)” the rest of the brief section concerns the novel multiunit extracellular recording methods that have revealed the fact that “all neural components show activity for all wind angles, and there is a pronounced pattern across the population: at all angles (except straight ahead) the GIs...are more active ipsilateral to [the] wind puff and substantially less active on the contralateral side (92).” Their

77 next section, entitled “Models for Integrating Wind-Sensory Information” details various lesion studies and interventions (including removing an entire cercus on one side) that can produce a bias toward turning to one side, even when it is toward the source of the wind puff. Beyond this, the section contains only one statement that looks plausibly representational: “normal physiology and lesion studies just cited suggest that the way wind information is displayed bilaterally within the CNS is related to an animal's direction of turning (92 [emphasis added]).”

Although the meaning of this language will become clear only when we fully see how it functions as part of their model, it is worthwhile just to remind ourselves of some common ambiguities in representational neuroscience that were discussed in chapter 3. On the one hand, we have claims expressed under the headings “Central Representation of Wind-Sensory

Information” and “Models for Integrating Wind-Sensory Information.” We are told that GIs have been shown to be wind-sensitive and that wind information displayed in the CNS affects turning direction. On the face of it, both statements seem to say little more than that, say, wind from direction x reliably predicts a proportionally higher level of activity among GIs 11 through

14. This does not by itself seem to be obviously involved with central representation of information in any way; unless, of course, we intend only the what I discussed as the “spatial representation” concept in the previous chapter. Again, this is little more than a claim about neural real estate - saying that a group of neurons “represent” means only that a certain sensory surface appears to disproportionately influence a specified group of neurons or that disruption of these areas is predictive of a specific functional deficit. One can see a version of the spatial representation in the example of what is called the “cortical homunculus” in humans and other mammals, where portions of the motor or sensory cortex are depicted as mapping to

78 a projection of the human body in a way that preserves their anatomical proximity (surfaces mapped roughly to the leg are adjacent to the hip, which leads to the torso etc.). But this notion of representation seems in many cases replaceable with a non-representational interpretation for reasons discussed in chapter 3. Without some additional details about how a spatial representation of the body in the homunculus might be leveraged to functional advantage, all it reveals is a plausible causal relationship.

The second description suggests that the relative activity of GIs be a code for wind- direction. This also lends itself to a few possible interpretations. It could mean nothing more than that they are wind-sensitive in the real estate sense just described. It could mean that the relative activity levels of the GIs on one side of the animal predicts turning direction, or it could mean that the relative activity level of the GIs are patterned (temporally, spatially, etc.) in a way that stores, extracts, or otherwise transforms the activity of the cercal sensory ganglia into a code formatted for use in a later stage of processing (say, the motor center). It is only this last reading that looks like what one might call “central representation” or “encoding,” and perhaps even this description might hold of some non-representational mechanisms. With these readings given some preliminary unpacking, let's look at how they fit into the model.

Comer and Dowd (1993) constructed a computational model of the cockroach CNS consisting of a randomly generated wind puff angle, a probabilistic estimate of the number of action potentials fired by the left and right GIs, and a thoracic integrator (their stand-in for the motor center). In the GIs the overall number of impulses could vary, although the temporal spacing between simulated spikes could not. There were two ways that the simulated cockroach's turning behavior was determined. Both methods were based upon summation of

79 the activity of the left and right GIs – a process known as a “neural comparator.” The first kind was an “absolute” comparator; the thoracic integrator would determine which “side had a sufficient level of activity (a given number of impulses occurring within a specified time interval) and directed the animal to turn” in the opposite direction (96). In the second, “relative” comparator model the thoracic integrator would either inhibit or excite the opposite side motor center, “effectively computing...the difference in activity on the two sides of the nerve cord

(96).” In both cases control over the animal's turn direction in response to any wind puff amounts to a competition between the right and left GIs and both the absolute and relative circuits seem to reproduce the behavior observed in their biological counterpart equally well.

Here we have a clearly mechanistic, but not obviously representational, model of what seems to be a plausible partial explanation of this ability. But Comer and Dowd argue that the fact that both types of models conform equally well to the data “says something fundamental about the representational character of information in the wind-sensory system: There do not have to be any cells...that compute relative amounts of wind-evoked GI activity on the two sides of the nerve cord – even if...directions of turn are predicted by relative activity levels of left vs. right

GIs.(96).” That is, the behavior so far observed in the animals does not distinguish between these two neural architectures and while physiological studies do “indicate that they sum inputs from one or both sides, but cells comparing left and right...have not been described

(97).” Again, as yet, this insight seems primarily to involve a distinction between two mechanisms that are not obviously representational – perhaps satisfying desideratum A but most likely failing to secure B and C.

80 This appeal to representation and information processing can readily be found throughout the literature on invertebrate sensory processing; Jacobs, Miller and Aldworth's

(2008) review of similar sensory mechanisms in the cricket begins by summarizing the neuroscientists' task as composed of three challenges:

(1) to understand the relationships between spatio-temporal activity patterns in sensory neural ensembles and the information they convey, (2) to understand how the spatio-temporal patterns are decoded by cells at the next processing stage, and (3) to understand how computations (e.g. pattern recognition) are carried out on that decoded information (Jacobs et al., 2008, 1819 [emphasis added]).

Another review, Burdohan and Comer 1996, describes GIs that “encode information on the direction of wind stimuli...which is then translated into directional motor output (5830).” This way of interpreting the functional significance of GI activation is common and part of a larger usage of representation-talk across various areas of cognitive neuroscience. Of course, one can understand and describe Comer and Dowd's model in terms of information flowing from the cerci to the motor center, mediated by CNS processing in the GIs, but the authors calling the relative activity of the left and right GIs an “encoding” appears to be dispensable at no cost to one's understanding of the mechanism. This suggests that their description of the response profiles and anatomical organization under the heading of “central representation in the CNS” is a matter of taste and is a kind of shorthand for something like “the main cells that first respond to cercal sensory activity.” Desideratum B asks of models marshaled as evidence in favor representational explanation that their key examples (at least) should not have an equally explanatory non-representational account available. Comer and Dowd's model is almost that already; the representational terminology is not required for, nor does it lend any additional explanatory purchase to, the explanation on offer. As such, I will use it as a primary point of

81 comparison as I look to similar instances, as well as cases where I will argue that representation is working to earn its keep. Before looking at further examples, though, a few important points of clarification must be made.

First, my analysis of Comer and Dowd's model is not intended to prejudge the issue of whether or not the GIs have a representational function. It may turn out that GI activity, when better understood, is clearly encoding information for some decoding mechanism, and that the

“intentional loan” (as Dennett might describe it) has paid dividends on our initial investment in drawing our empirical attention to important functional aspects of the mechanisms involved.

My point is that, despite Comer and Dowd's using explicitly representational terminology

(satisfying desideratum A) there seems to be little in the way of activity that gives this way of describing things the kind of warrant I am interested in (failing both desiderata B and C) insofar as their comparator model can be understood in other terms like GI competition, summation etc. I am not criticizing this type of use of representational terminology or claiming that Comer and Dowd don't know what they're talking about; of course researchers like Comer and Dowd know better than I do what they mean when they say that GI activation “encodes” wind sensory information. But, because the heterogeneous use of these terms by different investigators, domains, and across scientific communities, it is helpful to try to understand this vocabulary locally in terms of its function in an explanation before one assess this case's potential for meeting the modified job-description challenge outlined in the last chapter.

Case 2: The Love Songs of Flies

82 The antennae of the common fruit fly Drosophila melanogaster have long been known to play at least two important roles in their sensory lives, seemingly acting as both olfactory and auditory organs. And, at least since Shorey (1962), it has been known that drosophila males perform what are now commonly called “love songs” (patterned wing vibrations) as part of their surprisingly complex repertoire of courtship behaviors. As Ewing – whose (1978) paper was called “The antenna of Drosophila as a ‘love song’ receptor – described it, the love songs consist of two main parts, a sine song and a pulsed song (Ewing, 1983). The pulsed song is

“characterized by brief sound impulses separated by species-typical interpulse intervals

(Tootoonian et al., 2012, 788),” and has been implicated in mate selection – becoming the focus of most research on these songs. Importantly, drosophila is also one of the most studied model organisms in biology whose well-characterized physiology and genetics make it a great candidate for a study of the sensory machinery of simple nervous systems, as a wide variety of interventions and a wealth of information on its neural organization are available. These two factors have produced a recent resurgence of interest in audition in drosophila and, in the first case I will present, it appears to present an example of what one might call “nominal representation” in certain respects, while at other times employing representational concepts that might be contenders in my revised job-description challenge.

My first example from this literature is Yorozu et al.'s 2009 article entitled “Distinct sensory representations of wind and near-field sound in the Drosophila brain.” In it the authors apply several imaging and ablation techniques to study what they call “sensory representation” in a structure known as Johnston's Organ (JO) found in fly and other insect brains. Specifically,

Yorozu and her colleagues were interested in differences in the activity elicited by wind stimuli

83 as opposed to sounds like love songs (what they term “near-field” sounds). This focus was motivated by a couple of factors. While the function is still not understood, drosophila have been shown to quickly stop walking when confronted with air currents of enough strength. All the evidence on the sensory basis of this response has pointed to the arista – small feather-like bristles that line the antenna – and the mechanoreceptive JO neurons that respond to arista movement, whether the stimuli is intense like wind or relatively gentle movements such as sound. For Yorozu et al. the question seems to be “how flies are capable of distinguishing between distinct situations via the same organ – the arista – undergoing a similar kind of stimuli

(i.e. being deflected by air movement)?”

