Spike-Timing-Dependent Plasticity

one_liner: A synaptic learning rule where the sign of a weight change depends on the millisecond order of pre- and postsynaptic spikes — pre-before-post potentiates, post-before-pre depresses — turning a coincidence detector into a causality detector that grows directed connectivity.

origin_domain: neuroscience

core_idea: Spike-timing-dependent plasticity (STDP) is the synaptic learning rule in which the direction and magnitude of long-lasting change in synaptic efficacy are determined by the relative timing — on a millisecond scale — of presynaptic and postsynaptic action potentials: when the presynaptic spike precedes the postsynaptic spike within a window of roughly 10–40 milliseconds, the synapse is potentiated (LTP); when the order is reversed — postsynaptic before presynaptic — the synapse is depressed (LTD); and the magnitude of the change decays as the absolute timing interval grows, producing a characteristic asymmetric biphasic STDP curve. The rule was systematically demonstrated in cortical and hippocampal preparations by Guo-Qiang Bi and Mu-Ming Poo (1998) and in developing cortex by Henry Markram and colleagues (1997). The biophysical mechanism is NMDA-receptor coincidence detection: the NMDA receptor requires both presynaptic glutamate binding and postsynaptic membrane depolarisation to admit calcium; the pre-then-post order reliably satisfies both conditions within the millisecond window, producing the calcium influx that recruits CaMKII and triggers LTP-related downstream cascades; the post-then-pre order produces a weaker, differently-timed calcium signal that activates phosphatases and drives LTD. The consequence at the network level is that STDP is a causality detector: it selectively strengthens synapses from neurons that could plausibly have caused the postsynaptic firing (presynaptic spike precedes postsynaptic spike) and weakens those from neurons that could not (postsynaptic spike precedes the presynaptic one), so a network with STDP develops directed connectivity that tracks the causal-temporal structure of its input statistics rather than the mere co-occurrence structure that symmetric Hebbian learning would capture.

scope_of_application:
STDP lives across the synaptic-plasticity subfields of neuroscience and extends intact to the engineered spiking and neuromorphic substrates built to host it; its reach is bounded by the presence of spikes whose order carries information and a coincidence-detecting mechanism to read it — one abstraction layer up, the temporal-credit-assignment pattern it instantiates travels under that parent, but the calcium-and-spike rule itself does not.

- **Hippocampal LTP/LTD** — the classical Bi & Poo (1998) demonstrations of order-sensitive potentiation and depression at Schaffer-collateral synapses.
- **Cortical synaptic plasticity** — the rule measured across species and developmental stages, including the developing-cortex findings of Markram and colleagues (1997).
- **Cerebellar plasticity** — climbing-fibre-driven LTD sharing the temporal-order sensitivity that defines the rule.
- **Computational neuroscience** — STDP as core machinery in spiking-neural-network models of learning, perception, and motor adaptation.
- **Neuromorphic hardware** — the canonical local learning rule on chips such as Loihi, TrueNorth, and BrainScaleS, prized for its locality, low memory footprint, and unsupervised character; here it is genuinely the same rule on a different physical substrate, the spike, window, and sign-asymmetric update all literally present.

structural_signature:
**Sig role-phrases:**

- **the synapse-connected pair** — a presynaptic and a postsynaptic neuron joined by a synapse with an existing weight
- **the millisecond-resolved spike times** — pre- and postsynaptic action potentials timed at fine resolution, yielding a signed interval Δt
- **the NMDA coincidence detector** — the receptor admitting calcium only when presynaptic glutamate binding and postsynaptic depolarisation coincide, implementing the timing window biophysically
- **the locality constraint** — only the two endpoint spikes and the current weight enter the update; no global controller
- **the sign-asymmetric update** — pre-before-post (within ~10–40 ms) potentiates via strong calcium → CaMKII (LTP); post-before-pre depresses via weaker calcium → phosphatases (LTD)
- **the biphasic STDP curve** — weight change as one function of Δt, two opposite-sign lobes decaying as |Δt| grows
- **the causality-detector outcome** — at network scale, crediting plausible causal predecessors and debiting non-predecessors, growing *directed* connectivity that tracks causal-temporal (not mere co-occurrence) structure

what_it_is_not:
- **Not plain "fire together, wire together."** STDP refines Hebbian learning by making the *sign* of the weight change depend on the millisecond *order* of pre- and postsynaptic spikes — pre-before-post potentiates, post-before-pre depresses. Symmetric Hebbian rules treat co-activation without regard to order and cannot tell whether the presynaptic neuron led or followed; STDP can, which is the whole point.
- **Not a coincidence detector.** What it reads is temporal order, not mere overlap. Two cells firing "together" is not enough: the asymmetric biphasic curve credits inputs that *preceded* the output and debits those that *followed* it. Treating STDP as detecting simultaneity collapses the order-asymmetry that turns it into a causality detector.
- **Not a bare phenomenology.** The sign rule is grounded in a specific mechanism — NMDA-receptor coincidence detection, where the pre-then-post order admits the strong calcium influx that recruits CaMKII for LTP, and the reverse order gives a weaker, differently-timed calcium signal that drives LTD. The same machinery yields opposite outcomes by order; the curve is a prediction about biophysics, not a curve fit.
- **Not performing causal inference.** STDP exploits a causality-like asymmetry but does not reason about causes; it merely makes synaptic weights *track* the causal-temporal statistics of its inputs. The network ends up wired to reflect who plausibly drove whom, but no inference is computed — the arrow of time is written into weights by a local update, not inferred by an agent.
- **Not a globally coordinated rule.** The update is strictly local: only the two endpoint spikes and the existing weight enter it, with no global controller surveying the network. The directed connectivity a population grows is an *emergent* consequence of applying one curve per synapse over input statistics, not the product of any centralised allocation of credit.

clarity:
STDP's clarifying force is to replace the slogan "neurons that fire together wire together" with a precise, order-sensitive rule — and in doing so to convert a coincidence detector into a causality detector. Plain Hebbian learning treats co-activation symmetrically: it cannot tell whether the presynaptic neuron led or followed. By making the *sign* of the weight change depend on the millisecond order of pre- and postsynaptic spikes, STDP makes legible that timing, not mere overlap, carries the information — pre-before-post strengthens (the input could have helped cause the output), post-before-pre weakens (it could not). The sharper question a synaptic physiologist can now ask is not "did these two cells fire together?" but "in what order, within what window, and therefore is this connection being credited or debited?"

The deeper distinction it sharpens is between *co-occurrence structure* and *causal-temporal structure* in a network's wiring. Because the rule is order-asymmetric, a population governed by STDP develops *directed* connectivity that tracks which neurons plausibly drive which others, whereas symmetric Hebbian learning could only carve out undirected co-activation clusters. That reframing — a learning rule reading the arrow of time off spike order and writing it into the connectome — is what links the millisecond biophysics of NMDA-receptor coincidence detection to a network-level claim about how causal chains get reinforced over mere correlations, and it is what keeps STDP analytically separate from the broad family of timing-insensitive plasticity rules it refines.

manages_complexity:
Two problems that look sprawling collapse under STDP onto one compact rule. The first is the heterogeneity of synaptic change itself — the seemingly separate phenomena of potentiation here and depression there, induced under varied stimulation protocols across cortical and hippocampal preparations. The second is the network-scale mystery of how a population's connectivity comes to reflect anything about its inputs. STDP tames both by reducing the weight change at any synapse to a *single function of a single variable*: the signed timing interval Δt between presynaptic and postsynaptic spikes, read off the asymmetric biphasic curve. Potentiation and depression stop being two phenomena and become the two lobes of one curve; the whole induction story reduces to "where on the Δt axis did this spike pair land." Because the rule is local — only the two endpoint spikes and the existing weight matter — the entire emergent behaviour of an arbitrarily large network follows from applying that one curve per synapse over the input statistics, with no global controller to track.

What the analyst then reads off operates at two levels from the same rule. At the synapse, the sign of Δt is a credit/debit verdict: pre-before-post (within roughly 10–40 ms) credits the connection — the input could have helped cause the output — and post-before-pre debits it, so "is this connection strengthening or weakening, and by how much" is read directly from spike order and interval rather than re-derived from the biochemistry. At the network, the rule's order-asymmetry licenses a sharp qualitative prediction that symmetric Hebbian learning cannot make: a population under STDP develops *directed* connectivity tracking the causal-temporal structure of its inputs (which neurons plausibly drive which), whereas a timing-insensitive rule could only carve undirected co-occurrence clusters. So the analyst predicts the *kind* of wiring a network will grow — causal chains selectively reinforced over mere correlations — by tracking one property of the rule, its order-asymmetry, instead of simulating the molecular detail of every connection. The move is from a scatter of plasticity protocols plus an open question about emergent wiring to a single biphasic curve over Δt whose sign and magnitude the analyst reads to settle per-synapse credit and, at scale, to read the arrow of time off spike order and predict directed connectivity — collapsing a high-dimensional cellular-and-network problem onto one local rule with one input variable.

abstract_reasoning:
STDP's signature inference reads *causality off temporal order*, converting a coincidence detector into a causality detector. At a single synapse the analyst reasons from the sign of the timing interval Δt to a credit-or-debit verdict on the connection: a presynaptic spike preceding the postsynaptic one (within roughly 10–40 ms) means the input *could have helped cause* the output, so the synapse is credited and potentiated; the reverse order means the input *could not* have caused the output, so it is debited and depressed. The magnitude follows the asymmetric biphasic curve, decaying as |Δt| grows. The move is to treat spike order as evidence about who drove whom, and to write that evidence into the weight — so the question shifts from "did these two cells fire together?" to "in what order, within what window, and therefore is this connection being credited or debited?" This is the inference that distinguishes STDP from timing-insensitive Hebbian rules, which treat co-activation symmetrically and cannot tell whether the presynaptic neuron led or followed.

