Word Superiority Effect

one_liner: The finding that a target letter is identified faster and more accurately inside a familiar word than in a non-word or alone, because an activated lexical whole feeds activation back down to its constituent letters within the stimulus window.

core_idea:
The word superiority effect is the experimental finding that a target letter is identified faster and more accurately when it appears embedded in a familiar word (e.g., the K in WORK) than when it appears in a pronounceable non-word of the same length (e.g., OWRK) or when presented in isolation. Established by Reicher (1969) and Wheeler (1970) using tachistoscopic forced-choice paradigms designed to rule out guessing from partial word knowledge, the effect demonstrates that letter identification is not strictly bottom-up: the simultaneous recognition of the containing word feeds back to facilitate identification of its constituent letters, so a part embedded in a recognized whole is more perceptually accessible than the same part in isolation or in an unstructured string.

The structural mechanism, formalized in McClelland and Rumelhart's (1981) interactive-activation model, involves top-down excitation flowing from an activated lexical representation to the letter level: when the visual system activates the entry for WORK strongly enough, that activation reinforces the letter detectors for W, O, R, and K simultaneously, raising the signal at the target letter above what bottom-up visual input alone provides. The effect requires that the perceiver hold a lexical entry for the context word — it does not occur for pronounceable non-words that lack entries — and it depends on the lexical activation rising fast enough within the brief stimulus presentation to exert feedback before masking cuts off processing. The effect's magnitude grows with reading expertise as lexical entries accumulate and activate more rapidly, and it diminishes in beginning readers who are still acquiring entries and in pure bottom-up models of perception where such feedback is architecturally impossible.

scope_of_application:
The word superiority effect lives within visual word recognition and its reading-diagnostic neighbours wherever a literate perceiver, a briefly-masked string, and a lexical level that can feed activation back to the letter level are all in play; its reach is bounded to that lexical substrate — the whole-facilitates-part mechanism travels across perception (to object-superiority and face-composite effects, which are co-instances of the *parent*, not transports of this effect) under `gestalt_principles` / top-down-perception, not under the named effect.

- **Visual word recognition** — the canonical home and empirical anchor for interactive-activation models and the cascade-versus-feed-forward architecture debate.
- **Reading instruction and dyslexia diagnosis** — the developmental emergence of the effect read as a marker of lexical-level processing taking over from sublexical decoding.
- **Letter-perception research** — the benchmark against which novel feature-level effects are judged, with the forced-choice paradigm guarding against partial-word guessing.

structural_signature:
**Sig role-phrases:**

- **the literate perceiver** — a reader holding lexical entries, the substrate the feedback requires
- **the briefly-masked stimulus** — a short tachistoscopic presentation cut off by a mask, the window in which feedback must arrive
- **the probed component** — a single target letter at a known position, whose identification is measured
- **the contrast conditions** — the target embedded in a familiar word vs. a pronounceable non-word vs. in isolation, the design that isolates the effect
- **the lexical-entry enabling condition** — the load-bearing requirement: a recognised whole (an existing entry) must be available; entry-less non-words get no boost
- **the in-time top-down feedback** — activation flowing down from the activated lexical entry to the letter detectors, but only if the entry fires fast enough before masking
- **the part-identification advantage** — the signature: the target read faster/more accurately inside the recognised whole, a part inheriting accessibility from the whole
- **the forced-choice guess control** — the methodological guard ruling out partial-word guessing, so the advantage reads as genuine facilitation
- **the architecture test** — the discriminating consequence: such downward feedback is impossible in a strictly feed-forward system, so the effect's presence decides between cascade/interactive and feed-forward models

