Vision

Introduction

A bird is twittering in a garden. The listener hears it, recognizes the bird, and knows that the sound is twittering. Yet something fundamental has come apart. In a clinical report discussed by Maurice Merleau-Ponty in Phenomenology of Perception, the bird and its song seem separated by an abyss: both remain available to recognition, but they no longer belong together in the ordinary experience of a bird singing. Another reported disturbance concerns a clock. Its hands move, and its mechanism can be identified, yet their connection no longer presents itself as the clock’s working. What has failed is not simply the reception of information. The elements remain, but the coherence through which they constitute an event has been disturbed.

Merleau-Ponty considers these reports alongside alterations of bodily scale, spatial distance, and movement. Their importance lies in the distinction they disclose between knowing facts about a situation and inhabiting a perceptual world. A person may know that food and body occupy the same space while experiencing their relation as profoundly dislocated. Vision, accordingly, cannot be adequately described as the delivery of separate visual items to an observer who subsequently supplies their connections through explicit judgment. The body already participates in organizing a field of significance: near and far, reachable and unreachable, approaching and receding. Merleau-Ponty’s contribution to the study of perception is to make this embodied organization a problem in its own right. He reads perceptual disturbance not merely as the loss of a component, but as a disclosure of relations that ordinary experience usually allows us to take for granted.

The passage also places this organization within time. The body’s capacity to bring things together belongs to its orientation toward what can happen next. When that orientation collapses, the world can remain populated while losing its movement and practical coherence. This does not mean that every perceptual disorder has one cause. It means that the case directs attention beneath the inventory of identifiable objects toward the conditions under which objects, movements, and possibilities belong together. Before asking how much information a perceiver receives, we must ask how information participates in a meaningful situation.

A different discipline encounters a related organizational question when it asks how an artificial network learns. Such a network transforms inputs through interconnected computational units. Connections have adjustable strengths, called weights; intermediate layers produce activities that serve as representations for subsequent processing. During training, a loss function measures how far the output departs from a specified objective. But knowing that the output is wrong does not tell us immediately how each earlier connection should change. This is the credit-assignment problem. Backpropagation computes how sensitive the loss is to each weight by applying the chain rule backward through the network’s operations. An optimizer then uses these derivatives to adjust the weights. In their influential 1986 paper, David Rumelhart, Geoffrey Hinton, and Ronald Williams demonstrated how this procedure could train intermediate units to represent useful features rather than requiring those features to be specified individually beforehand. “Learning Representations by Back-Propagating Errors.”

The meeting with Merleau-Ponty occurs at the question of organization, not at an identity between conscious perception and numerical computation. Backpropagation is neither a description of lived experience nor an established account of how the brain learns. It is a mathematical procedure through which an overall objective can influence the formation of intermediate representations. Merleau-Ponty’s cases help us appreciate why the distinction matters: the presence of identifiable elements does not explain their perceptual unity. Conversely, the success of a computational representation does not by itself establish that anything is experienced. The philosophical and technical inquiries can illuminate each other precisely when we preserve the difference between what a system computes and how a world appears.

The five papers examined here pursue different aspects of the organization of learning. The first, approached through “Learning,” investigates how temporal persistence can introduce delays and how prospective input can improve responsiveness. The second, “Chunks,” examines whether coordinating the boundaries between groups of layers before refining their interiors improves learning. The third, “Trees,” reorganizes dependencies among locally trained blocks so that learning need not proceed through one uninterrupted sequence. Together, they ask when corrective information becomes effective, at what scale activities should be coordinated, and through what structure that coordination can occur. Their proposals retain different relations to backpropagation: some modify its operating conditions, while others replace global propagation with more local procedures. Prospective coding, boundary-first inference, and hierarchical block-local learning.