The mechanistic inference grounds the sign rule in coincidence detection and lets the analyst reason from biophysics to outcome. The NMDA receptor admits calcium only when presynaptic glutamate binding and postsynaptic depolarisation coincide, so the analyst infers that the pre-then-post order reliably satisfies both conditions within the millisecond window, producing the strong calcium influx that recruits CaMKII and triggers LTP, while the post-then-pre order produces a weaker, differently-timed calcium signal that activates phosphatases and drives LTD. The reasoning runs from a molecular coincidence requirement to the *direction* of the weight change — same machinery, opposite outcomes depending on order — which is what makes the sign rule a prediction about a specific mechanism rather than a bare phenomenology.

The most distinctive move operates at the network level: predict the *kind* of connectivity a population will grow by tracking one property of the rule, its order-asymmetry. Because the local sign rule credits causal predecessors and debits non-predecessors, the analyst infers that a population under STDP develops *directed* connectivity tracking the causal-temporal structure of its inputs — which neurons plausibly drive which — and selectively reinforces causal chains over mere correlations. This is a qualitative prediction a symmetric Hebbian rule cannot make: timing-insensitive learning could only carve *undirected* co-occurrence clusters. So the analyst reasons forward from "this network uses STDP" to "it will read the arrow of time off spike order and write it into the connectome," and predicts, for instance, that an upstream neuron that consistently leads a downstream one will become a progressively more reliable trigger of it, while a connection whose order is reversed by other inputs will fade. Crucially, because the rule is *local* — only the two endpoint spikes and the existing weight matter — this network-scale behaviour follows from applying one curve per synapse over the input statistics, with no global controller to track.

The boundary on all these inferences is the spike-timing substrate they presuppose. The credit/debit verdict, the calcium-mechanism reasoning, and the directed-connectivity prediction depend on discrete pre- and postsynaptic action potentials with millisecond-resolved timing, a narrow asymmetric window, sign-asymmetric update, and the NMDA-receptor coincidence biophysics that implements the window. The analyst draws a sharp distinction the rule is built to honour — *causal-temporal* structure versus mere *co-occurrence* structure — and the moves have force precisely where there are spikes whose order carries information and a calcium-dependent mechanism to read it; it is that fine-timing, locally-computed, order-asymmetric machinery, not a general notion of order-mattering, that makes the causality-detection and directed-wiring inferences load-bearing.

knowledge_transfer:
Within neuroscience STDP transfers *as mechanism*, and the transfer is exact because the rule is local and biophysically specified. The same signed-Δt biphasic curve, the same NMDA-receptor coincidence detection, and the same causality-detector reading carry across **hippocampal LTP/LTD** (the classical Bi & Poo 1998 findings), **cortical synaptic plasticity** (measured across species and developmental stages), and **cerebellar plasticity** (climbing-fibre-driven LTD sharing the temporal-order sensitivity). What carries is the working content: the per-synapse credit/debit verdict (read the sign of Δt — pre-before-post credits, post-before-pre debits), the mechanistic inference (same calcium machinery, opposite outcomes by order), and the network-scale prediction (a population under STDP grows *directed* connectivity tracking causal-temporal structure, where symmetric Hebbian learning could only carve undirected co-occurrence clusters). Notably, the transfer extends *intact to an engineered substrate that was built to host it*: STDP is the canonical local learning rule in spiking neural networks and on neuromorphic hardware (Loihi, TrueNorth, BrainScaleS), prized for its locality, low memory footprint, and unsupervised character — here it is genuinely the same rule, a different physical implementation, not an analogy, because the spike, the timing window, and the sign-asymmetric update are all literally present.

Beyond those spike-bearing substrates the honest reading is mixed, and worth stating precisely. There is a real **shared abstract mechanism** (case B): the substrate-general residue STDP instantiates is *causal-order-dependent credit assignment* — order matters, the earlier event credits the later one, and the sign of the update respects the arrow of time — and that pattern genuinely recurs as co-instances across distinct learning substrates: temporal-difference learning (credit assigned backward from rewards to predecessor states), eligibility traces in reinforcement learning (a temporal window over which a state stays eligible for credit), and backward induction in decision theory. These are not metaphors; they are independent solutions to the same temporal-credit-assignment problem, which is why STDP and TD learning are best related as siblings under that parent rather than one borrowing the other's name. But the cross-domain lesson should carry *that parent* — temporal-order-sensitive credit assignment (a composition of `temporal_dynamics`, `credit_assignment`, and `causality`) — not "STDP," because the STDP-specific cargo stays home: the millisecond window, the sign-asymmetric biphasic curve, the calcium influx, and the NMDA coincidence biophysics have no counterpart outside neural-or-neuromorphic substrates. And where the reach goes further still — "early collaborators got more credit for the project than late joiners," touchpoints preceding a marketing conversion credited while later ones are not — it lapses into **analogy** (case A): the temporal-order *shape* is borrowed while the narrow window, the biphasic sign rule, and the local-only-information constraint are all absent, so only the silhouette of the rule travels. The disciplined statement is therefore: STDP refines `hebbian_learning` by adding order-asymmetry and ports exactly to spiking and neuromorphic substrates; one abstraction layer up, the temporal-credit-assignment pattern it instantiates travels literally as a co-instance to other learning systems; and the named neural rule itself, with its calcium-and-spike machinery, does not.


Split-Brain Problem

one_liner: The distributed-systems failure where a network partition splits a replicated cluster into subsets that each elect themselves as write authority and accept divergent writes; guard against it by enforcing that at most one subset — the quorum majority — may write.

status: draft

core_idea:
The split-brain problem is the distributed-systems failure mode in which a network partition severs a replicated cluster into two or more subsets, each of which independently elects itself as the authoritative write-accepting node, and both proceed to accept divergent writes — so that when the partition heals, the system holds two incompatible histories with no ground truth to resolve them.

The structural logic that makes this dangerous is a forced trade-off. When a node in a replicated cluster loses contact with the rest, it faces an inescapable binary: either it refuses to accept writes (preserving safety — single coherent state — at the cost of availability) or it continues to accept writes (maintaining availability at the cost of potentially becoming a second, illegitimate primary). A split brain occurs when both halves independently choose the second option, each believing the other to be unreachable or failed. The result is two primaries committing writes to the same nominal dataset simultaneously. When the partition heals, the divergent write logs cannot be automatically reconciled without data loss, rollback, or manual intervention; one or both sides must be discarded or merged by a process that no protocol can guarantee is safe.

This failure mode is the empirical motivation behind the CAP theorem (Brewer 2000; Gilbert and Lynch 2002): under partition, a distributed system cannot simultaneously guarantee consistency and availability. Virtually all sound replication designs address it through one of four mechanisms. Quorum-based systems require a majority of voting replicas to authorize any write, guaranteeing that at most one partition can ever constitute a majority; minority partitions become read-only or refuse service entirely — the structural reason odd-numbered cluster sizes are preferred and two-node clusters require an external arbiter or witness. Fencing (also called STONITH, "Shoot The Other Node In The Head") forcibly terminates or evicts the losing primary before the winning primary begins committing, eliminating the window in which both are simultaneously active. Full consensus protocols — Paxos, Raft, Zab — are designed from first principles around the invariant that no two leaders can commit divergent writes for the same log position at the same epoch. Blockchain networks handle the Byzantine variant of the same problem via the longest-chain rule: competing forks coexist until one outgrows the other, at which point the shorter fork is discarded.

scope_of_application:
The split-brain problem lives across the replication and consensus subfields of distributed systems; its reach is within that domain, wherever the configuration recurs — replicated state, a partitionable network, leader election, authoritative writes. (The neuroscience homonym shares only the word, and organizational-schism analogues belong to the consensus/legitimacy parent prime, not here.)

- **Database replication** — master–replica configurations (PostgreSQL, MySQL, MongoDB) require explicit split-brain protection; automatic failover without fencing is a notorious way to lose data.
- **Cluster coordinators and service discovery** — etcd, Consul, and ZooKeeper use quorum leader election so only a majority partition can elect a leader, with minority partitions going read-only or unavailable.
- **Clustered storage and file systems** — SAN arrays, GFS2, and OCFS2 implement fencing (STONITH) to guarantee at most one active writer survives any partition.
- **Consensus protocols** — Paxos, Raft, and Zab build the single-leader-per-epoch invariant in from first principles, forbidding two leaders from committing divergent writes for the same log position.
- **Blockchain and distributed ledgers** — a partition that lets two miner subsets extend the chain independently produces a fork, the Byzantine variant resolved by the longest-chain rule.
- **CAP-theorem design analysis** — the failure mode is the empirical motivation for classifying any replication design by which leg (consistency or availability) it sacrifices under partition, and for the cluster arithmetic (prefer odd counts; even splits deadlock; two-node clusters need an external witness).