what_it_is_not:
- **Not bottom-up serial reading.** The effect refutes the intuitive picture that one identifies the letters first and only then assembles the word: a letter is read *better* inside its word than alone. Letter and word recognition run in parallel, with the activated lexical level feeding activation back *down* to the letter level — so the part inherits accessibility from a whole being recognised at the same time.
- **Not "any surrounding context helps."** The facilitation requires a *recognised lexical whole*, not merely additional letters or a regular-looking string: a pronounceable non-word with no lexical entry (OWRK) gives no advantage, because there is nothing at the lexical level to send activation down. The decidable claim is specifically a recognised whole arriving in time, not vague "context."
- **Not statistical guessing from partial word knowledge.** The advantage is not the subject inferring the letter from the word they think they saw. The forced-choice (Reicher–Wheeler) paradigm exists precisely to rule out partial-word guessing, so a surviving advantage reads as genuine perceptual facilitation rather than inference from word knowledge.
- **Not the letters being intrinsically clearer in words.** The boost comes from top-down lexical activation reaching the letter detectors, not from anything about the letters' own legibility — the identical letter in an entry-less string gets no such reinforcement. The advantage is read back as evidence the lexical level engaged, not as the part being easier to see.
- **Not the substrate-free whole-helps-part pattern.** The object-superiority and face-composite effects are genuine co-instances of one structure — a recognised whole top-down-facilitates its parts when a holistic representation is available — but they run on object and face representations, not lexical entries, so they confirm the *parent* (`gestalt_principles` / top-down perception), not a transport of "word superiority." The lexical machinery — dictionary entry, letter detectors, masking window, reading-expertise gradient — is exactly what those domains replace, and the cross-domain lesson belongs to the parent.

clarity:
The effect's clarifying force is that it refutes the intuitive serial picture of reading — that one must identify the letters first and only then assemble the word. By showing a letter is read *better* inside its word than alone, it makes vivid that letter and word recognition run in parallel and that the lexical level talks *back down* to the letter level, so the part can inherit accessibility from a whole that is being recognized at the same time. Naming this turns the vague claim "context helps perception" into a precise, decidable one: not just any surrounding letters help, but specifically a *recognized lexical whole* does, and it helps by feeding activation downward within the stimulus window. This separates genuine top-down lexical facilitation from mere statistical guessing — the forced-choice paradigm exists precisely to show the advantage survives when partial-word guessing is ruled out.

The sharper question the effect licenses is a boundary one: *does the perceiver hold a lexical entry for the context, and does it activate fast enough to feed back before masking?* That single criterion explains the otherwise puzzling pattern of where the advantage appears and vanishes — present for words, absent for entry-less non-words, growing with reading expertise as entries accumulate and fire faster, weak in beginning readers, impossible in any strictly feed-forward architecture. A practitioner who has the concept can therefore read the *presence or magnitude* of the effect as a diagnostic of whether lexical-level processing has come online, rather than treating each result as an isolated curiosity about letters and strings.

manages_complexity:
Letter-perception research, before the effect was named, faced a scattered set of results about when a brief, masked letter can and cannot be reported: a letter alone is hard; the same letter inside WORK is easy; inside OWRK it is hard again; the advantage is large in a fluent adult, small in a six-year-old, absent in a beginning reader staring at an unfamiliar string. Without an organizing principle these look like separate facts about strings and stimulus durations, each inviting its own ad hoc account of why *this* arrangement helped. The word superiority effect compresses them onto a single yes/no question the analyst can ask of any condition: *is there a lexical entry for the surrounding context, and does it activate fast enough to feed back to the letter level before the mask cuts processing off?* That one criterion has two readable inputs — entry-exists and activates-in-time — and the qualitative outcome of every case reads off from them without re-deriving the perceptual machinery each time. Entry present and fast (a familiar word in a fluent reader): predict the part-identification advantage. No entry (a pronounceable non-word, however regular): predict no advantage, because there is nothing at the lexical level to send activation down. Entry exists but is slow to fire (a beginning reader's still-forming lexicon, or a masking interval too short for feedback to arrive): predict a weak or absent effect. And the developmental gradient — the advantage growing with reading expertise — is just the same criterion tracked over time as entries accumulate and come to activate faster.

The deeper compression is architectural. Rather than catalogue context effects empirically, the analyst can hold one structural commitment — bidirectional flow, with an activated whole exciting its constituent parts within the stimulus window — and generate the whole pattern of presences and absences from it, plus the boundary cases (no feedback possible in a strictly feed-forward system, so the effect becomes a discriminating test between cascade and feed-forward architectures). The "does context help perception?" question, which in the abstract has no determinate answer, becomes a parameterized one with a small number of tracked quantities: whether a recognized whole is available, and whether it comes online in time. What was a list of string-by-string curiosities collapses to a single feedback condition whose two inputs let an analyst predict, and read as a diagnostic of whether lexical processing has engaged, the presence and even the magnitude of the advantage.

abstract_reasoning:
The word superiority effect licenses reasoning organized around a single feedback condition — does an activated lexical *whole* reach down to excite its constituent letters within the stimulus window? — so the reading researcher reasons from two inputs, entry-exists and activates-in-time, to the presence and size of a part-identification advantage, and reads that advantage back as a probe of whether lexical processing has come online.