The remaining papers open onto differentiation and visual intelligibility. “Parmenides” names our question about how a distinction between what is and what is not becomes operational; the corresponding paper’s narrower problem is how paired nonnegative activities can carry signed learning signals under biological constraints. “Phenomenology” names our inquiry into consolidating vision; its corresponding paper investigates how contrastive local learning can produce more interpretable, hierarchically organized features from visual representations. Neither historical term is offered as the authors’ declared method. Each provides an entrance through which the mechanism becomes accessible while retaining its particular assumptions and limits. Learning under Dale’s constraint and group-contrastive Forward–Forward learning.

We begin, then, with the bird whose song no longer belongs to it. Not because a learning algorithm explains that clinical experience, but because the case makes the stakes of organization unmistakable. Elements can remain available while the relations that make them consequential fail. The following inquiry asks how contemporary learning systems establish, transmit, and revise such relations—and how the history of thought can help us formulate those questions without substituting philosophical language for technical explanation.

Five Papers

1

Let’s begin with “Learning” and develop the continuous essay from there.

To learn is not merely to preserve what has occurred. It is to become differently capable of meeting what occurs next. A record may retain every event without acquiring any greater capacity to respond; conversely, a learner may forget many particulars while becoming more capable of recognizing a situation, following a movement, or correcting a mistake. The distinction matters because memory and learning, although closely related, do not name the same accomplishment. Memory carries something forward. Learning changes what carrying it forward makes possible.

Consider learning a melody. The note sounding now must be heard in relation to those that preceded it, but those earlier notes cannot occupy attention so completely that the next one arrives before we are ready. Nor can correction occur at just any time with equal usefulness. If we recognize a mistake only after losing track of the movement that produced it, knowing that something went wrong may tell us very little about what to change. Learning therefore involves more than receiving information and storing its consequences. It requires a coordination between what persists, what changes, and when correction can become effective.

This is an accessible entrance into the problem examined by Rawat and colleagues. In their recurrent networks, an internal state retains the influence of previous input. That persistence provides temporal context, but it also means the system does not respond instantaneously to a changing signal. When several such layers are stacked, delays accumulate. A teaching signal arriving downstream can consequently be poorly coordinated with the earlier activity it needs to modify. The machinery that makes memory possible thus introduces a difficulty for learning: persistence can become lag. “Prospective Coding Improves Learning in Deep Continuous-Time Recurrent Networks.”⁠

Their intervention is to use not only the signal’s current value but also its rate of change. In ordinary language, the system responds to where the signal is and how it is moving. This provides a locally calculated look-ahead that compensates for a particular filtering delay; it does not receive information from the future. The authors report improved performance in their tested recurrent systems and identify a specific attenuation mechanism that this adjustment removes under spatial-only backpropagation. The distinction is important: the method changes the conditions under which corrective information remains effective, rather than dispensing with correction or solving every difficulty of learning. Original paper, introduction and discussion.⁠

Here the technical specialization opens onto a larger question we can pursue: when does retaining the past assist responsiveness, and when does it obstruct it? This is our philosophical question, not a conclusion established by the experiment. Its value is that it lets us understand the engineering problem without stripping away its precision. “Memory” ceases to be an unquestioned good measured only by how much can be retained. We begin instead to ask about the organization of retention—whether what persists remains available for adjustment, and whether a system can carry its history without responding perpetually too late.

On this reading, learning concerns the timing of transformation. A correction must not only exist; it must reach an activity in a form and at a moment through which that activity can change. The paper gives us one concrete mechanism for improving that relation. Its broader interest lies in making a familiar difficulty newly exact: carrying more of the past does not by itself make us better prepared for what comes next. What matters is how the past participates in a responsiveness that remains capable of alteration.

2

Learning also requires that what changes be organized into something we can work on. We rarely understand a difficult sentence by treating every word as an independent problem. We distinguish phrases, recognize which clauses depend on others, and establish provisional relations among them. Only then can we return to a particular word and appreciate what it contributes. A chunk, in this ordinary sense, is a grouping that allows several elements to be handled together without requiring us to resolve each one separately at every moment. Its usefulness depends not only on what it contains, but on how it connects to what lies outside it.