structural_signature:
**Sig role-phrases:**

- **the replicated state** — multiple nodes maintaining copies of one shared, authoritative dataset
- **the single-writer entitlement** — the invariant that at most one node may be the current write authority for any state element
- **the leader-election mechanism** — the failure-detection and promotion logic by which a node decides it is the primary
- **the network partition** — a failure that bisects the cluster so each subset can no longer see the others
- **the symmetric mis-inference** — each isolated subset reads the silence identically, inferring the unreachable peers are dead and that it is entitled to take over
- **the autonomous re-election** — both subsets independently promote a primary without the other's agreement, yielding two simultaneous writers
- **the divergent histories** — both primaries commit writes to the same nominal dataset, producing two irreconcilable logs once the partition heals
- **the quorum knob** — the countable majority of voting members that, since only one subset can hold it, governs which side may continue
- **the enforcement toolkit** — quorum, fencing/STONITH, a consensus epoch (Paxos/Raft/Zab), or longest-chain, each guaranteeing single-writer entitlement before divergence can begin

what_it_is_not:
- **Not the neuroscience syndrome of the same name.** "Split-brain" most famously names the corpus-callosotomy condition — two hemispheres of one brain operating semi-independently after the connecting fibers are cut. That shares only the word and a faint bisection image: there is no replicated state, no leader election, no consensus toolkit. The distributed-systems failure mode and the neurological one are a naming coincidence, not the same phenomenon under two labels.
- **Not a node crash or hardware failure.** A split brain is the opposite of a node dying. Every node remains alive and functioning correctly; what fails is the *network between them*, partitioning the cluster so each live subset can no longer see the others. The danger arises precisely because both halves keep running and keep accepting writes — a crashed node accepts nothing and threatens no divergence.
- **Not a bug in any one node.** No node is faulty or misbehaving. Each half observes silence from its peers and runs the *same* failure-detection logic that, locally, correctly concludes it should take over. It is the *symmetry* of two correct nodes reasoning identically about each other — not any defect — that manufactures two self-appointed primaries.
- **Not data corruption or Byzantine misbehavior.** Both primaries write valid, internally consistent data; neither lies or computes wrongly. The irreconcilable state after the partition heals is not corrupted bytes but two *legitimate-looking* divergent histories of the same dataset. (The Byzantine variant, where nodes may act adversarially, is the separate longest-chain case, not the ordinary split brain.)
- **Not avoidable by simply staying available.** The intuitive fix — "if I can't reach the others, keep serving so the system stays up" — is exactly the availability-first reasoning that *causes* the split brain, because the other half reasons identically. Under partition the CAP trade-off is inescapable: a node must sacrifice either consistency or availability, and a design claiming to preserve both is unsound on its face.

clarity:
Naming the split-brain problem exposes the fallacy that availability-first failover designs walk into: the assumption that "if I cannot see the other half, the other half must be down." The label makes vivid why that reasoning is fatal — the isolated half on the *other* side of the partition reasons identically about *you*, so symmetry, not malice, is what produces two self-appointed primaries. Recognizing this reframes partition tolerance from an optional robustness feature into a property every replication design must explicitly handle, because partitions are not a rare anomaly to hope against but a certainty to plan for; the only open question is what a node does when it finds itself alone.

The concept also makes the underlying quorum logic legible: since at most one partition can ever hold a majority of voting members, granting write authority only to a majority guarantees at most one writer survives any partition. Stated as a bare failure mode, the split brain looks like bad luck; stated structurally, it tells the designer exactly which knob governs safety. That is why odd-numbered clusters are preferred (an even split can deadlock with no majority on either side) and why a two-node cluster cannot protect itself without an external witness — there is no majority to be had. The label thereby converts a vague fear of "what happens when the network breaks" into a sharp design question: which subset is entitled to continue, and what mechanism — quorum, fencing, or consensus epoch — enforces that entitlement before divergent writes can ever begin.

manages_complexity:
A replicated cluster under partition presents the designer with a combinatorial thicket: any number of nodes, any cut of the network into subsets, each subset running its own failure-detection and leader-election logic, each potentially right or wrong about who is alive. Reasoning about safety case by case — this cut, that timeout, this failover policy — is unbounded. The split-brain framing collapses that thicket to a single invariant the designer must preserve across every partition: at most one subset may hold write authority at any instant. Everything else about the partition — how many nodes, where the cut falls, which node noticed first — stops mattering once that one property is guaranteed; the analyst tracks not the topology of the failure but whether the design enforces single-writer entitlement through it. The sprawl of "what could go wrong when the network breaks" compresses to one yes/no question with a known governing knob.

That knob is what the concept makes legible. Because at most one partition can ever contain a majority of voting members, the safety question reduces to a single countable quantity — quorum — and a small, closed menu of mechanisms for enforcing the invariant once a partition is detected: quorum (grant authority only to the majority; minority goes read-only), fencing (evict the loser before the winner commits), or a consensus epoch (no two leaders for the same log position). The designer reads the qualitative outcome straight off cluster arithmetic: odd node counts admit a clean majority on exactly one side; even counts admit a tie that strands both halves; two nodes have no majority to be had and so demand an external witness. A space that looked like an open-ended catalogue of replication disasters — each protocol, each topology re-analyzed from scratch — becomes a one-parameter design problem (is there a majority, and what enforces its monopoly on writes?) with a predictable branch structure (majority survives / minority refuses / tie deadlocks) and a finite remedy set. The split-brain label is the move that turns "plan against every way the network might betray us" into "guarantee one invariant, sized by one count."

abstract_reasoning:
The split-brain frame licenses a tight set of reasoning moves about replicated systems under partition, all organized around the single-writer invariant and the cluster arithmetic that enforces it.

The signature *diagnostic* move runs from a node's local observation to a global danger by way of symmetry. When a replica loses contact with its peers, the naive inference is "the others are down, so I should take over." The split-brain reasoner negates that inference by reasoning about the *other* side: the isolated half across the partition observes exactly the same silence and reasons identically about *you*, so the same failure-detection logic that promotes you promotes it. The conclusion drawn is that symmetry, not any node's fault, manufactures two self-appointed primaries — and therefore that an unreachable peer must never be inferred to be a *dead* peer. Running the move backward, after a partition heals and two divergent write logs are found, the analyst infers that both halves chose availability and that no protocol-safe automatic reconciliation exists; the divergence is read as the fingerprint of a window in which two leaders committed to the same nominal dataset.

The *interventionist* move runs from cluster arithmetic to a guaranteed bound on the number of writers. The reasoner computes: because at most one subset of a partitioned cluster can hold a majority of voting members, granting write authority only to a majority forces at most one writer to survive any cut. From that single fact the design knobs follow as predictions. Prefer odd node counts, because an odd cluster always yields a clean majority on exactly one side; distrust even counts, because a 2–2 or 3–3 split strands both halves with no majority and the cluster correctly deadlocks rather than risk two writers; and recognize that a two-node cluster has no majority to be had, so it *must* import an external witness or arbiter to manufacture one. The reasoner also selects among a closed menu of enforcement mechanisms by what each guarantees and when it acts: quorum makes the minority refuse service; fencing (STONITH) forcibly evicts the losing primary *before* the winner commits, closing the simultaneity window directly; a consensus epoch (Paxos, Raft, Zab) forbids two leaders for the same log position by construction; and the longest-chain rule tolerates competing forks transiently and discards the shorter, the Byzantine variant of the same enforcement. Each choice carries a predicted cost — the minority's loss of availability — which is the CAP trade-off read off the design rather than discovered in production.

The *boundary-drawing* move fixes where the concept bites and which trade-off is unavoidable. It applies only to a genuinely replicated state with a notion of authoritative writes, a network that can partition, and a leader-election mechanism; absent any of those the failure mode does not exist. Inside that regime the binding constraint is taken as a certainty, not a risk: partitions *will* occur, so partition tolerance is not optional and the only open question a design may leave open is what a node does when it finds itself alone. The CAP reasoning then forces the regime split — under partition a node either preserves consistency by refusing writes or preserves availability by accepting them, and it cannot have both — so the analyst classifies any proposed replication design by which leg it sacrifices when the network breaks, and treats a design that claims to sacrifice neither as unsound on its face.

knowledge_transfer:
Within distributed systems the concept transfers *as mechanism*, and the transfer is essentially total across the field's subfields because they all instantiate the same configuration — replicated state, a partitionable network, leader election, authoritative writes. The diagnosis (symmetry manufactures two self-appointed primaries), the governing knob (quorum-sized majority), the closed remedy menu (quorum, fencing/STONITH, consensus epoch, longest-chain), and the cluster arithmetic (prefer odd counts; even splits deadlock; two-node clusters need a witness) all carry intact from one subfield to the next. Database replication (PostgreSQL, MySQL, MongoDB master–replica) imports them as fencing-plus-failover discipline; cluster coordinators (etcd, Consul, ZooKeeper) import them as quorum leader election; clustered storage and file systems (SAN arrays, GFS2, OCFS2) import them as STONITH; consensus protocols (Paxos, Raft, Zab) build the single-leader-per-epoch invariant in from first principles; and blockchain forks are the Byzantine variant, the longest-chain rule being the same single-writer enforcement under adversarial conditions. The vocabulary changes only at the surface — primary, leader, master, miner — while the structure and the remedies hold; within the domain this is the canonical failure mode the whole replication toolkit is engineered to prevent, and it transfers literally wherever that toolkit is deployed.