The study – ticking off desideratum A – is described in the title as having identified

“distinct sensory representations” in the fly brain for wind and sound and that “different neuronal subsets within the wind-sensitive population respond to different directions of arista deflection caused by air flow and project to different regions of the antennal and mechanosensory motor centre, providing a rudimentary map of wind direction in the brain (201

[emphasis added]).” What do Yorozu et al. (2009) refer to here? First, it should be noted that terms such as “representation”, “map” etc. (these should be familiar by now) occur mainly in the title and introduction. The bulk of the paper’s content attends primarily to the tuning profiles and spatial organization of JO neurons and how these features allow flies to respond differently to sound and wind via the same sensory organ. Interestingly, these two representational claims perform quite differently in the revised job-description challenge. I will first look at the content of both before providing some analysis of what this difference shows.

84 I will begin by unpacking the explanatory purpose of positing “distinct representations” in the fly brain for wind and sound. The authors begin this discussion with the strong correlation they found in imaging between sound stimuli and two regions of the antennal sensory-motor center (where JO axons from the antennae terminate. These two regions (they call them zones A and B) became strongly activated by presentation of the courtship song, while being comparatively unaffected by wind. Conversely, two nearby groups of motor center cells

(C and E) that displayed increased activity for wind stimuli remained “quiet” in the presence of the courtship song. Furthermore, destroying JO-C and E neurons eliminated the reflex to suppress locomotion in strong wind without negatively affecting responsiveness to courtship songs in females. While the two correlations between neural sub-populations and stimuli types are a central piece of their claim to distinct representations, Yorozu et al. also place emphasis on the response profiles of these different JO cell types; JO-A neurons, for instance, were most active briefly at the onset and offset of stimuli, whereas JO-E cells continued to spike steadily for the duration of arista deflection.

So, why think that flies employ “a rudimentary map of wind-direction”? There are a few main pieces of evidence given. First, the authors explain that wind tends to push the aristae in one of two directions – either forward or backward – and different sub-populations of neurons in the sensory-motor center respond differently depending on whether the deflection is forward or backward. Specifically, when the motion is forward, a distinct group of cells (JO-C) become more active while inhibiting the activity of another (JO-E), while the opposite direction of deflection has the reverse effect. Second, even though the antennae C cells are interspersed with (and are difficult to distinguish from) E cells, group C and E axons project to distinct

85 adjacent zones in the motor center. Yorozu et al. argue that this spatial organization “could provide a basis for computing wind direction” via an “internal comparison of activity between zones...both within and between each [brain hemisphere] (203)”.

There are two concepts of representation here that differ in terms of what and how they explain. The first concept directs our attention to groups of cells that appear to clump together (as revealed by staining) by type as they enter the motor center. This is an instance of the spatial-type representational concept we looked at last chapter; “distinct representations” in the fly brain are suggested as a means of explaining how the fly responds differently to wind gusts as opposed to the courtship song. But, when one looks at the evidence presented, the core claim is that the two types of stimuli activate particular regions/types of cells in the antennal motor center and that disruptions in each cell type or region is predictive of which of the two behaviors will be affected. The fact that the two cell types' difference in behavior is mirrored by a spatial differentiation in where they terminate in the motor center might be of interest to potential representational explanations, but if we need not interpret these details representationally then desideratum B asks that we refrain from doing for now. I argue that the

“distinct representations” description here is merely nominal, as it fails to satisfy the second two requirements. Their claim is that the stimuli of interest mutually activate and inhibit two spatially segregated regions of the brain, respectively, and that this could support different adaptive behaviors given different, albeit similar, interactions with the antennae and that the two behaviors are strongly correlated with activity in specific regions of the brain. That is, there is a mapping between wind and a region’s cells and between sound and a region of cells, and that each regions activity inhibits the other. Still, the proposed mechanism for distinguishing

86 sound from wind looks too much like applying the comparator model to these separate neural populations. The “distinct representations” posited here fail desiderata C because the story that they figure in (as yet) does not rely in any way on their storing, transforming, or computing information about what they are said to represent – if, for example, it turns out that cell zones

C and E more-or-less directly halt locomotion when sufficiently active, we would be fairly far along the way to having a mechanistic explanation of the gust-evoked reflex that obviates need for treating any stage in the mechanism as representing anything. Being strict in applying our challenge, this appears to be an example of the inference from a causal correlation to representation, however complex the causal correlation happens to be. What it seems is meant here by “distinct representations” is that the authors claim to have identified separate mechanisms for the wind- and love-song-related behaviors, respectively.

Interestingly, we can bring out the weakness of the representational posit described above by contrasting it with the other representational claim featured in this article. While I do not think that it fares much better, I will use it to suggest some additional considerations that might have distinguished this from merely nominal representation. When we look at the explanatory basis for speaking of “basic maps of wind-direction” we can see a more developed representational mechanism being described. In this portion of their experiment, the aristae were moved mechanically back-and-forth while activity in JO neurons in regions C and E

(correlated with wind-activity rather than sound) were monitored. Pushing the aristae backwards appeared to activate neurons in zone E, while simultaneously inhibiting cells in zone

C, with forward displacement having an opposite effect on each region. This still a correlation

87 between activity at the antennal sensory surface and activity in parts of the brain, but the authors offer a more ambitiously representational conjecture:

This model can explain the asymmetric activation of zones C and E...because this stimulus produces opposite deflection of the aristae...[An] internal comparison of activity between zones C and E, both within and between each hemibrain, could provide a basis for computing wind direction. (203)

While this mechanism is only a provisional hypothesis – and is still reminiscent of Comer and

Dowd's comparator model – when combined with the evidence backing up the claim to

“distinct representations” one sees the suggestion of a possible explanation that would depend more essentially on interpreting the relative activity levels in zones C and E as forming a simple map of wind direction. To see this, we will combine the spatial-type representation described above with a receptor-type concept of representation. Recall the authors’ attention to the response profiles of the various cell types. Although they are afferents to the same aristae, JO- neurons that later group together in the antennal cortex appear to have affinities for various properties of aristae deflection. They likened this to tactile neurons in mammalian skin, where quickly adapting cells are thought to facilitate sensation by firing action potentials on the onset and offset of pressure, while tonic firing cell types respond with continuous activations as long as pressure is applied. As in tactile perception in mammals, this specialization in cell types is presented as evidence for cell zone's C, E etc. as forming distinct representations of wind. The authors posit that, since cells that were sensitive to continuous pressure seem to respond preferentially to certain directions of arista deflection, their total response could act as an indicator of the overall direction of the wind. While it is speculative at the time of their writing, this proposal hints at a more essentially representational mechanism. For this activation to act as an “indicator” of anything, one might think, there would need to be some consumer

88 mechanism with access to the relevant features of both hemispheres – “[An] internal comparison of activity between zones C and E, both within and between each hemibrain (203).”

As opposed to the simpler comparator model, where the competition between the two cockroach GIs more-or-less facilitates the environment’s direct control of the direction of movement, the computation posited here in passing has empirical implications that rely on the interpretation of specific cells as “coding” for direction. For example, subsequent research into the architecture of afferents to the antennal lobe might reveal that there are no structures that could support such a computation at a gross anatomical level. Alternatively, future intracellular recording that “assumes” that these cells are vying for inter-hemibrain dominance might find that there are more nuanced variations between same-typed cells than would be predicted by a competitive model, or that they bear signatures more typical of something like contrast- enhancement than of summation. In both examples, the empirical implications that are raised appear to follow from the assumption that the known organization and behavior of these structures can be explained by how it reliably supports the encoding and transformation of representations. Of course, these possibilities are complete speculation, but I indulge these fictional scenarios to put into relief how Yorozu et al.’s description of “distinct representations” of wind and sound differs importantly from their exposition of what is meant by a “rudimentary map of wind direction.” While neither offer a satisfactory defense of representation from its philosophical opponents, I hope that the difference between the two is clear. The first explanation offers nothing we have not seen in other spatial or real estate concepts of

“encoding”, while the second begins to suggest certain implementation details and empirical commitments that appear to be accessible only when applying the representational gloss.

89 More Essentially Representational Explanations:

Case 3 - Analysis of Chemical Signals

I have been contrasting the explanatory apparatus in my examples with what I have been referring to as more “essentially representational explanations”. My first example comes from John G. Hildebrand's 1995 paper “Analysis of chemical signals by nervous systems” that presents a review of the neuroscientific literature regarding olfaction. His review is centered around a study his lab performed on “one of the most extensively studied examples of neural processing of semiochemical information: the sex-pheromone specific olfactory subsystem in male moths”, that he describes as exemplifying an “exaggeration of organizational principles and functional mechanisms” characteristic of many such sensory systems (67).

The basic architecture of olfactory anatomy is common to many creatures – many receptor cells are distributed in a specialized tissue called “olfactory epithelia” that are exposed to the external environment and, thus, make direct contact with airborne chemicals. As we have seen before, these olfactory receptor cells (hereafter ORCs) typically come in different types with varying sensitivity to a range of molecules, and each project to specific “glomeruli” - round structures formed by the synapses of ORCs with one of a few specialized kinds of cells.