The second paper gives this organizational question a specific computational form. In a predictive-coding network, hidden states are internal activities situated between input and output. During inference, these activities are adjusted to reduce discrepancies between predictions and the states they predict. Adjusting hidden activities is distinct from changing the connections through which the network learns, although the two processes are related. The authors investigate whether changing the order of those internal adjustments can improve learning. Their chunks are groups of network layers—not concepts, memories, or passages of language. “A Study of Hidden-State Optimization Order in Predictive Coding Networks.”⁠

The proposed procedure begins at the boundaries between these groups. It first coordinates the activities through which one chunk relates to another, then refines the activities within each chunk. Imagine collaborating on an essay: before polishing every paragraph, we establish what each section receives from the preceding section and what it must make available to the next. This does not settle every detail in advance. It gives local work a provisional orientation. Similarly, the network’s boundary-first schedule supplies a way of coordinating internal adjustments before undertaking their finer elaboration. The authors report improved accuracy and evidence of stronger early-layer feature learning in the predictive-coding systems they tested. Original paper, methods and results.⁠

This is where chunks open onto the historical problem of parts and wholes. We need some grasp of a whole to understand its parts, yet our understanding of that whole develops through the parts themselves. There need be no final choice between beginning with absolutely independent elements and possessing the completed whole in advance. A provisional organization can guide differentiation while remaining open to correction. Plato’s collection and division provides one historical approach to this problem: gathering a multiplicity into an intelligible unity and distinguishing articulations within it. The philosophical concern is not simply whether we divide, but whether our divisions help us understand what we are examining. Plato, ⁠Phaedrus⁠, 265d–266b.⁠

The computational result lets us sharpen that concern. A grouping does not become useful merely because its members have been placed together; its relations with other groupings must support the work occurring inside it. Nevertheless, the paper does not demonstrate that the network discovers natural conceptual divisions. The researchers supply the grouping scheme, and the experiments concern how hidden states are optimized within that arrangement. What the results suggest is narrower and more interesting than a universal prescription to “start with the whole”: the organization and sequence of local adjustment can materially affect whether useful representations develop. Original paper, conclusion.⁠

For our inquiry, the chunk therefore names a provisional scale of formation. It makes a portion of a larger process available for coordinated work, without making that portion self-sufficient. Its boundary is not merely where its contents stop; it is also where their relation to other contents becomes consequential. If the first paper asked when corrective information can effectively arrive, this paper asks how a system can be organized so that local correction has somewhere productive to act. Learning depends not only on the availability of correction, but on the relations that allow one adjustment to matter beyond itself.

3

Once several elements can be worked on together as a chunk, another question arises: how should these groups communicate? An arrangement can contain the same components yet impose very different conditions on their cooperation. If each group must wait for the preceding group to finish, the work proceeds as a chain. If groups can work concurrently and coordinate their results through successive levels, the arrangement branches. The tree names this second possibility: a structure in which local operations participate in a larger process without every operation occupying a single queue.

This is the problem addressed by “Breaking Chains with Trees.” Standard end-to-end backpropagation computes how changes in a network’s parameters would affect its final error. To do so, it passes corrective information backward through successive layers. These dependencies create what researchers call update locking: a layer must wait for the downstream calculation needed to determine its update. The issue is not that backpropagation cannot use parallel computation—it can—but that parallel work within layers does not remove the sequential dependencies between them. “Breaking Chains with Trees: Deep Learning with O(log N) Parallel Time Complexity.”⁠

The authors propose Hierarchical Block-Local Learning, which organizes blocks through a branching structure and trains them using local objectives derived from a shared probabilistic formulation. Local work is therefore not independent guesswork: the objectives coordinate how intermediate representations should relate. The logarithmic claim concerns the depth of the ideal parallel computation. A balanced binary tree connecting sixteen endpoints has four branching levels; doubling the endpoints adds another level rather than doubling the path. This illustrates the structural advantage, not the architecture’s complete implementation. The paper’s claim does not mean that total computation or actual training time necessarily falls in the same proportion, and gradient calculations remain within the local learning procedure. Original paper, introduction and theoretical formulation.⁠