Beyond the domain two distinct things happen, and they must be kept apart. First, the homonym: "split-brain" in neuroscience (the corpus-callosotomy syndrome) is **not a transfer at all** — it names two hemispheres of one brain operating semi-independently after the connecting fibers are cut, with no replicated state, no election, no consensus toolkit. It shares the *word* and a faint bisection image, nothing of the mechanism; treating it as a cross-domain instance would be a pure naming coincidence, not even an analogy. Second, the genuine cross-domain reach — organizational schism, a succession dispute between two leadership factions after a CEO is incapacitated — is **(A) analogy**: it renames the components (replica → faction, partition → loss of contact) and borrows the *shape* (two parts of a formerly unified whole each claim to be the legitimate continuation) while dropping the machinery that gives the distributed-systems concept its predictive force. There is no automatic failure-detection, no fencing, no countable quorum that mechanically guarantees a single survivor; the human resolution mechanisms (boards, shareholder votes, courts) are wholly different, slower, and not protocol-safe. The picture is apt, but it is resemblance, not the mechanism travelling, and it should be marked as analogy.

Underneath that analogy there is a real **(B) shared abstract mechanism**, but it is more general than this entry and belongs to the parent it instantiates: a single coordinated authority is severed and both fragments self-authorize, so consensus on *who is the legitimate continuation* is lost and divergent histories accrue. That legitimacy-and-consensus pattern genuinely recurs — in organizational schisms, religious successions, contested elections — and the portable lesson (a fragment that cannot see the rest must not infer it is entitled to act as the whole) carries with it. But the *named cargo* of the split-brain problem does not: quorum, fencing, the CAP partition leg, the consensus epoch, the odd-cluster arithmetic are all networked-replicated-state furniture that dissolves the moment there is no automatic detection or countable voting membership. So when the lesson is needed outside distributed systems, the thing to carry is the parent prime — the consensus/legitimacy failure of a partitioned coordinated system — of which "split-brain" is the replicated-state instance, not the named concept with its protocol toolkit attached. See *Structural Core vs. Domain Accent* for why that places this entry as a domain-specific instance rather than a prime.


Spotlight Effect

one_liner: Explain the felt certainty that everyone noticed as an estimation error: the actor anchors on how salient a self-feature is from the inside and under-corrects toward observers' far lower base-rate attention, so predicted notice runs about double the actual.

core_idea:
The spotlight effect (Gilovich, Medvec & Savitsky 2000) is the documented tendency for people to systematically overestimate how much observers notice, attend to, and remember their appearance, behaviour, and performance. In the canonical demonstration, participants who wore an embarrassing T-shirt into a room of peers estimated that roughly half the room would recall the shirt; actual recall was closer to a quarter. The same pattern replicates for verbal slips, hairstyle changes, and displays of athletic performance.

The mechanism is egocentric anchoring: when estimating how much observers will notice a feature, the actor starts from the feature's salience in their own attention — where it is extremely prominent — and adjusts insufficiently toward the lower base rate of observer attention, because observers are occupied with their own concerns, their own appearance, and their own performance. The actor cannot readily inhabit the observer's perspective and experience the feature as just one item in a competing field of stimuli; they remain anchored on its salience from the inside. The result is a systematic asymmetry between first-person salience and third-person notice: whatever one cannot stop attending to about oneself commands far less external attention than one projects. The spotlight effect is closely related to, but distinct from, the *illusion of transparency* — the parallel overestimation that observers can read one's internal emotional states from external cues — which shares the egocentric-anchoring mechanism but operates on inferred mental states rather than noticed surface features.

scope_of_application:
The spotlight effect lives across the social-cognition and clinical subfields of psychology, plus its applied reach into communication and design; its substrate is always a perspective-taking human cognitive system over-projecting attention to a self-feature, so the "estimator over-weights its salient anchor" lesson that travels beyond that belongs to the anchoring-and-adjustment and base-rate-neglect primes, not the named effect.

- **Social anxiety (clinical)** — overestimation of others' attention to one's perceived flaws is a core cognitive distortion CBT targets; psychoeducation about the spotlight effect is a standard intervention.
- **Public-speaking coaching** — speakers overestimate how much the audience perceives their nervousness ("everyone could tell I was sweating"); rehearsal-feedback evidence of low observer recall is the standard correction.
- **Adolescent developmental psychology** — self-conscious overestimation of peer scrutiny peaks in adolescence, the construct being the modern formalisation of Elkind's *imaginary audience*.
- **Workplace and organisational distress** — employees overestimate how much colleagues notice individual mistakes, missed deadlines, or appearance choices, generating disproportionate distress and self-monitoring.
- **Design and marketing** — designers and brand managers overestimate consumer attention to incremental visual or product changes that consumers in fact do not register (construct-reuse, same human cognitive substrate in an organisational role).

structural_signature:
**Sig role-phrases:**

- **the salient self-feature** — some aspect of one's own appearance, behaviour, or performance that dominates one's own attention
- **the observer attention budget** — others' finite attention, spent mostly on their own concerns, so the feature is just one item in a competing field
- **the egocentric anchor** — the actor's estimate of observer attention started from the feature's inside-view salience
- **the insufficient adjustment** — failure to correct that anchor down toward the observer base rate, because the actor cannot inhabit the observer's perspective
- **the salience-vs-notice gap** — the resulting overestimate, fixed in direction and rough size: predicted attention runs systematically above observed, on the order of double the recall
- **the affective amplification** — anxiety, avoidance, and self-monitoring whose costs scale with the overestimate

what_it_is_not:
- **Not the claim that observers notice nothing.** The spotlight effect is a calibration error in *degree*, not the assertion that one is invisible. Observers do register the feature — just at a far lower rate than the actor projects (roughly half, in the canonical recall figures); the defect is the over-projection, not the existence of attention.
- **Not a report about how much was actually seen.** The felt "everyone noticed" is the output of an estimation error, not evidence about the social world. The concept localises the distortion to the actor's estimate of others' attention — anchored on inside-view salience and under-corrected toward the observer base rate — so the corrective is recalibrating the estimate, not changing the situation.
- **Not vanity, narcissism, or self-importance.** The mechanism is egocentric *anchoring*, not egotism: it fires hardest for features one would rather hide — an embarrassing shirt, a verbal slip, visible nervousness — where the actor wishes for *less* attention, not more. Reading it as conceit mistakes a perspective-taking limit for a motive.
- **Not the illusion of transparency.** Its closest relative runs on the same egocentric-anchoring engine but takes different content: the illusion of transparency is overestimating that observers can *read an internal state* (nervousness, a lie, an emotion) from external cues, whereas the spotlight effect is overestimating that they *notice a surface feature*. They are distinguished by whether the overestimate is about what *shows* or what can be *inferred*.
- **Not the substrate-free anchoring prime.** Strip the actor/observer/attention vocabulary and what remains is "an estimator over-weights its salient starting point and under-corrects toward the external rate" — exactly anchoring-and-adjustment plus base-rate neglect, which recurs in numeric and probability estimation with no self and no observers. The spotlight effect is that general engine pointed at one specific content; the cross-domain reach belongs to the parent, not the named effect.

clarity:
Naming the spotlight effect makes a specific asymmetry legible and measurable: the gap between *first-person salience* and *third-person notice*. A feature one cannot stop attending to about oneself commands far less external attention than one projects, because observers are spending their own attention budget on their own concerns. With the label, the distressing intuition "everyone noticed" stops being a fact about the social world and becomes a predictable estimation error with a known direction and rough size — actors' predictions running on the order of double the observed recall. That reframing also separates two things self-conscious experience fuses: the *fact* of being observed from the *content* of what is observed. One may indeed be seen, but the slice of oneself that registers in others' memory is far narrower than the fact of being seen implies, and the spotlight effect localizes the error to the content estimate rather than to the visibility itself.

This is what gives the concept its clinical bite. In the cognitive treatment of social anxiety the load-bearing question becomes not "how do I stop being seen" but "is my estimate of others' attention anchored on my own salience and uncorrected toward their base-rate notice" — relocating the target of intervention from the situation to the estimate, which rehearsal feedback and direct attention-checks can correct. The label also draws a clean line to its nearest neighbour: the spotlight effect concerns overestimating that observers *notice a surface feature*, while the illusion of transparency concerns overestimating that they can *read an internal state* from external cues. The two run on the same egocentric-anchoring engine but take different content, and keeping them distinct tells a practitioner whether the overestimate is about what shows or about what can be inferred.

manages_complexity: The distress of feeling watched arrives in a sprawl of superficially unrelated situations — an embarrassing shirt worn into a room, a verbal slip in a meeting, a new haircut, visible nervousness during a talk, a missed deadline, an athletic flub, a barely-changed product detail — each of which seems to demand its own account of who saw what and how badly it landed. The spotlight effect collapses that sprawl to one mechanism, egocentric anchoring, and reduces the whole class to a single tracked relation: the gap between *first-person salience* (how prominent the feature is in the actor's own attention, where it dominates) and *third-person notice* (the far lower rate at which observers, spending their attention budget on their own concerns, actually register it). Rather than reconstructing each scene's social dynamics from scratch, the analyst reads the outcome off that one asymmetry, with both its direction and its rough magnitude already fixed: the actor's estimate is anchored on the inside view and adjusted insufficiently toward the observer base rate, so predicted attention runs systematically above observed — on the order of double the actual recall. The compression also localises the error, which is the load-bearing move: it splits the *fact* of being observed from the *content* of what is observed and pins the distortion to the content estimate, not the visibility. One may indeed be seen; the slice of oneself that lands in another's memory is far narrower than the fact of being seen suggests. That localisation hands the practitioner a clean branch structure in place of an open problem: because the defect lives in the estimate rather than in the situation, the lever is not "stop being seen" but "recalibrate the estimate toward the observer's base-rate notice," which rehearsal feedback and direct attention-checks can move — and the same one-parameter read sorts the neighbouring case (overestimating that an internal state can be *inferred*, the illusion of transparency) as the identical anchoring engine pointed at different content, so it need not be theorised separately either.

abstract_reasoning:
The spotlight effect licenses a set of inferential moves in social-cognition and clinical psychology, all reading off one asymmetry — the gap between *first-person salience* (how prominent a self-feature is in the actor's own attention, where it dominates) and *third-person notice* (the far lower rate at which observers, spending their attention budget on their own concerns, actually register it).