Hildebrand's discussion begins with explicitly representational language, stating that the capacity to “distinguish myriad odors” is due to the “generation of action potentials in temporal...and spatial...patterns that represent features of the stimuli (68).” As I hope to have shown, this assumption is common to many studying the mechanisms underlying sensory systems and the account below certainly meets the (rather low) barrier for entry in satisfying desideratum A. However, what is important about this case is that the author begins with two

90 commitments of immediate interest to satisfying desiderata B and C. Specifically, he argues that “understanding the neurobiology – the organization and function of the neural circuits – of the olfactory system is crucial for relating properties of an olfactory stimulus to an animal's behavioral responses to it (68).” This ambition to tie one's representational posits to well- characterized anatomy and mechanisms is a crucial, and seemingly more elusive, part of meeting desideratum C. Secondly, addressing both my requirements in B and C, his explanation is tied somewhat inextricably to explaining aspects of the biology in terms of their capacity for supporting representational processes: “According to this model, olfactory information processing involves generation of a series of activity maps termed 'molecular images,' in the olfactory pathway (68, emphasis mine).” We will now look briefly at some of the content of these claims, and try to get a sense of how they function in the model being advanced.

The study the article presents concerns “Neural Processing of Sex-Pheromonal

Information in Moths (69).” In it, the researchers stimulated male moths with female sex- pheromones specific to the species that are known to elicit a characteristic behavioral response while tracking the effects in a series of structures downstream of the olfactory epithelium. Their analysis of the processes at each stage were informed by an interest in the quality (which pheromone), concentration, and how intermittent the presented stimulus was, as only stimuli falling within a certain range of each of these properties appears sufficient to provoke a sexual response. The team used a variety of intracellular recording and imaging/staining techniques to map response profiles of individual ORCs at the sensory surface, to try to trace the cascade of responses in the antennal lobe, and to map the functional organization of each structure thought to be involved in scent discrimination. At different levels in the pathway, they claim to

91 have found, the “olfactory system uses distributed neural activity to encode information about olfactory stimuli (69).” As expected, at the olfactory epithelium on the antennae, different ORC types displayed different affinities for specific odorants and would become active proportionally to the pheromone presently binding to the cell. As they project away from the epithelium, the axons of the ORCs are arranged such that cells of different chemical proclivities intermingled. Notably, though, these axons neatly arrange again by type as they approach the array of glomeruli, as each glomerulus contains synapses for a single variety of ORC. This process of “regrouping...accomplishes a reorganization of the axons (68).” Hildebrand explains the significance of the organization of the previously disorganized set of cells in terms of how their structural arrangement contributes to solving a problem: “The pattern of activity in the epithelium...constitutes the first molecular image of [the] stimulus, which represents the determinants of the stimulating molecules” and that “the initial representation of an odor stimulus in the olfactory pathway does have a spatial structure (69, emphasis mine).” In order to understand why this spatial format might have emerged, we must look to the later levels in the pathway as described here:

At subsequent levels...new molecular images...are formed as patterns of activity across an array of neural elements...across the array of glomeruli...yet another molecular image is generated as the pattern of activity across the array of projection-neuron axons emanating from the glomeruli. Each molecular image of a particular odor stimulus exemplifies the way neural space is used at that level in the pathway to represent information about the stimulus (69).

Hildebrand argues that this organizational principle suggests a modular system harnessing the power of what is known as “across-fiber pattern” encoding (often contrasted with “labeled- line” encoding). Labeled-line coding is said to occur in systems where receptors are tuned to respond preferentially toward a specific kind of stimuli and project to afferents “concerned”

92 with detecting that stimulus in particular (if you will pardon the intentional loan). Across-fiber pattern coding, on the other hand, involves receptors tuned to fire in the presence of a wider range of stimuli, and information about a stimulus can only be derived as a function of the collective activity of a group of such receptors (Sandoz 2007). On this model, the state of the

ORCs in the olfactory epithelium, where receptors of different types are interwoven, provides the initial odor image, consisting of various levels of activation corresponding to what

Hildebrand calls “primitives” indicative of “molecular length, position of functional groups, geometry of double bonds” of multiple molecules (Hildebrand, 69). The re-organization of ORC axons into kind-specific assemblies as they terminate on the glomeruli allows the activity of the glomerular array to serve as a simplified vector representing a picture of what is occurring across the entire olfactory epithelium. At this level, this “molecular” image presents a across- fiber “molecular image” carrying information about the potential chemicals present (their concentration etc.) where a “characteristic set or pattern of modules would be activated by a given odor stimulus, and particular modules could be shared by the patterns activated by different odor stimuli if the molecular determinants of the stimuli overlap (69).”

I use Hildebrand as an introduction because he does double-duty in the introduction to this section. He introduces us to some aspects of invertebrate olfaction and across-fiber encoding that will be important to the next example, but he also provides an exposition of a model that is both quite well developed and provides a picture of what a more essentially representational account might look like. The principle explanatory posits employed here – activity maps, molecular images, their transformations, etc. – are explicitly representational.

The initial odor image is taken to be a datum with definite features of interest to the organism’s

93 behavior – in this case, the tuning of specialized cells in the olfactory epithelium allows information about molecular structure to be made available to the animal’s nervous system.

The first step in making this information available, per Hildebrand, is achieved by the structural reorganization of sensory afferents in early processing. The initial grouping is based upon their spatial relations in the epithelium; chemoreceptors with quite different tuning affinities are interspersed amongst one another. However, these “afferent fibers” are then re-grouped “as they approach their central targets,” a process that “accomplishes a reorganization of the axons, from grouping based on somatotopy to grouping based on odotopy” as the olfactory axons each descend to one (and only one) cluster, forming the glomeruli.

Note that his combination of spatial- and receptor-type considerations rely crucially on the premise that this component of the larger mechanism has the purpose of representing information about the stimuli. The grouping of cells by chemoreceptive affinity is partially explained by what information they are said to be carrying; in vision or tactile perception one might expect that preserving somatotopy would be important because the spatial relationship of receptors on the sensory surface may carry information about salient spatial relationships in the stimulus itself, but not here. Hildebrand understands the significance of the spatial organization of this mechanism in light of its being used to represent odor, which is plausibly not a “spatial” domain. The reorganization of olfactory cells by odotopy in each glomerulus common to olfactory systems in many organisms is explained by the way spatial relationships between cells can be recruited to solve what seems to be a non-spatial problem: When it comes to information, the olfactory epithelium presents a noisy environment. To compound this, many different odorants are often binding simultaneously and individual receptors in this

94 tissue appear to have wide tuning profiles that respond preferentially for many kinds of molecules. The re-mapping that takes place in the formation of glomerular “modules,” on this account, acts to compress the widely-tuned array of cells into a new, less noisy, molecular image. Subsequent mechanisms in the antennal lobe seem to form associations between the patterns of activation in the array of modules and certain odors. As the author describes it: “A characteristic set or pattern of modules would be activated by a given odor stimulus, and particular modules could be shared by the patterns activated by different odor stimuli if the molecular determinants of the stimuli overlap (69).” This is a feature common to across-fiber encoding explanations. Individual sensory cells by themselves are not thought to encode determinant information about the world; instead, associations are formed with global, distributed patterns spread over large swaths of neurons that serve to clump otherwise very dissimilar stimuli into groups defined by subtle statistical similarities that (hopefully) track behaviorally-relevant similarities in the world.

The model described above exemplifies many of the features I seek in an exemplar for representation in cognitive science. Its adherents are committed to an ontology of “molecular images” and their transformations – more than enough for desideratum A. In pursuit of B, we might wonder whether Hildebrand’s program could get by without only the representational posits. This ontological cost seems hard to avoid; for all intents and purposes, all that is meant when Hildebrand talks of molecular images is that there are spatiotemporal patterns of activity at various levels in the antennal lobe that are transformed to derive useful information about stimuli. This is what Hildebrand uses to explain the capacity of the moth as well as the observed behavior and organization of the structures he implicates. The moth can detect pheromones

95 and their concentration because segments of the moth’s nervous system transform a molecular image at the sensory surface into other images that make behaviorally-relevant information contained in that image available to other neural structures. This lower-level capacity is itself explained in large part by the theory of across-fiber encoding, as it provides an implementation capable of supporting the distributed feature-extraction needed for determining type as well as intensity given the nature of the epithelium’s surface. If molecular images are removed from the equation, then the utility of across-fiber encoding for solving this problem is largely lost, if it is not lost completely. This amounts to no explanation of the olfactory capacity at all. Although there is certainly room for non-representational alternatives, this model of the mechanism does not appear to be explicable in non-representational terms, satisfying B.

The molecular images model satisfies C because the representational functions attributed to parts of the mechanism highlight aspects of the structure’s causal economy as of special interest by allowing their observed behavior to act as evidence for or against the representational interpretation. Hildebrand paints a picture of a mechanism that takes molecular images as its input and whose product is further molecular images, and this understanding of the system colors how he understands elements throughout. Being imbued with representational significance allows for the possibility of unifying numerous phenomena by portraying them as components of a larger mechanism. Chemoreceptors are understood as responding to “molecular primitives” that individually do not contain enough information to determine the type or concentration of an odorant. The grouping of the sensory axons into glomeruli (common in other invertebrates as well as vertebrates) is made intelligible in terms of how this re-mapping converts the initial molecular image into a format that can be used to

96 represent odors and their intensity. This “format” is itself tied intimately with the explanation; we are told that associations are formed between spatio-temporal patterns in glomerular activation and the quality, concentration, and intermittency of various chemicals. If one were to eliminate molecular images from this model, it would lose much of its explanatory content. The representational “gloss” here informs one’s understanding of the contribution of each component of the mechanism and has significant empirical implications about what one ought to expect given a specific experimental manipulation. If the concept of a “molecular image” as described here does not track functionally significant patterns in this neural system at all, then this model is likely to present a fundamentally misleading description of the activity of these structures. That is, to interpret the glomerular array as a molecular image formatted for across- fiber encoding is to be constrained empirically; certain details about neural architecture, if discovered, could affect the plausibility of the claim that molecular images exist. I take this to be an indicator that the posit in question is doing explanatory work and is, to the same extent, empirically well-motivated. In other words, explanations are “essentially representational” in proportion to the affect that eliminating the representational posits would affect our empirical expectations of the mechanism’s behavior. With this in mind, let’s look at another example that explores a similar topic.