*Diagnostic (read whether the lexical level engaged, and rule out guessing, from the part-identification advantage).* The defining inference goes from a letter-report advantage back to the architecture that produced it. A target letter reported better inside WORK than inside OWRK or alone is read as evidence that a lexical entry was activated and fed activation down to the letter detectors — top-down lexical facilitation, not bottom-up acuity, because the same letter in an entry-less string gets no such boost. The effect's *magnitude* is itself diagnostic of lexical-level functioning: a large advantage indexes fast, well-formed lexical entries (fluent reading), a weak or absent one indexes a lexicon still forming (a beginning reader). Crucially, the inference is licensed only because the forced-choice paradigm rules out partial-word guessing — so the analyst reads a surviving advantage as genuine perceptual facilitation rather than as inference from word knowledge. The move runs part-report advantage → lexical activation reaching the letter level, never part-report advantage → the letters being intrinsically clearer.

*Interventionist (manipulate the context or the timing, predict the advantage to appear or vanish).* Because the advantage requires an activated whole feeding back in time, each manipulation has a forecast. Embed the target in a familiar word and the prediction is facilitation; swap to a pronounceable non-word with no entry and the prediction is that the advantage disappears, since there is nothing at the lexical level to send activation down. Shorten the masking interval below the time the lexical entry needs to fire and the prediction is that feedback arrives too late and the effect weakens or vanishes even for a real word. Push a reader's expertise up — accumulate entries that activate faster — and the prediction is that the advantage grows. Each intervention pairs a change in the context's lexical status or in the available feedback time with a predicted change in part-identification accuracy.

*Boundary-drawing (a recognized lexical whole, arriving in time — and a test between architectures).* The concept fixes its scope through a sharp criterion that turns the vague "context helps perception" into a decidable claim: not just any surrounding letters help, but specifically a *recognized lexical whole*, and only if it activates fast enough to feed back before masking. That boundary explains exactly where the advantage appears and where it does not — present for words, absent for entry-less non-words, growing with expertise, weak in beginners — and marks the regime where the effect's reasoning applies. The deepest boundary is architectural: such downward feedback is impossible in a strictly feed-forward system, so the presence of the effect becomes a discriminating *test* between cascade/interactive and feed-forward models of perception, and its absence under conditions where an entry should have fired is read as evidence against feedback in that case.

*Predictive / branch-ordering.* From the two inputs the analyst forecasts every condition before running it: entry present and fast yields the advantage; no entry yields none; entry present but slow (immature lexicon, or a mask too quick for feedback) yields a weak or absent effect — and tracked over development, the same criterion predicts the advantage emerging and growing as entries accumulate and come online faster. Presence, magnitude, and developmental trajectory all read off whether a recognized whole is available and whether it arrives in time.

knowledge_transfer:
Within visual word recognition the effect transfers *as mechanism*, because the single feedback condition it isolates — does an activated lexical whole reach down to excite its constituent letters within the stimulus window? — and its two inputs (entry-exists, activates-in-time) apply unchanged across the cases. It is the empirical anchor for interactive-activation models and the cascade-versus-feed-forward debate; reading-instruction and dyslexia work read the *developmental emergence* of the effect as a marker of lexical-level processing taking over from sublexical decoding; and letter-perception research uses it as the benchmark against which novel feature-level effects are judged. The vocabulary — *top-down lexical facilitation*, *interactive activation*, *lexical entry*, *feedback before masking*, *part-identification advantage* — and the forced-choice logic that rules out partial-word guessing carry intact across that cluster because the substrate is constant: a literate perceiver, a briefly-presented masked string, and a lexical level that can feed activation down to the letter level.

Beyond reading the most striking cases are the *near* ones, and they are best read as **shared abstract mechanism (B)**, not as transports of the word effect. The object-superiority effect (a component line identified better inside a coherent object than an incoherent one) and the face composite effect (a feature recognised better inside an upright whole face than inverted or in isolation) are genuine co-instances of one structure — *a recognised whole top-down-facilitates identification of its parts when a holistic representation is available* — but they run on object and face representations, not lexical entries. So they confirm that the general whole-helps-part mechanism recurs across perceptual domains; what they do *not* do is carry "word superiority" with them, because the lexical machinery (the dictionary entry, the letter detectors, the reading-expertise gradient) is exactly what those domains replace. The portable content is the parent, not this effect.