The signature *diagnostic* move runs from a felt "everyone noticed" to a verdict about the estimate rather than the social world. The analyst infers that the actor's prediction of observer attention was *anchored on the inside view* — the feature's overwhelming salience in their own attention — and adjusted insufficiently toward the observer base rate, so the distress is the predictable output of an estimation error, not a report of how much was seen. The inference comes with both direction and rough magnitude pre-fixed: predicted attention runs systematically *above* observed, on the order of double the actual recall. A second, sharpening move splits the *fact* of being observed from the *content* of what is observed and localises the error to the content estimate: one may indeed be seen, but the slice of oneself that lands in another's memory is far narrower than the fact of being seen implies — so the analyst attributes the distortion to "how much of me registered," not to "was I visible."

The *interventionist* move follows directly from that localisation and relocates the target from the situation to the estimate. Because the defect lives in the estimate rather than in the visibility, the predicted-effective lever is not "stop being seen" but *recalibrate the estimate toward the observer's base-rate notice* — which rehearsal feedback (showing a speaker how little of their nervousness observers recalled) and direct attention-checks can move. This yields the concept's clinical bite and a falsifiable contrast: in the cognitive treatment of social anxiety, an intervention that supplies evidence of low observer recall is predicted to reduce the overestimate, whereas situation-directed avoidance (which leaves the anchored estimate untouched) is predicted not to correct it. The reasoning thus predicts that two responses which both feel like "managing being judged" diverge — feedback-recalibration helps, concealment does not.

The *predictive* move estimates the error and its downstream cost before measuring the scene. The actor's estimate is forecast to exceed observed recall by roughly a factor of two; and because the spotlight perception drives anxiety, avoidance, and self-monitoring, those affective costs are predicted to *scale with the overestimate* — the larger the gap between projected and actual notice, the greater the distress and withdrawal. This makes the magnitude of self-conscious suffering a derivable consequence of the anchoring gap rather than an independent variable.

The *boundary-drawing* move keeps these inferences inside a system where an agent can model how others attend and where self-focused attention is salient enough to serve as the anchor that fails to be adjusted; remove the perspective-taking capacity or the salient self-anchor and there is nothing to over-project. The boundary also draws a clean line to the nearest neighbour by *content*: the spotlight effect concerns overestimating that observers *notice a surface feature*, while the illusion of transparency concerns overestimating that they can *read an internal state* from external cues — the same egocentric-anchoring engine pointed at different content, so keeping them distinct tells the practitioner whether the overestimate is about what *shows* or about what can be *inferred*. Carried to a company "overestimating consumer attention," the substrate is still a human cognitive system embedded in a role rather than a new mechanism, so the inference is the same anchoring read, not a separate phenomenon.

knowledge_transfer:
Within social and clinical psychology the spotlight effect transfers *as mechanism*, not as analogy, because every site in its range runs the identical engine — an estimate of others' attention anchored on the actor's own salience and adjusted insufficiently toward the observer base rate — over different content. The cognitive treatment of social anxiety (psychoeducation that supplies evidence of low observer recall), public-speaking coaching (rehearsal-feedback showing how little of a speaker's nervousness registered), the developmental study of adolescent self-consciousness (the modern formalisation of Elkind's *imaginary audience*), and workplace distress over a noticed mistake or appearance choice are not separate phenomena: they are one asymmetry — first-person salience versus third-person notice — read off the same way, with the same direction and the same rough magnitude (predicted attention running on the order of double the observed recall), and the same load-bearing intervention (recalibrate the estimate toward the observer's base-rate notice, rather than try to stop being seen). The diagnostic, the roughly-2× gap, and the feedback-recalibration lever all carry intact across the cluster because all of it shares the precondition: a human cognitive system that models how others attend and is anchored on a salient self-feature.

The honest beyond-case is **shared abstract mechanism (B)**, not a second domain. The genuinely substrate-portable structure is the parent — *anchoring and adjustment* combined with *base-rate neglect* — which recurs across countless estimation tasks with no self and no observers at all (numeric anchoring on an arbitrary starting value, neglecting the population base rate in a probability judgment). The spotlight effect is precisely that general engine pointed at one specific content: the attention other people pay to a feature of oneself. So when the cross-domain lesson is needed — "an estimator over-weights its own salient starting point and under-corrects toward the external rate" — it should be carried by the anchoring/base-rate primes, **not** by "spotlight effect," whose named cargo (the actor/observer/attention vocabulary, the self-feature, the social-anxiety clinical apparatus) stays home-bound. This is also why its apparent extensions are reuse rather than reach: a company "overestimating consumer attention" to an incremental product change feels like a new domain, but the substrate is still a human cognitive system embedded in an organisational role — the same anchoring read, not a fresh mechanism, so the move is **analogy / construct-reuse (A)** and should be marked as such.

The same boundary sorts the whole egocentric-anchoring family. The illusion of transparency, illusion of asymmetric insight, false consensus, and false uniqueness are each the same engine pointed at different content (inferred internal states, others' beliefs, others' similarity to oneself); the spotlight effect and the illusion of transparency in particular run on one shared anchoring core and differ only in whether the overestimate concerns what *shows* or what can be *inferred*. None of these named effects travels as mechanism beyond human social cognition; what travels is the shared parent they all instantiate. The clean boundary, then: literal transfer of the spotlight effect wherever a perspective-taking human over-projects attention to a self-feature, and beyond that the reach belongs to anchoring-and-adjustment plus base-rate neglect, not to the named effect. (See *Structural Core vs. Domain Accent*.)


Spotlight Fallacy

one_liner: Infer that a property is common in a population because it is prominent in coverage of it — mistaking a selection bias for a base rate, since an attention-allocating channel surfaces cases by newsworthiness rather than by frequency.

core_idea:
The spotlight fallacy is the informal fallacy of inferring that a property is common in a population because it is prominent in media coverage of the population. The structural defect is a *selection bias mistaken for a base rate*: media (or any attention-allocating channel) systematically over-covers vivid, sensational, or newsworthy instances, so the sampled frequency that reaches the reasoner has been filtered by newsworthiness rather than drawn at random. A reasoner who treats the filtered sample as base-rate evidence concludes that the population resembles its coverage — that most teenagers commit crimes (from crime reporting), that shark attacks are a major cause of death (from their disproportionate news visibility), that a minority group consists primarily of its most-covered subset (from terrorism or crime coverage of that group).

The fallacy is the informal-logic label for the same inferential error that Tversky and Kahneman (1973) named the *availability heuristic* — the tendency to estimate frequency by how easily instances come to mind — and that philosophy of science recognizes as a selection-biased sampling problem. The distinctive rhetorical contribution of the label is to identify the *attention-allocating channel* (the filter) as the mechanism that corrupts the sample: it is not that memory is unreliable in some generic sense, but that a specific institutional process — media coverage, algorithmic curation, a curated anthology of famous successes — surfaces cases according to their newsworthiness or visibility rather than their population frequency, and the reasoner who forgets this produces systematically inflated frequency estimates for whatever the channel preferentially surfaces. The inverse move — "I read about a case where…" used to rebut a statistical generalization — commits the same error in the other direction: one vivid covered instance is treated as falsifying a population-level claim whose base rate the single case cannot touch.

scope_of_application:
The spotlight fallacy lives across argumentation and the adjacent fields that reason from a sample to a population about how common something is; its reach is bounded to inferences where a frequency belief is built from cases delivered by an attention-allocating channel, while the underlying `selection_bias` + `availability_heuristic` it instantiates travels off-domain under those parents rather than as the media-framed fallacy.

- **Informal-logic fallacy catalogues** — the home, where it names the inference from media prominence to population frequency and its reverse (one covered anecdote offered against a base rate).
- **Crime and terrorism perception** — public estimates running orders of magnitude high because reporting over-covers the vivid and sensational.
- **Risk perception and policy** — vivid low-frequency harms (shark attacks, child abduction) driving pressure misaligned with actuarial risk.
- **Stereotype formation** — over-coverage of a marginal subgroup inflating the population estimate toward its most-covered subset.
- **Survivorship and success literature** — famous covered successes mistaken for the population of attempts, making the path look easier than it is.
- **Algorithmic curation** — what a feed or recommender surfaces taken for what is common in the world.
- **Cognitive psychology and philosophy of science** — the *same* error recognized literally under other names, the availability heuristic (Tversky and Kahneman 1973) and selection-biased sampling, marking where the parent rather than the rhetorical packaging is doing the work.