Case 4 – Intensity and Identity Coding

Stopfer et al.'s 2003 paper “Intensity versus Identity Coding in an Olfactory System” also explores activity in structures in the antennal lobe (AL) of locusts. The model proposed here was motivated by a desire to explain how organs downstream of early olfactory processing deal

97 with variance and other potentially confounding factors for determining odor identity. The challenge posed to the organism in this task arises from the fact that rising “odor concentration

[leads] to changes in the firing patterns of individual antennal lobe projection neurons (PNs) similar to those caused by changes in odor identity (991).” The consequence of this is that looking at early sensory processing in the PNs does not seem to allow for a distinction between certain odors and certain odor intensities, at least at first glance. Locusts appear to be sensitive to both features of odors, so it would be nice to have an explanation that can explain the observed behavior of PNs as well as the discriminatory capacities seen in the animal.

Like Hildebrand, Stopfer et al. hold that “odor identity is encoded by spatiotemporal activity patterns...across dynamic assemblies of principle neurons” and that each such “odor representation can be thought of as a high-dimensional vector of principal neuron states evolving over the duration of the stimulus in a stimulus-specific manner (991).” They note that this model has been elaborated elsewhere, but that it had not yet been applied to trying to understand how stimulus intensity affected the elements involved. With this in mind, their team used intracellular and extracellular recording techniques to track the activity of cells in a structure called the mushroom body, as well as the first and second relays of the locust while the animal was stimulated with odorants of varying types and concentrations. Recordings of

PNs after presentation of odors of 4 concentration intensities found that the resulting activity changed substantially in quality and frequency at each level of concentration rather than co- varying linearly with the stimulus intensity. At one concentration (0.001) a PN was seen to fire a stream of action potentials before settling into quiet hyperpolarization, while at a higher level

(0.1) the reverse occurred, with further variation found between different odorants (993).

98 However, it was also found that each step up in odor concentration was not met with the associated response in all PNs simultaneously. This, they argue, suggests that “odor identity and concentration appeared to be confounded in the response patterns of individual projection cells (993). To compound this, mean firing rates in 110 PNs increased with concentration for some odors and decreased for others while, at the same time, their summed output in the lobe varied little across changes in concentration. From this it was concluded that if “stimulus concentration is represented downstream from the AL,[they] predict that it should require decoding the patterning of the AL output rather than its integrated intensity (993).” This is an important move in their argument; there is no clear correlation between stimulus concentration and individual PN activation in the AL and, furthermore, there does not appear to be an opportunity for PN summation to carry this information, as it remains stable with concentration variation over time. This presents a problem because evolutionary considerations and behavioral data strongly suggest that concentration is an important factor in olfaction in these creatures.

The authors looked at response patterns across the 110 neurons over time in 15 trials using three different odorants (two which were molecular similar and a third that differed significantly), seeking to uncover global patterns. Every trial was treated as a vector, with “k = n

X m dimensions, where n is the number of PNs...and m is the number of” spike-counts in a 50- millisecond window such that each vector “represented the spatiotemporal pattern defined by the responses of n PNs over time in a single trial (993).” Their analysis of the resulting data found interesting similarities in global patterns of activity between trials that “revealed odor- and concentration-specific structures in the spatiotemporal patterns that were not seen in

99 individual PNs (994).” While there are multiple ways to visualize their statistical relationships, perhaps the state-space diagram (similar to Churchland and Sejnowski’s example in chapter 3) is most striking. In this visualization, response vectors are clustered into distinct regions of the space in a way that preserves relationships relevant to the olfactory task in question. To be more specific, vectors elicited by a single odorant appear closer to one another while, within each cluster region, vectors are arranged in a continuum of increasing concentration (see below, 995). Using this and other statistical metrics, Stopfer et al. were able to

predict the identity of both odors and concentration from individual vectors with >90% accuracy. The units of representation, they argue, are across-fiber vectors of spatially- and temporally-extended patterns of activation in AL neurons. These representations allow patterns of activity spread over populations of cells to group chemically-similar stimuli together and for finer sub-clusters within each larger clustering to reflect other relevant features such as

100 concentration. This and subsequent extrapolations of this predictive method led them to the conclusion that “odors appear to evoke distributed spatiotemporal patterns in which many neurons contribute to encoding both concentration and identity” and that these patterns are organized hierarchically, with patterns within clusters of patterns drawing finer distinctions between individual sensory events (997). One might worry that, even if we have reasons for thinking that these vectors warrant being called “representational”, the statistical patterns found within these groupings could only serve as representations if there is some consumer mechanism that can avail itself of the AL's hierarchical partitioning of vector-space. After all, even if we can be convinced that these patterns in the PNs contain information that is sufficient for deriving stimulus type and intensity, calling this information a “representation” seems to imply that there be a mechanism that uses this information. Luckily, Stopfer and his colleagues' do attempt to develop a biologically realistic account of how these across-fiber vectors are decoded.

Stopfer et al. are careful to ask that their model conforms to the realities of the AL anatomy, stating that proposed vector representations “should…be interpreted from the perspective of their targets (1001).” The targets in this case are ensembles what are known as

Kenyon Cells (KCs) where the PNs terminate. Studies of these cells indicate that “PN output is decoded by KCs over individual...oscillation cycles” such as those measured to delineate the vectors mentioned previously (997). Taking pains to conform to the biological reality of the proposed consumer of these representations, Stopfer et al. note that KCs are subject to an inhibitory “reset” after each oscillatory cycle. This means that they are subject to a stream of independent snapshots of upstream activity. Estimating that the roughly 50,000 KCs that

101 receive inputs (10-20, it appears) from the some 830 PNs in locusts, the authors offer the following possibility: “If each KC is connected to a subset of PNs that, for the appropriate stimulus, is coactivated with within the same cycle or cycles, that KC's responses should reflect the variations...of coactivity of the PNs connected to it (1000).” Such an arrangement could support “decoding” relevant information from PN activation vectors if some KCs were tuned to fire preferentially for relatively narrow concentration vectors within a specific odorant-grouping while others were “indifferent...reflecting the combined sensitivities of the presynaptic PNs

(1000).” On this model, groups of type-generalist and intensity-specialist KCs receiving inputs from the same PNs could collaborate to transform the initial vector into a further vector, pairing odor types to their perceived intensity. Pursuing this possibility, they conducted simultaneous recordings from 133 KCs in 17 trials while presenting the locust with similar stimuli as previously. What they found appeared to be conform to what was predicted by their model. “KCs...[responded] selectively to specific concentrations of particular odors” while others responded only “to one odor across a contiguous range of concentrations (1000).”

Specifically: timing of KC responses relative to stimulus onset differed across KCs and stimuli...consistent with the finding that decoding...PNs output occurs both piecewise...and throughout the stimulus duration...These results indicate that odors are represented in the mushroom body by identity- selective sets of KCs, containing cells with different degrees of concentration invariance. The observed degrees of selectivity (to odor identity and concentration) are consistent with the amount of information present in a small proportion of randomly chosen assemblies of 10 to 20 PNs (1000).

That this finding may provide some evidence in favor of their proposed representational system is interesting, but it is not terribly important to my argument. What interests me is how the above considerations reveal important motivations giving warrant to the representational

102 baggage. Specifically, they show how higher-level assumptions about informational content and its manner of presentation can guide research in a way that allows lower-level observations of the mechanism’s components to act as evidence that representation grants one access to real and informative patterns in the causal/organizational structure of the system. I will pay close attention to this as we examine how this account stacks up against our challenge.

I take it for granted that Stopfer et al.’s model satisfies desiderata A. In my earlier examples, we saw cases that sought to understand the “central representation” of wind- direction by the relative activation of two groups of cells. However, when examined more closely, the mechanisms posited looked more like a competition between the summed activation of two assemblies. This mechanism would allow the wind to more-or-less directly govern the turning direction in the animal. Given that one understands the way that the cockroach GIs will be affected by wind on each side of the body, the competitive model explains the direction of turn if there is a mechanism for allowing the winning assembly to influence locomotion in the appropriate way. This example fails to satisfy B because there is no need to invoke representation, the adaptive behavior is already explained. We can see this by noticing that the observation (by itself) that GI activity on each side of the cockroach is predicted by the degree of cerci deflection does not explain the phenomenon any better if we alter each description of their representing “wind direction” to say that the GIs represent “threat direction,” “pressure to the rear-abdomen,” or “tactile sensation.” This is another way of saying that this explanation does not have any need to commit themselves to a claim about what information is being carried by the GIs, because claims about what information is carried by these neurons are not turning any wheels in how the explanation makes the system intelligible.