The historical connection becomes accessible through the difference between an inventory and an articulated inquiry. An inventory places things beside one another. A branching inquiry establishes relations through which one distinction makes further distinctions possible. Plato’s collection and division offers a way into this difference: gathering dispersed cases under a common account, then distinguishing their kinds without cutting arbitrarily. Such a procedure does more than store conclusions. It organizes the work by which something becomes intelligible. We need not identify Plato’s dialectic with this algorithm to recognize a shared question about how the arrangement of distinctions enables thought to proceed. Plato, ⁠Phaedrus⁠, 265d–266b.⁠

Yet a computational tree is not necessarily a tree of meanings. Its higher nodes need not represent more universal concepts, nor its lower nodes more particular objects. Here, branching primarily organizes dependencies in learning. Keeping this distinction intact allows a more precise philosophical reading: hierarchy can coordinate activity without assigning superior truth or value to whatever occupies its upper levels. A higher position may designate a wider coordination task, not a more perfect being. The tree helps us separate organizational level from rank—two notions that are easily confused when technical arrangements become metaphors for knowledge or society.

For our inquiry, then, trees concern the routes through which local formation becomes jointly effective. Chunks make groups available for work; trees organize how that work can proceed, meet, and influence other work. The significant possibility is neither complete central control nor the absence of coordination. It is an arrangement in which dependence is structured so that several processes can develop together. We can consequently ask not only what a learning system contains, but what its arrangement obliges each part to wait for—and whether those obligations are necessary to what it is trying to learn.

4

Parmenides enters here through the distinction itself: what is, and what is not. Before asking how a system learns from a difference, we must ask how that difference becomes available to it. There is a distinction between something failing to occur and a system registering that it has not occurred. Silence alone does not tell us whether an expected sound is missing, whether nothing was expected, or whether the listener has ceased to listen. For an absence to become informative, some organization must make it distinguishable.

The fourth paper gives us a specific way into this problem. Its on–off architecture uses paired nonnegative channels to represent signed activities and learning signals. The “off” channel is therefore not simply a neuron that has stopped firing: it can carry activity representing the negative side of a quantity. Both channels belong to the system’s actual operations. Their coordinated influence allows updates in opposing directions while respecting constraints on firing rates and excitatory or inhibitory identity. Under symmetric forward and feedback connections, the proposed local update recovers the backpropagation update. The immediate technical problem is signed computation, rather than a general classification of existence and nonexistence. But your reference identifies a philosophical question within that problem: how can what is not be distinguished through something that is? “Learning in Deep Networks under Dale’s Constraint.”⁠

In Parmenides, the distinction between being and non-being governs what inquiry can legitimately think and say. Fragment 8 refuses to let what-is originate from what-is-not, or to let nothingness provide a gap dividing being from itself. Non-being cannot simply be introduced as another available thing whenever an explanation requires it. This gives our reading its pressure: if we invoke an absence to explain an effect, what exactly have we invoked? Have we merely named what is missing, or have we accounted for the existing relations through which its being missing makes a difference? Parmenides, fragment 8.⁠

Consider an indicator that lights when a required component is missing. The component is absent; the illuminated indicator is present. These are not contradictory descriptions, because they concern different things. The indicator does not bring the component into existence. It makes the component’s absence available to a process that can respond. This is an illustration of the distinction we are pursuing, not a literal description of the paper’s circuit. It helps us separate the absence of something from the positive activity through which that absence is registered.

Your axis consequently takes us beyond the observation that positive quantities can encode a negative number. It asks us to distinguish what a process is from what it indicates. An existing activity can indicate that a condition does not hold. Conversely, the mere lack of activity does not automatically amount to a meaningful judgment that something is not. Meaningful differentiation requires an arrangement: what is being tested, which alternatives count, and how the result can affect subsequent activity.