structural_signature:
**Sig role-phrases:**

- **the underlying population** — the set of cases the arguer ultimately wants a frequency conclusion about
- **the attention-allocating channel** — media coverage, a feed algorithm, search rankings, a curated anthology: the filter that surfaces cases by newsworthiness/visibility rather than population frequency
- **the covered subset** — the cases that actually reach the reasoner through the channel, systematically over-weighted toward the vivid, rare, or sensational
- **the inferential leap** — treating the channel-filtered covered subset as if it were a representative (random) sample, so the population is read off its coverage
- **the anti-correlated selection criterion** — the sharp version of the defect: the very features that earn coverage (rarity, vividness, moral charge) make an instance *unrepresentative*, so the filter is not merely silent about frequency but anti-correlated with it
- **the directional inflation** — the consequence the anti-correlation predicts: the estimate is wrong *in a known direction* — the more newsworthy the property, the more its coverage overstates prevalence
- **the locate-the-filter remedy** — the fix the diagnosis fixes: not "think harder" or "remember better" but interrogate the sampling channel (the defect is the filter, not the faculty), with the acquittal that a non-visibility-selecting method lets the inference stand
- **the reverse move** — the same corrupted-sample error pointed the other way: one vivid covered instance ("I read about a case where…") treated as overturning a base rate it cannot touch

what_it_is_not:
- **Not the availability heuristic itself.** Availability is the cognitive *mechanism* — the ease with which instances come to mind. The spotlight fallacy is the specific *inferential error* of treating that ease, or the coverage that produces it, as evidence of how common something is. Holding the two apart matters because they imply different fixes: the remedy is not to manage the faculty but to interrogate the channel feeding it.
- **Not unreliable memory.** The defect is not that recall is generically untrustworthy but that a specific institutional process — media coverage, an algorithmic feed, a curated anthology of famous successes — surfaces cases by newsworthiness rather than frequency. So the fix is "locate the filter, not the faculty": interrogate or replace the sampling channel, not "think harder" or "remember better."
- **Not any inference from examples.** Reasoning from cases is fallacious here only when the cases arrived through a channel whose selection criterion is *anti-correlated* with the property of interest — where the very features that earn coverage make an instance unrepresentative. If the cases were gathered by a method that does not select on visibility, the generalization stands on other grounds; the test is the channel's selection criterion, not the mere use of examples.
- **Not a claim about whether the belief is true.** The defect is the *provenance of the sample*, not the truth of the conclusion. The analyst convicts the inference on the channel alone — a frequency estimate built from crime reporting is unwarranted however vivid the instances — and bypasses the fact-by-fact rebuttal that the next headline would only reopen. The error can also run in reverse: a single covered anecdote ("I read about a case where…") offered to overturn a base rate it cannot touch is the same corrupted-sample move pointed the other way.
- **Not a substrate-spanning structural pattern.** Strip the "media" and "newsworthy" vocabulary and the construct dissolves into `selection_bias` combined with the `availability_heuristic` — inferring population frequency from a non-representative sample whose selection criterion is anti-correlated with the property. The spotlight fallacy's only distinctive cargo is the rhetorical accent (the channel framed as *media*, the filter as *newsworthiness*); it is the media-and-rhetoric instance of the parent sampling error, sibling to hasty generalization, survivorship bias, and base-rate neglect, and adds no structural content the parents do not already supply.

clarity:
Naming the fallacy makes a near-invisible inferential step explicit: the silent assumption that coverage is a representative sample of the world. The everyday slide from "I see this often in the news" to "this is common in the world" passes unexamined precisely because the sampling stage is hidden — the reasoner experiences the covered cases as simply *what is out there*. The label forces the buried premise into view, and once it is visible it is plainly false: the very features that earn an instance coverage (rarity, vividness, moral charge) are the features that make it *unrepresentative*, so the channel's selection criterion is not merely uninformative about frequency but anti-correlated with it. That reframes the reader's question from "is this claim true?" to the sharper "by what process did these cases reach me, and does that process track frequency or newsworthiness?"

The label's second clarifying service is to separate a *property* from its *misuse* — to mark the line between the ease with which examples come to mind and the inference that draws frequency from that ease. Availability names the cognitive mechanism; spotlight fallacy names the specific error of treating availability as evidence of how common something is. Holding the two apart matters because the diagnosis points to a different fix: the remedy is not "think harder" or "remember better" but locate the *filter* — the media outlet, the algorithmic feed, the curated anthology of famous successes — and ask whether it surfaces cases by their population frequency or by their visibility. This also lets the practitioner catch the fallacy running in reverse, where a single vivid covered instance ("I read about a case where…") is offered to overturn a statistical generalization whose base rate one case cannot touch.

manages_complexity:
Inflated frequency beliefs arrive as a scattered crowd of separate-looking errors: most teenagers commit crimes, shark attacks are a major cause of death, a minority group consists mostly of its most-covered subset, terrorism and child-abduction are rife, the path to success looks like the famous successes one reads about. Treated as a menagerie, each is its own object — a crime-perception mistake here, a risk-perception mistake there, a stereotype, a survivorship error — to be argued down on its own facts, with the standing risk that any new domain (a fresh subject of coverage, a new feed, a new anthology) breeds a new instance to be diagnosed from scratch. And each looks, on the surface, like a question of whether the belief is *true*, inviting a fact-by-fact rebuttal that the next vivid headline immediately reopens.

The spotlight fallacy compresses that crowd to one structural diagnosis and one question. Every instance is the same defect underneath — a *selection bias mistaken for a base rate*: an attention-allocating channel surfaces cases by newsworthiness (vividness, rarity, moral charge) rather than by population frequency, and the reasoner treats the filtered sample as if it were drawn at random. The verdict on any instance therefore no longer requires re-litigating its facts; it turns on a single parameter the practitioner can name in the moment — *by what process did these cases reach me, and does that process track frequency or newsworthiness?* That question yields a clean read. If the cases arrived through a channel whose selection criterion is visibility, the sample is corrupted and the frequency inference is unwarranted, however vivid the instances; worse, because the very features that earn coverage are the features that make an instance unrepresentative, the channel's criterion is not merely silent about frequency but anti-correlated with it, so the inflation is predictable in direction. If the cases were gathered by a method that does not filter on visibility, the inference stands on other grounds. The analyst tracks the filter — the media outlet, the algorithmic feed, the curated anthology of famous successes — rather than the truth of each separate belief, and the verdict reads off whether that filter samples by frequency or by attention.

The compression has further reach in two directions the named fallacy makes legible. It folds the apparently distinct cases — crime perception, risk policy, stereotype formation, survivorship literature, algorithmic curation — into instances of one mechanism under different attention-allocation regimes, so the same diagnosis ports across them without a per-domain catalog; locate the channel, and the rest follows. And it catches the error running in reverse: a single vivid covered instance ("I read about a case where…") offered to overturn a statistical generalization is the same corrupted-sample move pointed the other way — one channel-surfaced case treated as touching a base rate it cannot reach — caught by the same question about how the case arrived. The compression also fixes the remedy by the diagnosis: because the defect is the filter and not the reasoner's memory, the fix is not "think harder" or "remember better" but interrogate the sampling channel — a fix that reads off the structure directly, rather than being guessed at case by case.

abstract_reasoning:
The spotlight fallacy licenses a set of moves in the analysis of frequency claims, all anchored to the recognition that the corrupting agent is an attention-allocating channel, not the reasoner's memory. The signature *diagnostic* move runs from a belief about how common something is back to the *process by which the supporting cases arrived*: rather than asking whether the belief is true, the analyst asks "by what channel did these instances reach me, and does that channel sample by population frequency or by newsworthiness?" An estimate built from crime reporting, a sensational feed, or a curated anthology of famous successes is convicted on the channel alone — the sample was filtered by visibility, so the frequency inference is unwarranated however vivid the instances. The inference is from *provenance of the sample* to *validity of the generalization*, and it bypasses the fact-by-fact rebuttal that the next headline would only reopen.

The most distinctive move is a *directional prediction* that ordinary "your sample is biased" cannot make. Because the very features that earn an instance coverage — rarity, vividness, moral charge — are the features that make it unrepresentative, the channel's selection criterion is not merely silent about frequency but *anti-correlated* with it, so the analyst predicts not just that the estimate is wrong but that it is inflated *in a known direction*: the more newsworthy the property, the more its coverage overstates its prevalence. This lets the practitioner anticipate the error's sign before measuring — public estimates of terrorism, shark attacks, and child abduction are predicted to run high, success rates inferred from famous successes are predicted to look easier than they are — and to expect the divergence to widen exactly where coverage is most disproportionate.

The *boundary-drawing* moves keep the diagnosis precise and aimed at the right target. First, the analyst separates the cognitive *mechanism* (availability — the ease with which instances come to mind) from the *inferential error* (treating that ease, or that coverage, as evidence of frequency), and holds them apart because they imply different fixes: the defect is not unreliable memory to be corrected by "thinking harder" or "remembering better" but a corrupted sampling channel to be *interrogated and, where possible, replaced* by a method that does not filter on visibility. The remedy thus reads off the diagnosis — locate the filter, not the faculty. Second, the analyst draws the regime edge that acquits legitimate inference: if the cases were gathered by a method that does *not* select on visibility, the generalization stands on other grounds and the fallacy charge does not apply, so the test is the channel's selection criterion, not the mere fact that the conclusion came from examples. Third, the same machinery catches the error *running in reverse*: a single vivid covered instance ("I read about a case where…") offered to overturn a statistical generalization is the identical corrupted-sample move pointed the other way — one channel-surfaced case treated as touching a base rate it cannot reach — so the analyst applies the same provenance question to the rebutting anecdote and rejects it on the same grounds. And a *recognition-across-regimes* move folds crime perception, risk policy, stereotype formation, survivorship literature, and algorithmic curation into one mechanism under different attention-allocation channels, so the diagnosis ports across them without a per-domain catalog: identify the channel, establish that it sorts by visibility rather than frequency, and the verdict follows the same way every time.

knowledge_transfer:
Within argumentation and the adjacent fields that reason from a sample to a population, the spotlight fallacy transfers *as mechanism*: wherever a frequency belief is built from cases delivered by an attention-allocating channel, the provenance-of-sample diagnostic ("by what channel did these instances reach me, and does it sample by frequency or by newsworthiness?"), the directional-inflation prediction (the more newsworthy the property, the more its coverage overstates its prevalence), and the locate-the-filter remedy all carry intact. They apply the same way across **crime and terrorism perception** (public estimates running orders of magnitude high), **risk perception and policy** (vivid low-frequency harms driving pressure misaligned with actuarial risk), **stereotype formation** (over-coverage of a marginal subgroup inflating the population estimate toward it), **survivorship and success literature** (famous covered successes mistaken for the population of attempts), and **algorithmic curation** (what the feed surfaces taken for what is common) — folding these into one mechanism under different attention-allocation regimes rather than a per-domain catalog, and catching the error in reverse (a single covered anecdote offered to overturn a base rate it cannot touch). Most tellingly, the *same* inferential error is recognized, not by analogy but literally, under other names in neighboring disciplines: cognitive psychology calls it the **availability heuristic** (Tversky and Kahneman 1973), and the philosophy of science treats it as a special case of **selection-biased sampling**. That cross-disciplinary recurrence is real and mechanism-level — but it is a clue to the entry's true status, because what those disciplines share with the spotlight fallacy is the underlying sampling error, not the rhetorical packaging.