103 In fact, almost any claim as to what information was being represented would be likely to diminish the explanatory power of the model – trading predictive power as well as precision for an overall loss in the number of events it was capable of explaining. For example, even if it was known that cockroaches evolved in an environment where 99% of the times this reflex was triggered it was by a specific predatory spider, if Comer and Dowd had described the GI activity as “central representation of spider attack,” then fewer events, such as a cat’s looming paw triggering the response, would be explained by this model. Of course, one could say that the cockroach benefits from a misfire in this instance, but this is an unattractive option when we can have the explanation without the added ontological cost. Describing the content of these representations as “wind information,” of course, does bring a far greater number of causal events under the explanation’s umbrella, but there is still no motivation for the added metaphysical baggage. We can see how the causal dynamics of the mechanism their model portrays would benefit the species’ survival and explain why causes other than wind – like poking a cockroach with a finger – might trigger the evasive maneuver without making assumptions about informational content. This shows that this model fails part B of the challenge by being explicable in non-representational terms, but it also is damning when it comes to C, as it demonstrates that treating this mechanism in information-processing terms does not contribute anything extra to our understanding of the causal dynamics of the system and why they might have been favored by selection.

The process of interpreting the observed behavior of a system in light of some prior functional hypothesis is common in mechanistic modeling, regardless of whether the models in question are representational. This is an example of what Bechtel and Richardson call the

104 “synthetic strategy,” wherein “the empirical task involves testing performance projected on the model against the actual behavior of the system (1993, 20).” These types of assumptions have multiple important ramifications when we evaluate how these entities function in explanation.

First, in more essentially representational mechanisms, we will expect that the content and the format of representations posited help to have empirical import that might otherwise be difficult to secure. We would also expect that such empirical implications help explain, and are answerable to, observations about the actual observed behavior of the model. After all, as

Bechtel and Richardson point out, “speculation without empirical constraints is as likely to produce spurious explanations as correct ones (21).” When we compare this to Stopfer et al.’s model, however, the representational vehicles stack up much more impressively regarding B and C. The explanatory force of their model cannot be easily maintained if its representational elements are eliminated because claims about what information is being represented in processing are intimately linked with the explanatory power of the model. The authors identify the representational entities and processes (vector representations, across-fiber encoding etc.) with observed patterns and they provide details as to their format as well as what is represented in that medium. The model’s supposition that the PNs form representations that encode information about odor identity and concentration (as opposed to other properties) informs the researchers’ decisions about what patterns to look for in their data, as well as providing an explanation of why these cells collaborate to form the kinds of spatiotemporal patterns observed. The hierarchical structure of this representational medium organizes their activity in a way that makes the confounding similarity between PNs response to changes in identity and changes in intensity less surprising. This is because the representational strategy

105 they attribute to these structures does not have a need for individual receptors to directly reflect stimulus intensity with proportionally increased activation; instead, information about concentration as well as identity are extracted from the complete vector by the structure of the activation vectors in the PN neurons. What information is being encoded, and how these encodings are formatted and transformed serve to unify several observed phenomena: the behavioral sensitivity to odor concentration and identity, the known anatomical organization of the AL, the tuning profiles distributed across neurons in multiple sensory layers, the patterns of activity seen in their measurements, the synaptic and temporal relationships borne by the KC neurons to glomerular PNs, amongst others. This process of unification is important to why I believe that this account satisfies desideratum C, as it imbues observed patterns, causal features of the mechanism, and the anatomical organization of these systems with a unique significance as components in a representational mechanism. The contribution of each component in producing the explanandum, as portrayed by this model, draw one’s attention to specific processes and patterns as of special interest. This grants the causal economy of this system an intelligibility that would otherwise be lacking, as we understand the major actors in the model in relation to each other and to their capacity for supporting the process of forming and transforming representations of a very specific kind. The story of how these representations enhance and encode behaviorally-relevant information portrays a possible solution to a problem facing the organism. For example, KCs would need certain properties to decode glomerular representations – dendrites harboring an appropriately large number of PN synapses, the right synaptic architecture, oscillatory cycles that provide the appropriate temporal window for tracking PN vectors. Stopfer et al. pay close attention to this, and

106 attempted to “estimate the information content of the PN assembly for individual cycles” by looking at the vectors in 50ms time slices as their activity evolved in the continued presence of the stimulus (998). The impetus for this seems to be motivated by their understanding that the inhibitory reset of the KCs places restrictions on the PN assembly because, if KCs are to decode their vectors, then PN activity must contain sufficient information within the narrow timeframe within which their activity can influence KCs. In other words, this model has empirical implications about the organization and behavior one should expect to find in this and related nervous systems, and these implications flow directly from interpreting spatiotemporal patterns in the PNs as representations of a very specific sort. Establishing that their model is biologically realistic grants additional credence to the claim that their proposed implementation of this solution may explain why this system developed the causal and anatomical organization that it did. This puts regularities in the relative number of cells in various regions, the number and arrangement of synapses between individual neurons, their oscillatory dynamics, and the distribution of inhibitory and excitatory processes into a context where they seem less surprising or arbitrary.

This mechanistic context also has the potential to inform our understanding of other neural structures (and other nervous systems) by endowing shared principles of organization seen to reoccur both within nervous systems and across species with an enriched intelligibility.

We can see examples of this process when existing models shape how other research programs apply these results to their interpretation of systems that they deem to share meaningful similarities. This can occur in somewhat unsurprising instances, such as the application of

Stopfer et al.’s results to understanding (relatively) closely related species, such as the honey

107 bee, but it also occasionally responsible for bringing more distantly related structures into focus. We can see an example of both in “Keeping their distance? Odor response patterns along the concentration range” by Strauch et al. This paper explicitly applies Stopfer et al.’s research on the locust to understanding the dynamics of the glomeruli of wild-caught, untrained honey bees, making two main claims. First, they believe to have demonstrated that the grouping of PN response vectors in the bee reflects their similarity and dissimilarity in the odorant along two axes: number of atoms in the carbon chain and the functional group (any combination of these features would be sufficient to identify a single odor in their experiment, as each molecule type shares a unique pair of these two features). Second, they claim that as concentration increases in a stimulus, the vectors representing these two features increase in distance from one another, while increasing their relative grouping-up along the predicted axes. What they found was that the bee’s “odor response patterns are roughly sorted by chemical (dis)similarity of the odors, in particular carbon chain length (Strauch et al. 2012, 5).” They pay much of their attention to the way that the distancing of individual response vectors as concentration increases enhances contrast between individual vectors, allowing “response pattern

(dis)similarities [to] reflect chemical (dis)similarities” with more stark differences between the groupings formed (5).” Importantly, they add that “due to the similarities between olfactory systems (2),“ this general model subsumes a larger trend seen in other animals – flies [Wang et al. 2003], zebrafish [Friedrich and Korsching 1997], turtles [Wachowiak et al. (2002)], and even mammals such as rats [Cleland et al. (2007)] – under a single explanation by showing how this enhancement of contrast is “not a bug, but a feature that improves representation of odor

(dis)similarity in the brain” and “extends prior work aimed at solving the problem of

108 concentration-invariant perception…by emphasizing that there is also something to gain from increased odor concentration (5).” Obviously, this process of explaining diverse explanada under a more general model is not unique to representational modeling (or even mechanistic modeling), but I present this example to show how the process of unification they describe can be linked to the elements of an explanation with straightforward representational commitments. While we can abstract from certain particularities in the example, such as the belief that PN activations carry information about carbon chain length and functional group, the more general pattern of contrast enhancement with increases in odorant concentration is what the authors see as common to the behavior of the glomeruli in diverse olfactory systems.

Understanding this process as contrast enhancement relies on the assumption that one can meaningfully pick out spatiotemporally distributed patterns of glomerular activity, that these vectors carry information about differences between the properties of odorants, and that there are consumer mechanisms that can leverage this differentiation to the benefit of solving a problem. Without the information-processing perspective, though, it is not clear how one could construe the evolving activity in this structure as contrast enhancement, because it is the attribution of informational content to PN vectors that enables one to see their changes over time simultaneously as a distancing and as preserving salient relationships between vectors.

This is made explicit in discussions of contrast-enhancement as a general principle of neural computation: for example, it is said that “contrast enhancement [relies] upon a representation of the similarities among stimuli in [sensory] input” in solving how “the lack of an ordered topography of stimulus quality across the [sensory] surface [limits] the mechanisms that can be deployed to perform similarity-dependent computations on the primary representation

109 (Hölscher and Munk, 257, emphasis in original).” In Strauch et al.’s case, assumption of informational content is directly built-into their modeling of the response vectors: vectors were defined by a complex relationship between groups of individual glomeruli’s activity at time t, and a pairing of concentration and identity for a stimulus at t - 4 seconds. Computing the statistical distance between glomerular response patterns, consequently, was a product of the similarities between response patterns that shared a common stimulus type (3-5). This did not guarantee that their distances, so-defined, would correlate with their predetermined metric of distance between odorant types, but it shows how the presumed informational content of the vectors shaped what patterns were seen as preserved as responses changed. To say that this mechanism enhances contrast is to say that it exaggerates important similarities of individual representations to facilitate the process of differentiating odors by the consumers of these representations. The assumption that these vectors carry information about stimuli is necessary to explain why this system preserves the patterns that it does as the vectors change, but also shows how such a mechanism would produce behavior that makes the right information available to consumer mechanisms. In applying this general framework to other nervous systems, we can see how the representational features of an account can uniquely identify some causal/anatomical features as characteristic of a specific solution to a problem. Adopting any one solution is a contingent matter of fact, but examples like this show the way that some representational models can designate certain physiological arrangements as markers of a particular mechanistic strategy.