Here is the connection to operative absence, understood as our philosophical development rather than the paper’s conclusion. We need not turn what is absent into a hidden substance to account for its consequences. We can investigate the expectations, dependencies, and responses through which its absence becomes consequential. Parmenides supplies the demand not to treat nothing as an explanatory thing; the paper supplies a concrete case in which an oppositional function is accomplished through actual organized activity. Between them lies the question you have identified: how does a distinction between what is and what is not become something a system can act upon?

5

“Consolidating vision” gives us a more precise entrance than interpretability alone. We do not ordinarily see a collection of unrelated features and then consciously assemble them into a recognizable thing. We see something: a face, an animal, a doorway. The philosophical question concerns this coherence—how differences in what appears can belong together without becoming indistinguishable. Phenomenology is central to the reading you are proposing because it asks about that achievement of recognizable unity, rather than treating recognition as an unexplained starting point.

The final paper approaches a computational version of this problem. Its group-contrastive Forward–Forward method trains a module on pretrained visual representations, using pairs from the same coarse category as positive examples and pairs from different categories as negative examples. Class-specific routing organizes neurons into groups. The authors report that increasingly abstract, comparatively single-concept-selective features develop across layers without an imposed sparsity constraint. “Consolidation,” in our vocabulary, would name the formation of more coherent and differentiated selectivity: varied examples elicit activity that becomes intelligible in terms of shared features. But this organization is partly prepared by labels and routing; it is not the unrestricted emergence of meaning from raw vision. “Emergent Hierarchical Monosemantic Neurons from the Group-Contrastive Forward-Forward Algorithm.”⁠

Husserl provides a way to deepen the question. When we walk around a table, the visible profile changes, but we perceive the same table. Its unity does not require all its sides to be simultaneously visible, nor does each new view ordinarily present an unrelated object. Perception holds together what appears now with other possible appearances. This is the work of synthesis, which need not be a deliberate judgment. Yet we should distinguish recognizing one object across changing views from recognizing a shared kind across different objects. The paper’s grouping of examples bears more directly on the latter. Phenomenology helps us articulate that difference rather than allowing “recognition” to conceal several distinct accomplishments. Husserl on perception, synthesis, and embodiment.⁠

Gibson’s The Ecological Approach to Visual Perception belongs in this discussion, but as a productive challenge as much as an ally. For Gibson, natural vision involves an observer who moves, looks around, and explores an environment. Changes in the available light are not merely disturbances that an internal system must overcome; their structure supplies information about surfaces, layout, and the observer’s movement. Perceptual learning involves becoming attuned to this information. Where an internal-representation account might ask how disparate inputs are assembled into coherence, Gibson asks whether the environment already provides structured information that an active perceptual system can detect. Gibson, ⁠The Ecological Approach to Visual Perception⁠.⁠

Take a doorway. A computational classifier might identify features associated with doorways across many pictures. A phenomenological inquiry can examine how this doorway remains recognizable through changing appearances and anticipated views. An ecological inquiry asks how its opening is perceived in relation to the observer’s body and possibilities of passage. These are related questions, but they do not describe the same achievement. Consolidating vision might mean identifying a category, maintaining an object’s identity, or perceiving an opportunity for action. Your term opens the field precisely because it lets us ask which coherence is being accomplished.

This also brings a second act of seeing into view: the researcher’s. The paper seeks to make network activity legible as responding to particular concepts. We therefore confront both the organization of visual representations and the organization of our understanding of those representations. Calling a neuron “monosemantic” is a claim about the consistency and specificity of its selectivity, not evidence that it experiences the object it helps classify. Our phenomenological reading can examine this passage into intelligibility without equating feature selectivity with consciousness.

So phenomenology can be the organizing question of our treatment without being the paper’s declared method. “Consolidating vision” asks how multiplicity becomes coherently available as something recognizable. Gibson keeps that question open toward the moving observer and the environment; the paper makes one particular form of representational organization experimentally examinable. The meeting ground is not that all three offer the same explanation. It is that each helps us distinguish what it would actually mean for vision to become coherent.