Beyond the media/argumentation framing the honest reading is **shared abstract mechanism**: the structural defect genuinely recurs across domains as co-instances, but what travels is the *parent*, not this entry's named machinery. Strip the "media" and "newsworthy" vocabulary and the construct dissolves into *inferring population frequency from a non-representative sample whose selection criterion is anti-correlated with the property of interest* — which is `selection_bias` combined with the `availability_heuristic`, both substrate-portable in their own right. The spotlight fallacy's home-bound cargo is precisely the rhetorical accent: framing the channel as *media coverage* and the filter as *newsworthiness*, which is what makes the same statistical mistake legible to a critic of public discourse and stereotype but adds no new structural content over selection bias. So when the pattern turns up in scientific publishing (citing famous successes), citation analysis (over-cited papers), or recommender feeds, the transferable lesson is that a *selection-biased channel is being read as a base rate* — carried by `selection_bias` (and `availability_heuristic`), of which the spotlight fallacy is the *media-and-rhetoric* instance, sibling to the other named population-inference fallacies (hasty generalization, anecdotal fallacy, survivorship bias, base-rate neglect) that all instantiate the same sampling failure. The cross-domain reach belongs to those parents; "spotlight fallacy" as named is the rhetorical packaging of the underlying statistical error, and stretching the label past the attention-allocation framing would borrow its vividness while leaving behind nothing the parent prime does not already supply (see *Structural Core vs. Domain Accent*).


St. Petersburg Paradox

one_liner: A gamble whose expected monetary value is infinite yet which real deciders will pay only a few coins to enter, exposing that a value function linear in money mishandles fat-tailed payoffs and must be replaced by a concave or bounded utility.

core_idea:
The St. Petersburg paradox is the observation, introduced by Nicolaus Bernoulli (1713) and analysed by Daniel Bernoulli (1738), that a specific gamble has *infinite expected monetary value* yet real decision-makers will pay only a modest finite sum to play it. The game: a fair coin is flipped repeatedly until tails first appears; the payoff is 2^n ducats where n is the number of flips including the final tails. The expected monetary value is the sum over all n of (1/2^n) × 2^n = 1 + 1 + 1 + … = infinity. Yet almost no one will pay more than a few ducats to enter. The paradox is the gap between what expected-value maximisation prescribes and what observed willingness-to-pay reveals.

Daniel Bernoulli's resolution is the foundation of modern utility theory: he proposed that the subjective value (utility) of money is not linear but *concave* — the marginal utility of each additional unit of wealth is smaller than the previous one, so the expected *utility* of the gamble is finite even though its expected *monetary value* is not. A decision-maker with logarithmic utility, for example, assigns finite expected utility to the gamble and will pay only a finite price for it, approximating observed behaviour. This concavity argument introduced the distinction between *risk-neutral* decision-makers (who maximise expected monetary value) and *risk-averse* ones (who maximise expected utility of a concave utility function), and directly prefigured the axiomatisation of expected utility theory by von Neumann and Morgenstern (1944). The paradox is also the historical entry point for recognising that *expected-value maximisation is inadequate as a normative criterion when payoff distributions have divergent or fat tails*, a lesson that recurs in decision problems with heavy-tailed payoffs such as catastrophic insurance and venture investment with power-law returns.

scope_of_application:
The St. Petersburg paradox lives across decision theory, economics, finance, and the philosophy of probability, as a diagnostic-and-repair and pedagogical anchor; it operates wherever an agent faces a heavy-tailed or divergent-expectation gamble. Its transferable content belongs to the parent primes `expected_utility` / `risk_aversion` / `heavy_tail` (Pascal's Mugging is that parent recurring, not the paradox arriving), so the genuine habitats are the in-domain contexts where the specific gamble organizes analysis.

- **Pedagogical anchor for utility theory** — the canonical textbook entry point: every standard microeconomics and decision-theory course introduces concave utility and the expected-value/expected-utility wedge via St. Petersburg.
- **The risk-aversion debate** — the paradox is the canonical motivation for adopting expected utility, rather than expected value, as the normative decision criterion, and for the risk-neutral/risk-averse distinction.
- **Tail-risk finance** — lottery, venture-capital, and catastrophic-insurance problems with ill-behaved means are analyzed through St. Petersburg logic: divergent expectations forced through concave or bounded utility transforms.
- **Foundations of probability and rationality** — the paradox features in philosophical debate over whether expected-value reasoning is normatively binding when expectations diverge (Hájek's "Vexing Expectations").
- **AI / decision-systems design** — Pascal's-Mugging defenses against unbounded-expectation policies are modern St. Petersburg defenses, bounding utility so a tiny probability times an enormous claimed payoff cannot dominate.

structural_signature:
**Sig role-phrases:**

- **the gamble structure** — a coin flipped until tails, payoff 2^n for n flips: continuation probability declining geometrically while payoff grows proportionally
- **the divergent expected monetary value** — the sum Σ (1/2^n)·2^n = ∞ under linear-in-money scoring, a property of the distribution alone
- **the observed finite willingness-to-pay** — real deciders paying only a few ducats, the behavioral fact the infinite EV contradicts
- **the linear-money/divergent-tail mismatch** — the diagnosed pathology: a value function linear in money meeting a mean dominated by vanishingly probable enormous payoffs
- **the concave/bounded-utility repair** — replacing linear money-value with a concave or bounded utility, rendering expected *utility* finite while expected *money* diverges
- **the risk-neutral/risk-averse split** — the distinction the repair statably introduces, with utility curvature indexing risk aversion as a one-parameter family
- **the EV/EU wedge** — the load-bearing separation of expected monetary value (distribution) from expected utility (preferences), so the decision rule cannot live in the distribution
- **the divergent-mean boundary** — the scope limit: expected-value maximization is admissible only when the mean is well-behaved, inadmissible for fat-tailed or divergent expectations
- **the inverse rationalization move** — recovering the utility function under which the observed price is rational (e.g. log utility), the program von Neumann–Morgenstern systematized

what_it_is_not:
- **Not a genuine logical paradox.** It is a *mismatch* between a decision criterion (maximize expected money) and observed behavior, not a contradiction in the theory. It dissolves the moment value is allowed to be concave in money: the expected *utility* is finite while the expected *money* diverges. "Paradox" marks a surprise that resolves, not an antinomy.
- **Not uniquely resolved by concave utility.** Diminishing marginal utility is the *historical* (Bernoulli) resolution, but bounded payoffs (no casino can pay an infinite prize), bounded utility, and probability weighting that compresses extreme tails each independently defuse the divergence. Reading concavity as the *only* answer mistakes one repair for the structural fact that any bound or sub-linear transform tames the tail.
- **Not evidence that real deciders are irrational.** The modest finite price is not a failure to compute the mean; it *reveals* a concave (or bounded) value function and is fully rational under it. The paradox's enduring move is inverse — "under what utility is this willingness-to-pay rational?" — which turns an apparent anomaly into evidence about the shape of preferences, the program von Neumann–Morgenstern systematized.
- **Not a demonstration that expected-value reasoning is generally wrong.** Expected-value maximization is inadmissible *only* when the payoff distribution's mean diverges or is fat-tailed. For well-behaved means it remains the right criterion; the lesson is to check whether the mean is even well-behaved before trusting an EV computation, not to abandon expectations wholesale.
- **Not a portable structural pattern.** It is a *named example* and pedagogical anchor for `expected_utility`, `risk_aversion`, and `heavy_tail` (one of a family with Allais, Ellsberg, Newcomb, Pascal's Wager). The transferable content — "an unbounded linear-in-payoff criterion mishandles fat tails; bound or concavify" — belongs to those primes; Pascal's Mugging in AI alignment is *that parent prime* recurring, not "the St. Petersburg paradox" arriving.

clarity:
The paradox's clarifying force is to drive a wedge between two quantities that pre-Bernoulli reasoning treated as one: *expected monetary value*, a property of the payoff distribution alone, and *expected utility*, a property of the agent's preferences over outcomes. By exhibiting a gamble whose expected money is infinite while its accepted price is a few ducats, it makes legible that the decision criterion cannot live in the distribution — no amount of recomputing the mean rescues expected-value maximization — but must incorporate how the agent values wealth. That separation is the hinge on which risk-sensitive economics turns: once "what the gamble is worth in money" and "what it is worth to a decider" are held apart, the concavity move (diminishing marginal utility of wealth) renders the gamble's *expected utility* finite, and the distinction between a risk-neutral agent maximizing expected money and a risk-averse one maximizing expected utility of a concave function becomes statable. The paradox thereby lets a practitioner stop asking "what is the gamble's expected value?" and start asking the sharper "under what utility function is this finite willingness-to-pay rational?" — which is the question the von Neumann–Morgenstern axiomatization later answered systematically.