We have seen that this model satisfies C by making it possible to account for a wide variety of observed phenomena by highlighting aspects of the system's “organization/causal

110 economy” and how this might motivate our belief that evolution favored representation in an individual case. Another crucial aspect to this notion of “empirical motivation” is the possibility that a model can possess predictive or explanatory power that depends upon the representational aspects of the model. The commitment to a specified informational format/content also pays its way in this regard, as the higher-level functional interpretation of the mechanism as an information-processing system places restrictions on our expectations about the properties of lower-level components and their causal organization. For instance, interpreting PN activity as vectors encoding information hierarchically about both identity and concentration says a lot about what spatiotemporal patterns to expect to see in future research. The model predicts how the structure and distribution of vectors in the state-space would change given certain interventions upon components or processes of the mechanism or the presentation of other odorants. This counterfactual information is present in this chapter’s counterexamples too, of course, but the important difference is what parts of the model mold these expectations. Recall Yorozu et al.’s “map” of wind direction in the fly brain. There are plenty of counterfactual predictions that could be made about this system, such as the prediction that lesioning zone C would negatively impact the wind-invoked halting response.

This prediction is motivated by the empirical situation (vis-à-vis Yorozu et al.’s research), but this motivation is not beholden to any representational commitments. The correlation between zone C cells and the behavior is strong enough to infer that these cells are at least causally implicated in the normal occurrence of this behavior (even as mere signal relays), so their destruction could be expected to impact the ordinary functioning of the mechanism. But consider predictions about where to expect various stimuli to be grouped in a state-space

111 visualization given counterfactual stimuli. This capacity for prediction issues directly from the representational interpretation; what information we think these vectors represent and how this information is structured (e.g. by hierarchical grouping) is what allows us to predict how the vectors will be grouped and how each will relate to one another. This aspect of the account also imposes useful empirical constraints on the types of observations that can act as confirmation or disconfirmation for their account. It is easy to imagine myriad ways that future data could distribute vectors that would be inconsistent with their explanation; if, say, vectors produced by the same odorant with different intensities were found at opposite ends of the state-space rather than grouped together, this would be immediately damaging to their interpretation of what information was being tracked by vector grouping. That is, the kind of information represented and format of the representational vehicles posited have empirical content. To take another example, these same information-processing assumptions have empirical implications about the behavior of alleged consumer mechanisms. The KCs, as supposed consumers of PN representations of intensity and concentration, would need to demonstrate the capacity to respond in a way that reflects the format of the vehicles carrying this information. As mentioned previously, this was the motivation for looking for coalitions of type-generalist and intensity-specialist KC neurons that received input from the same PN subpopulations, as this would enable the KCs to transform activation vectors into a new vector that paired odor identities with the highest concentration vectors within that group (Stopfer et al., 1000-1002). The significance (on this model) of shared patterns across certain PN vectors, in other words, made predictions about the tuning sensitivities, distribution, connectivity and activation vectors of KC cells, which they state openly:

112 The selectivity of the postsynaptic KCs to odor intensity could be explained by their connectivity to particular sets of PNs. A KC connected to PNs that are largely coactive (during a given oscillation cycle) across many concentrations should be concentration invariant within that range. By contrast, a KC connected to PNs that are not coactive across concentrations should be more selective (1002).

One can also see straightforward empirical implications of, say, selective ablation of the intensity-specialist KC neurons that left their type-generalist partners intact; one might expect, for instance, that identity processing might continue to function partially while increases in intensity would no longer confer any additional advantage in identification (in fact, something like this has been attempted in the KCs of the fly: see Xia and Tully, 2007). By itself, the combined set of vectors in their data would not seem to have these implications. It is the interpretation of the vectors as representations of a very particular kind and content that motivates these very precise expectations about the architecture and behavior of the PNs in the antennal lobe as well as the KCs in the mushroom body of the locust.

How does this help proponents of representation?

In this chapter, I have tried to use the 3 desiderata of my revised job-description challenge to a few examples from neuroscience. Although Ramsey’s (2007) job-description challenge inspired this pursuit in both name and subject matter, the motives behind our challenges differ in ways that are worth mentioning. Ramsey’s aim in posing his challenge is to account for “how representation can be part of a naturalistic, mechanistic explanation” by finding “conditions that delineate the sort of job representations perform, qua representations, in a physical system (27)” that cohere with a minimally commonsense folk-understanding of the term “representation.” While his analysis is distinct from the many investigations into the

113 content-determination relation in the philosophy of mind, the project is ultimately metaphysical, and is concerned primarily with seeking out and criticizing accounts of the necessary and sufficient conditions for functioning as a representation. My revised job- description challenge has a quite different goal that is (admittedly) somewhat less ambitious. As

I have argued, TCC (and Ramsey) are right to say that many ascriptions of representational significance to mechanisms are of dubious explanatory use, are poorly motivated, and admit of non-representational explanation (whether tightly-coupled or not). Moreover, much of the philosophical literature defending representation seems to be purely defensive, making evasive maneuvers rather than compelling reasons to think that representational descriptions may, in at least some cases, be warranted by considerations that are more compelling than, say, tradition in a research program.

My revised job-description challenge was conceived as a method for developing an approach for selectively defending cases of representation by showing that there were empirically motivated representational accounts even for problems that do not appear to be obviously representation-hungry. As such, the 3 desiderata that comprised my challenge were never meant to be necessary and sufficient conditions for being a representation. Instead, they were meant as tools for differentiating between individual models by the degree to which their representational posits were tied to explanatory power. This process has a few stages. The first stage was to try to understand the content of representational claims by providing evidence for how to best interpret the claim from the author’s discussion and practice. For example, looking at what kinds of observations are presented as evidence of a “map of wind-direction.” Does the

“map” feature prominently in the explanation of a behavior, or act as shorthand for neural real

114 estate? Do the authors commit themselves to specific functional properties of the “map” in their explanation? What data or other considerations led to their identifying the “map”? The next stage then applies desideratum B to this interpretation by attempting to pry away the representational veneer from the model’s non-representational mechanistic features. This is a roundabout way of linking specific attributions of representation to explanatory force. If what remained after the separation was a more-or-less intact mechanistic explanation for the behavior, then I have taken this as evidence that the information-processing aspects of the model were explanatorily inert. Evaluating how “intact” the remaining explanation was following this procedure required an appraisal of the consequences of the removal of information-processing assumptions in terms of what I hope were suitably general explanatory/scientific virtues. Desideratum B acts as a kind of filter for potential counterexamples to TCC’s anti-representationalism. Where representational language is applied in explanations as a placeholder for neural real estate, mere reliable causal co-variation between neural activity and a stimulus, or for which an alternative TCC-style explanation exists, we should expect little loss of overall explanatory power. The degree of explanatory damage done from the loss of the representational assumptions forms the metric for B’s “explicable in other terms” clause. With this filtering complete, the final stage of this process is to apply desideratum C to the case. Desideratum C seeks evidence for instances of empirically motivated attributions of representational significance by looking for specific kinds of reasons given for applying a representational model. In the cases we looked at, the reasons that were most important here tended to be the way that the ascribed format and content of representations was tested against and/or given support by, physiological expectations about the number,

115 connectivity, and organization of cells. In addition to this, we saw how anatomical similarities, as well as shared patterns of activity – such as across-fiber encoding, lateral inhibition, or contrast-enhancement – can function to unify disparate structures both within and between species, by allowing shared patterns of causal organization to play the part of markers of a specific representational strategy.

In weighing these accounts against our 3 desiderata we have seen that satisfying A by itself is rather easy; all it requires is that an explanation of a cognitive phenomenon is described in representational or information-processing terms (really, anything that failed to do this would not be of much interest here). Desideratum B’s separation of explanatorily inert representations from posits that exert a more impressive contribution to empirical content, along with C’s focus on identifying signature features of individual representational strategies, makes way for a new type of response to TCC to begin to form. The new stratagem enables potential defenders of representational theorizing to point out that the picture of representation that emerges from comparing accounts is not that there is a single type of mechanism called “representation” common to information-processing models. Instead, individual instances of representational theorizing seem to exist on a continuum from quite useless attributions to ones that would be impoverished without their information-processing assumptions (at least at present). This is consistent with the heterogeneity that characterizes concepts of cognitive representation. It also begins to carve a route around the evasive tactic.

Instead of trying to massage some concept of representation to accommodate TCC and anti- representational criticisms, one can openly embrace them while still showing that there exist information-processing accounts whose representational entities are believed in for reasons no

116 more dubious than one would give for any mechanistic entity. Based on individual accounts, there are examples of information-processing models that include entities whose function is to represent information that are not motivated by mere tradition, an inability to measure or intervene upon the level of organization of interest (as in higher-level cognition and folk psychology), and that are just as observable as other posited mechanistic components. Given their definition, for example, Stopfer et al. can count, compare, locate, and record vectors in the glomeruli. Like any theoretical entity, there can certainly be bad reasons for attributing representational content to a process, and this should be avoided when possible, since (as chapter 3 argued), unwarranted assumptions – especially pervasive ones – have the potential to stifle other potentially productive research programs. However, given that explanatorily inert posits can often be distinguished from explanatory ones, and the assumption that it is an empirical and contingent matter whether any organism evolved to employ the representational strategy, it seems premature to dismiss any one model because of a family-resemblance or terminological similarity to a larger class of explanations, even if it does include some bad apples. But this is especially true when you can show that, in well-motivated instances, representational assumptions can be tied to their predictive/explanatory power, or (as C helps put into relief) that the recognition of shared anatomical/causal structure can drive productive and successful research in a way that depends quite strongly on these assumptions. In the next, and final, chapter we will look in more detail at the lessons that can be gleaned from this exercise.