It also makes a specific structural failure mode visible: *expected-value maximization is inadequate as a normative criterion precisely when the payoff distribution has a divergent or fat-tailed mean*. The clarity is in localizing the breakdown to the tail. A practitioner who has absorbed the paradox knows that the trouble is not arithmetic error but the linear-in-money scoring rule meeting a distribution whose mean is dominated by vanishingly probable enormous payoffs — and so knows where to look in any heavy-tailed decision problem, from catastrophic insurance to power-law venture returns, for the same pathology. The sharp question it licenses there is not "what is the expected payoff?" but "is the mean even well-behaved, and if not, what bound or concave transform should replace it?"

manages_complexity:
The paradox compresses an open-ended class of decision problems — every gamble, lottery, insurance contract, or investment with a divergent or heavy-tailed payoff distribution — into a single diagnosable failure and a single repair. Confronting such problems one by one, an analyst might try to patch each with an ad hoc rule (cap the payoff here, ignore the rare catastrophe there, distrust the arithmetic somewhere else). The paradox collapses that case-by-case scramble to one structural recognition: the trouble is never an error in computing the mean but the *linear-in-money scoring rule meeting a distribution whose mean is dominated by vanishingly probable enormous payoffs*. Once that is the named pathology, the analyst tracks just two things — is the payoff distribution's mean well-behaved, and is the value function linear in money? — and the qualitative verdict (expected-value maximization is or is not adequate here) follows without re-litigating each gamble.

The repair compresses just as sharply. Rather than a bespoke fix per problem, the paradox installs one parametric lever: replace the linear money-value with a concave (or bounded) utility, and the divergent expected *money* becomes a finite expected *utility*. The entire space of "how much should one pay for a heavy-tailed gamble?" reduces to the shape of a single function — its curvature indexing risk aversion, the gap between risk-neutral (linear) and risk-averse (concave) decision-makers becoming a one-parameter family rather than a zoo of unrelated attitudes. An analyst facing catastrophic insurance, power-law venture returns, or a Pascal's-Mugging-style unbounded claim reads the same two-step off the paradox: check whether the mean is well-behaved, and if not, ask which concave transform or bound restores a finite, decidable value — a high-dimensional menu of tail-risk problems folded onto one diagnostic and one tunable utility curve.

abstract_reasoning:
The St. Petersburg paradox licenses a compact set of moves in decision theory under risk, all organized around the wedge between expected monetary value and expected utility, and the tail pathology that opens it.

*Diagnostic (locate the breakdown in the linear-money rule meeting a divergent tail).* The defining move is to attribute the gap between an infinite expected value and a modest willingness-to-pay not to arithmetic error but to a *structural mismatch*: a value function linear in money meeting a payoff distribution whose mean is dominated by vanishingly probable enormous payoffs. The signature inference runs from a finite accepted price for an infinite-EV gamble to the conclusion that the agent's value function *cannot* be linear in money — it must be concave or bounded, because only a sub-linear valuation renders the gamble's expected *utility* finite while its expected *money* diverges. The reasoning thus moves from observed behavior (small price) to a hidden property of preferences (diminishing marginal utility of wealth), and the diagnostic generalizes: in any heavy-tailed decision problem the analyst looks first at the tail, asking whether the mean is even well-behaved before trusting any expected-value computation.

*Boundary-drawing (when expected-value maximization is an admissible criterion).* The paradox draws a sharp boundary on the scope of the expected-value rule: it is adequate as a normative criterion only when the payoff distribution's mean is well-behaved, and is ruled *inadmissible* precisely when the mean diverges or is fat-tailed. The move "maximize expected monetary value" is out of bounds for a gamble whose expectation is dominated by rare extreme payoffs, and no amount of recomputing the mean rescues it — the criterion itself must be replaced. This boundary also separates two quantities the analyst must never conflate: expected monetary value (a property of the distribution alone) versus expected utility (a property of the agent's preferences) — holding them apart is what makes the criterion question decidable, since the decision rule cannot live in the distribution but must incorporate how the agent values wealth.

*Interventionist / repair (restore a finite decidable value by concavifying or bounding).* The paradox installs a single parametric repair: replace the linear money-value with a concave or bounded utility, and the divergent expected money becomes a finite expected utility with a determinate willingness-to-pay. The move is predicted to work in proportion to curvature — a more concave utility yields a lower finite price — so the gap between a risk-neutral (linear) and a risk-averse (concave) decision-maker becomes a one-parameter family indexed by the utility function's shape rather than a collection of unrelated attitudes. The interventionist content is therefore a tunable lever: choose the concave transform or bound that restores a finite value, and the curvature of that choice *is* the agent's risk aversion. The analyst facing catastrophic insurance, power-law venture returns, or an unbounded-claim problem applies the same repair and reads the implied risk attitude off the transform required to tame the tail.

*Inverse / rationalization reasoning (recover the preference that makes observed behavior rational).* The paradox licenses an inverse move characteristic of utility theory: rather than asking "what is the gamble worth?", ask "under what utility function is this finite willingness-to-pay rational?" — inferring the agent's preferences from their accepted price. A logarithmic utility, for instance, assigns the gamble finite expected utility consistent with paying only a few units, so the observed behavior *reveals* a concave value function rather than contradicting rationality. This reasoning — from choice back to the preference structure that justifies it — is exactly the program the von Neumann–Morgenstern axiomatization systematized, and the paradox is its historical entry point: it converts a behavioral anomaly into evidence about the shape of the value function.

knowledge_transfer:
Within decision theory, economics, finance, and the philosophy of probability the St. Petersburg paradox transfers *as mechanism* — more precisely, as a diagnostic-and-repair and a pedagogical anchor — across the settings that share its tail pathology. The same recognition (a linear-in-money scoring rule meeting a distribution whose mean is dominated by vanishingly probable enormous payoffs) and the same repair (replace the linear value with a concave or bounded utility, whose curvature *is* the risk aversion) carry intact into the textbook introduction of utility theory, the risk-aversion debate (the canonical motivation for expected utility over expected value as the normative criterion), tail-risk finance (lottery, venture capital, catastrophic insurance — ill-behaved means forced through utility transforms), foundational debates about whether expected-value reasoning is binding when expectations diverge (Hájek's "Vexing Expectations"), and AI decision-systems design (Pascal's Mugging defenses against unbounded-expectation policies). The diagnostics carry with the vocabulary — the expected-monetary-value/expected-utility wedge, the divergent-mean boundary on the EV criterion, the concave/bounded repair, the inverse "under what utility is this price rational?" move — wherever an agent faces a heavy-tailed or divergent-expectation gamble. The von Neumann–Morgenstern axiomatization is the systematized form of the same inverse reasoning the paradox introduced.

Beyond decision theory the honest reading is the **shared-abstract-mechanism** case (B): the paradox is one *named example* and pedagogical entry point, not itself a portable structural pattern, and what travels is the cluster of primes it instantiates. The transferable content — "an unbounded linear-in-payoff criterion mishandles fat tails, requiring a concave or bounded transform" — is the content of `expected_utility` and `risk_aversion` (with `prospect_theory` as the behavioral refinement, and `concavity` and `fat_tail`/`heavy_tail` as the structural relations). Those primes are what genuinely recur across substrates, and the cross-domain lesson should be carried by them. The clearest case is Pascal's Mugging in AI alignment: it is *structurally the same problem* (a tiny probability times an enormous claimed payoff yielding unbounded expectation under linear scoring), but its solution is just the expected-utility/bounded-utility prime applied in a new substrate — so the recurrence there is the parent prime as a co-instance, not "the St. Petersburg paradox" arriving.

The home-bound cargo is essentially the specific gamble and its pedagogical framing: the coin-until-tails structure with geometrically declining continuation probability and proportionally growing payoff, the ducat denomination, the historical Bernoulli resolution, and the paradox's role as the textbook anchor for utility theory. Strip the coin/ducat vocabulary and the paradox dissolves into "a distribution with a divergent mean is not well-handled by linear value functions; bound or concavify" — a *methodological lesson*, not a structural object, and one already housed in the expected-utility family. So invoking "the St. Petersburg paradox" in another substrate is best understood as pointing to that family via its most famous illustration; the honest move is to name `expected_utility` / `risk_aversion` (or `heavy_tail`) for the actual content, reserving the paradox as the teaching example. It sits in a family of named decision-theory anomalies (Allais, Ellsberg, Newcomb, Pascal's Wager) that share this disposition — pedagogical anchors for the same underlying primes rather than primes themselves. One discipline travels usefully wherever a heavy-tailed decision problem appears and is the paradox's sharpest lesson: look at the *tail first* — ask whether the mean is even well-behaved before trusting any expected-value computation, and if it is not, replace the criterion (concavify or bound) rather than recompute it. That habit generalizes to any divergent-expectation problem, which is exactly why the example endures. Mechanism (as diagnostic-and-repair) within decision theory, parent-prime (`expected_utility` / `risk_aversion` / `heavy_tail`) recurrence beyond — the profile *Structural Core vs. Domain Accent* makes precise.