117 Chapter 5: Final Thoughts

Let’s take account of things. In chapter 2, I attempted to demonstrate that Tightly- coupled Cognition (TCC) offers a formidable challenge to representational explanations in cognitive science. The strongest of these challenges come from three main claims:

1) TCC-style research has shown that there exist explanations of cognition, action, and perception that do not implicate representation or information processing. Some of these involve agent-environment dynamics that preclude the kind of isolatable and interpretable states that representation requires. 2) Any cognitive capacity may turn out to be best explained without representation. 3) “Representation” is used pervasively in cognitive theorizing even though the concept is rife with confusion/inconsistency and appears, in some cases, to be explanatorily inert.

Each of these claims gives us reason to join the TCC-theorist in her distrust of how commonly used representational terminology is in this arena. In chapter 3, I argued that the common replies to these challenges have typically relied on analyses of pre-empirical intuitions about the metaphysics of representation – employing evasive tactics to define “representation” in a way that safeguards representation/information-processing from any possible elimination. In response, I have proposed what I see as an alternative, non-evasive approach to defending the use of cognitive representation: arguing that TCC’s three claims are true, but that the distrust they instill in representational approaches does not apply to many cases where attributions of representation are explanatory and empirically well-motivated. Consistent with premises 1 and

2, representing information is a contingent evolutionary solution to various problems of coordinating action in the world. Consequently, there ought to be empirical situations that justify interpreting some systems as information-processors. Just as there are tell-tale markers of coupling between the types of systems TCC takes as core cases, the proponent of representation should look to scientific practice to develop an account of the empirical

118 considerations that motivate genuinely representational approaches, rather than trying to establish pre-empirical definitions of representation and then seeking to see if these criteria obtain of certain physical systems. As premise 3 advises, this means clarifying how and why such terms are used, and differentiating between “representations” that justify their use in the form of explanation and productive research, and those where such terminology is misleading, explanatorily inert, or insufficiently justified. We saw that representational terminology in cognitive neuroscience comes in myriad varieties, and that some of these seem to be doing little explanatory work. Their representational import is ambiguous, and many times seems to stand upon feeble ground that could mislead future research by slipping in illicit assumptions about the nature of the functional significance of the mechanisms under study. In practice, the process of developing this analysis looks a lot like cataloging any other kind of mechanistic models. But, specifically, we separated them into mechanistic models that involve the systematic storage and transforming of information and contrasts them with the kind of attributions of representation in cognitive neuroscience that engender justified suspicion. The hope is that, in doing so, one will be able to provide an existence proof for representational explanations that offer insights into how some models cast representational explanations as a distinct evolutionary or developmental strategy for successfully getting around in the world of a living creature.

Analysis: Representation as an umbrella-term

The result of my previous case-based analysis was limited, of course, in that it allowed for close analysis of a small set of example examples that were deemed to be sufficiently similar

119 in nature to admit of a fair comparison. That said, I do think that the results offer some preliminary insights into features that might help one understand how information-processing functions in explanation – but only when it actually does. We saw some gestures in the direction of this type of account in analyzing the way that specific representational views leverage their representational assumptions in generating explanations and predictions of cognitive systems and their components. The main takeaway from my strongest examples for the would-be defender of representation is that well-motivated representational explanations are beholden to, and garner support for, their information-processing assumptions from the same kind of considerations that would be used to evaluate the worthiness of less-contentious mechanistic posits. Rather than take the course I have criticized in the previous chapters of trying to produce an analysis of “representation” as a determinate relation that obtains between X and Y when, say, certain conditions are met, or trying to show that apparently non- representational models are somehow deficient from the outset, it would be much more valuable – and true to actual scientific practice – to think of “representational models” more as an umbrella-term for a constellation of particular mechanistic explanations that share some common traits. For example:

1) An attention to properties of mechanisms that could reasonably be expected if the particular mechanism in question is solving some coordination/evolutionary problem by processing information. 2) An interest in reoccurring structures and principles of organization that might endow a mechanism with the capacity to extract or otherwise make use of information that is available to the system. 3) A focus on evidence that favors the interpretation of generalizable organizational patterns as significant in solving information-processing problems.

Examples of 1 and 2 include analysis of structures and their parts in terms of their capacity to reduce noise in a signal, to enhance contrast in an input for a putative consumer mechanism, to

120 encode or extract information, or to map particular instances of a pattern to a common more- general ‘type’. Many examples of this have been seen in the previous chapters. We see it in the recognition and application of known anatomical arrangements that may reappear in diverse nervous systems. Mechanisms such as lateral inhibition, topographical organization in many sensory systems, or principles like population coding: for example, how “spike frequency adaptation” has been found to govern olfactory vector discrimination in some insects, but also appears to be implicated in similar structures in auditory and visual systems of others (Nawrot et al., 7). These regularities are often useful to representational models because they allow novel structures to be understood as instances of a more general strategy for solving certain types of problems that might be rediscovered, conserved, or adapted-from in the course of evolution or learned in the lifespan of the organism.

The third feature above emphasizes the fact that – as a contingent solution that might have not been arrived at – it should be possible to distinguish regularities in anatomy that reflect adaptation’s tendency toward conserving overall structures with regularities that reoccur because they afford the organism with information-processing capacities. In my discussion of TCC in chapter 2, we saw examples of how some of the most interesting examples of TCC-style explanations exemplify features of self-organization. If you recall, it can be illuminating to view some types of phenomena in these theoretical terms, and this often presents a challenge to being able to view such processes in decomposable, mechanistic parts.

Research on these types of systems has identified some key “signatures” of self-organization, such as the reoccurrence of “pink noise” or 1/f scaling at many levels of cognitive systems (Van

Orden et al. 2005). Similarly, mechanistic “archetypes” such as cross-fiber encoding, lateral

121 inhibition, or topographic organization function as similar markers of possible information- processing mechanisms, guiding research as described previously. But, in certain cases, they may allow researchers to attempt to distinguish between systems that warrant a representational treatment and those that do not. In a discussion of topographic organization as a recurring organizational principle in many animal nervous systems, the authors make note of the fact that there is evidence for the functional significance of this type of organization.

After all, they mention that “it may reflect a developmental accident. The mechanisms that guide axons from one structure to another structure during development may incidentally preserve the relative positions of those axons with respect to one another, thus preserving topographic organization (Patel et al., 22).” That is, the relative position of sensory cells in the retina may be preserved in afferent structures in the central nervous system without their spatial relationships contributing any functional significance. However, they point out that there is still reason to believe that this reoccurring phenomenon may have been selected for:

[There] are instances of topography that [an accident of development] cannot explain. For example, consider axons from the dorsal root ganglia that carry fine touch information from the periphery up to the brainstem. When these axons enter the dorsal columns, they do so dermatome by dermatome. Because dermatomes overlap, the representation of the body surface is not one-to-one. However, within the dorsal column they re-sort themselves back into a single continuous topographic representation (22).

This re-convergence of topographic organization may remind you of the dispersal, and then subsequent reorganization of different primary neuron into odorant-preference-specific groups in the insect glomeruli that has been seen in a few of our earlier examples. The fact that some sensory neurons lose their topographic relationship to neighboring cells on the sensory surface only to be resorted at a later stage – at the cost of energy, to name only one factor – suggests that this stereotyped pattern of neural architecture confers some functional advantage. The

122 rationale for why this unexpected re-convergence might occur is explicitly tied to an information-processing interpretation in light of anatomical and energy constraints. The authors note that many early sensory processes (notably visual and tactile ones) are thought to involve comparison of intensity or other properties of nearby cells in the retina or the skin, and that topographic mapping “[places] neurons with adjacent or overlapping receptive fields as closely together as possible [which will] minimize total axon length and thereby save space, metabolic resources and time (22).” Arguments such as these provide a picture of how a representational theoretical framework can help explain phenomena like the consumption of resources and energy for preserving certain structural regularities, or the apparent conservation and/or convergence of such organization by drawing focus to features of these systems that would be expected if the system were functioning to process/represent information under these specific anatomical and histological constraints. The functional utility of spatial proximity for making these types of comparisons, given the structure and behavior of the types of cells involved, explains patterns that would otherwise be surprising or mysterious by showing how such principles exploit spatial relationships between cells to extract useful information about matters of interest, such as boundaries or the foci of a stimulus, that are common to disparate kinds of sensory input.

The Umbrella-term and its Implications

While by no means exhaustive, the three features above, along with some overlapping explanatory emphases (encoding strategies, recurring structure types, etc.) help us understand the explanatory function of representation much more neatly than a theory of necessary and

123 sufficient conditions for what sorts of causal or law-like relations tie ‘internal’ state to external ones. Different research programs need not share any particular concept of representation, or even a well-developed concept of what it is “to represent”. Instead, “representation” seems to be an umbrella-term that unites various research programs by a family of shared assumptions about the relevant features of interest. The theories under this umbrella appear as a broadly mechanistic approach that implicates a variety of general computational and anatomic principles and theoretical frameworks, rather than any univocal body of work with a common theory of representation. But what does the characterization of representationalism as an umbrella-term mean for our principle subject – the would-be defender of cognitive representation? In Representation Reconsidered, Ramsey’s job-description challenge proposes that a satisfactory defense of representationalism would require an answer to questions like:

- Is there some explanatory benefit in describing an internal element of a physical or computational process in representational terms? - Is there an element of a proposed process or architecture that is functioning as a representation in a sufficiently robust or recognizable manner, and if so, how does it do this? - Given that theory X invokes internal representations in its account of process Y, are the internal states actually playing this role, and if so, how (34)?

All of these questions are geared toward finding a naturalistic answer to the question of what kind of role (or job) we are ascribing to a process when we claim that that process is representational. This might seem like a tall order when attempting to give an analysis of representation tout court – especially one that accords with commonsense or folk intuitions about representation in ordinary discourse and obtains of the disparate uses of representation in neuroscientific practice, as we’ve seen. This yields the impression that we may not have a clear concept of what we would need to observe empirically to say that we have found a

124 representation. However, in any individual case, if we were to substitute “representation” in the above questions with the particular mechanism being proposed in our earlier examples, the impression is reversed. We may not know what we’d have to see to be sure that we were dealing with a representation in general, but I would argue that researchers like Stopfer and his colleagues have a fairly good idea of the types of observations that would support or counter the claim that there are across-fiber, hierarchically organized vectors encoding information about odorant intensity and composition in the moth antennal lobe (Stopfer et al., 2003) or in other similar neural assemblages. They outline the relevant time-scale and types of patterns that they interpret as bearing what information, and suggest a process for how this information is made accessible to downstream mechanisms. There are many possible observations that would have direct import regarding the plausibility of this mechanism’s existence. Realism about such an entity, at this point, is a purely empirical matter. The representational mechanism at issue certainly involves a hypothesis about the causal structure and composition of a particular mechanism, and relies on an attending hypothesis about the reasons that this causal organization confers a benefit to the organism’s successful navigation of its environment, survival, and life-cycle. But this can be said of most mechanistic explanations (if not all). Explaining photosynthesis mechanistically would similarly depict a set of entities and relationships between them that produces a certain phenomenon and the particulars of the mechanism, so conceived, will have direct empirical implications about the sorts of observations that might count as evidence for or against that depiction’s reality and/or its biological function. We would not expect our realism about a purported photosynthetic mechanism to hinge upon the grounding of a satisfactory metaphysical analysis of

125 “metabolism,” though. In the practice of studying neural mechanisms, this requirement is no different. As one writer relates:

[in] spite of its extended use, we still lack a clear, universal and widely accepted view on what it means for a nervous system to represent something… [but the] lack of a theoretical foundation and definition of the notion has not hindered actual research… As with many other terms, such as circuit, area, process, system, or network, we need not define it every time we use it nor give a specific reference for the term to be able to use it properly. (Vilarroya, 1, 2017).

The metaphysical worries about the representation/content relation or theories on the general function of representation seem to have trouble meeting the Ramsey’s job-description challenge. But I believe that my revised job-description challenge shows that an appeal to individual representational accounts may be able to take up this burden. In the process of surveying the varying degrees that different “representational” accounts actually require this theoretical baggage, we can see that on one end of the spectrum lie models whose representational assumptions are a crucial source of their explanatory power, and serve to endow otherwise novel structures and mechanisms with intelligibility by integrating them within a broader framework of related principles of organization and function. On the other side of the spectrum we find cases that exemplify some of the shortcomings that Ramsey and

TCC’s representational skeptics have highlighted. It is true, as Ramsey argues and I have shown, that mere causal correlations between a stimulus and some neural activity are frequently dubbed “representations”, and that this can be misleading, as closer inspection reveals that the correlation is the only content of this designation. And it is true, as some have pleaded, that this can lend an illusion of comprehension to little-understood phenomena and can prejudice research and experimental design away from other equally motivated approaches or assumptions. One of the lessons from TCC’s successes should be that there are methodological

126 dangers involved in assuming the information-processing perspective without appropriate motivation, as it stifles the consideration of potentially creative and fruitful research programs, and applies unnecessary blinders to alternative ways organisms solve cognitive and perceptual problems. But, if I am right, we can distinguish between more or less motivated attributions of representational functionality even in relatively simple nervous systems and for behaviors that do not seem to be obvious candidates for nature’s favoring an information-processing solution.

This means that it may be true that this terminology is used carelessly, but it is not always used without the kinds of reasons that would be considered respectable in other fields of biological explanation. As I have tried to argue in the previous chapter, the status of individual representational mechanisms relies on the same kind of scientific virtues used to evaluate

“ordinary” mechanistic accounts. That is, the representational accounts worth defending really are just a subset of ordinary mechanistic accounts. In practice, there does not appear to be anything untoward about these types of attributions of information-processing. They explain by positing mechanisms that can transform information in a way that tends toward adaptive coordination of the organism in its environment, and often using models that rely importantly on this information-processing lens. Insofar as the representational umbrella carves up the organism into mechanisms and subcomponents in a way that is precluded by TCC-type interaction-dominant dynamics or other approaches, it is (by definition) offering a set of concepts and explanatory frameworks that will not be a part of TCC. But, in the absence of an argument for the impossibility of such a thing, mechanistic information-processing of the kind we have been discussing is conceivably one of the ways that organisms might have evolved to solve perceptual and cognitive problems, and this is true even if systems of tightly-coupled non-

127 mechanistically explicable interactions that span organism and environment turn out to be the most successful explanations of our cognition, perception, and action most of the time. The alternative to this proposition – that there is some argument demonstrating that one or the other of these approaches is necessarily misguided – is not likely to be forthcoming. This suggests that we allow multiple conceptual frameworks and traditions to coexist, compete, and draw mutual influence from one another. Confronted with this rather milquetoast pluralism, the would-be defender of representation can point out that the concepts and ontology of representational neuroscience – whose membership includes things as varied as spike-trains, lateral inhibition, activation vectors, neural networks, and topographic organization, and

Bayesian predictive coding – occupy central explanatory roles in some of our most successful and/or productive theories and models on the subject. For those inclined toward scientific naturalism, this is already a good reason to believe in them. Individual mechanisms within this framework, such as the molecular images in moths, are subject to empirical pressure from future experimentation and theory change; data might increasingly show that these structures do not behave in the way described, they might be radically reconceived in the context of a larger mechanistic economy, or alternative (maybe non-mechanistic or non-representational) programs may provide impressively successful explanations of their contribution to successfully navigating the world. More general principles, like topographic mapping, may continue to enjoy a place in neuroscience but be shown to be an artifact of development with no information- processing implications, or that that this is the case in most organisms but some subset of creatures have subsequently leveraged this accidental organization toward some functional advantage. Spatio-temporal vectors of the type discussed in Stopfer et al (2003) might have

128 been the result of statistical fudging and find little useful application elsewhere, or they may form the basis for a successful research program that has implications for understanding how and what information is being extracted and exploited by other systems in the same or other creatures. These are all empirical matters to be sorted out in the course of science. In light of all this, the fact remains that some of the best theories on offer sit under the umbrella of representation, and that approaches like the revised job-description challenge can show that not all of them use representational terminology in a way that is inappropriate or inert. These examples avail themselves of the above concepts and entities (and others) in constructing explanatory models of neural function and organization, and their representational assumptions are responsible for their explanatory power/scope, potential for unification, guidance of experimental design, and generally uncontroversial desired elements of a scientific program. There does not seem to be any obvious universally held “representational assumption” that is shared by all theories and models under this umbrella or that one might consider necessary for representational theorizing. Rather, these programs share models and concepts, historical and pedagogical lineages, and other common features that emerge in the production of science and scientists. If one wanted to make a stronger (and therefore more controversial and interesting) claim, it might be to say that in addition to these conceptual and social factors, there may be sufficient conditions for falling under the representational umbrella. One might be inclined to say something general like: “these approaches assume that information is being extracted and transformed by systems in the production of behavior”. But there is probably room for information transformation in directing and guiding behavior in non- representational approaches as well. One might want to add a clause that the approach also

129 includes parts that are amenable to decomposition into functional/mechanistic parts in a system; that the approach must be mechanistic. But I remain wary of trying to find any one assumption that is common to all and only representational views; seeking pre-empirical definitions that delimit the boundaries of representation is what prompted my critique of the traditional philosophical defenses of representation in the first place. The defender of representational cognitive neuroscience might be better off abandoning even the friendlier world of sufficient conditions. It is enough to state that there in fact exist important models whose explanatory power is not only invested essentially in claims about how and what information is being encoded and manipulated in particular mechanisms, and that these models play important theoretical roles in relating neural components at many levels of organization both within and across individuals and species, as well as in developmental, anatomical and evolutionary domains. If we think that mechanistic information-processing is an available solution to solving the problems facing an organism in navigating its way around the world, then the work falling under the representational umbrella is a worthy candidate in scientific terms. Facing criticism from a rival and, understandably, youthful and confrontational paradigm in the form of TCC, we saw that some have been tempted to defend representation on the grounds that representation is necessary for cognition or perception etc., and that any non-representational account of cognition would miss the explanatory mark by definition.

Others have attempted to show that rival accounts are still compatible with representation, or have tried to convince us that even explicitly non-representational approaches are still somehow representational, or that representation is safe if these rival processes can be re- described in representational terms. I think that all these arguments are wrongheaded. They

130 fail to appreciate a very interesting tension between two seemingly incompatible, and simultaneously scientifically promising, views of the major principles guiding perception, action, and cognition. More importantly, they fail to defend what is worth defending in the representational approaches, mischaracterize the shared theoretical factors and assumptions of many research programs under the guise of metaphysical definitions of “representation” or

“information”, and do not to pay due appreciation to the frustrations that many advocates of dynamical/TCC-style approaches have with the pervasive overuse of the term “representation,” and its inconsistent and casual application in neuroscience. When we look at the way that representational assumptions and their attending theoretical apparatus function in actual scientific practice, representational cognitive neuroscience presents itself as a family of bold mechanistic theories about function that have served important explanatory roles, some of which are certain to be overturned in the course of future developments. If representational models have anything to fear, it is the possibility of empirical inadequacy, overreach, or the overthrow by a much more successful program. They are not in any danger of abandonment for lacking a metaphysical foundation.

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