That parent is already in the catalogue: `gestalt_principles` (the whole as the primary perceptual object) and the broader top-down / hierarchical-perception family. The word superiority effect is one experimentally precise *instance* of that family, distinguished within it by the sharp commitment that the whole must be *recognised as familiar* (an entry must exist) and must activate in time — a commitment the bare gestalt claim does not make. So when the cross-domain lesson is wanted — "when a whole is recognised, its parts inherit accessibility from it via downward feedback" — it should be carried by `gestalt_principles` and the top-down-perception primes, **not** by "word superiority effect," whose distinctive cargo (letters, lexical entries, masking windows, the architecture test between cascade and feed-forward reading models) is reading-research furniture that does not travel. And further out — invoking "word superiority" for any case where context aids a part — risks **analogy (A)** that drops the load-bearing requirement of a *recognised lexical whole*: mere statistical co-occurrence or transient priming is not the within-stimulus top-down feedback the effect names, and the forced-choice paradigm exists precisely to keep the two apart. The clean boundary: literal transfer of the word superiority effect across visual word recognition and its reading-diagnostic neighbours; the whole-facilitates-part mechanism travels across perception (to object and face superiority) under its `gestalt_principles` / top-down-perception parent; and looser "context helps" usages are analogy unless a recognised whole is genuinely feeding back in time. (See *Structural Core vs. Domain Accent*.)


Working Memory

one_liner: Maintain a small, short-lived set of representations in an actively accessible workspace whose executive control can select, rehearse, transform, replace, and coordinate them for the task currently being performed.

status: draft

core_idea: Working memory is the cognitive architecture that keeps a small set of representations actively accessible while a person performs a task. Unlike passive short-term retention, it combines temporary maintenance with executive operations that select, rehearse, transform, reorder, replace, and coordinate the current contents. Its identity is the workspace, not the amount it can hold, the demand placed on it, or any one operation performed inside it.

scope_of_application:
- **Reasoning:** retaining premises while deriving and checking an intermediate conclusion.
- **Language:** maintaining earlier words while integrating a sentence or discourse.
- **Mental calculation:** preserving partial results while executing the next operation.
- **Planning:** holding goals, constraints, and candidate actions in an active task model.
- **Learning:** coordinating new material with retrieved long-term knowledge.
- **Executive control:** replacing stale contents as task relevance changes.

clarity: Working memory separates four neighboring questions that are often collapsed: What is the active workspace? How much can it hold? How much demand does a task impose? How are its contents updated? The architecture is Working Memory; the supply quantity is Working Memory Capacity, the demand is Cognitive Load, and real-time revision is Working-Memory Updating.

manages_complexity: The abstraction replaces a loose catalogue of remembering, focusing, rehearsing, and manipulating with one bounded active-content system. Analysis can then track the current representations, the control process selecting them, the operations applied to them, and the capacity or duration constraints that force competition and eviction.

abstract_reasoning: Working Memory supports state-tracking over an explicitly limited active set, supply-versus-demand comparison, and intervention reasoning about rehearsal, chunking, sequencing, and external offloading. It also licenses a boundary test: if a mechanism only selects an input, only stores it passively, or only measures a limit, it is related to the workspace but is not the workspace itself.

knowledge_transfer: The mechanism transfers within cognitive psychology, neuroscience, education, interface design, and human-factors work wherever a human active workspace is the explanatory object. Queues, caches, scratchpads, and organizational dashboards are useful analogies, but their cross-domain structure belongs to more general primes; they do not become Working Memory without the cognitive architecture and executive-control commitments.

notes: _(New domain-specific intermediate; queued for Claude style harmonization and independent citation review.)_


Working Memory Capacity

one_liner: The finite quantity of information a person can hold actively available for simultaneous manipulation — a small chunk-counted budget distinct from long-term and sensory memory — sized as supply against a task's demand, and attackable by chunking, offloading, or sequencing.

core_idea: Working memory capacity is the cognitive-psychology construct denoting the finite quantity of information that a person can hold *actively available* for simultaneous manipulation — distinct from long-term memory (slower to access but effectively unbounded) and from sensory memory (high-capacity but pre-attentive and lasting only milliseconds to seconds). The construct is operationalised through a family of complex span tasks — reading span (read a sentence, remember the last word, repeat across multiple sentences), operation span (solve an equation, remember a word), spatial span — that require concurrent storage and processing, distinguishing active holding-under-load from passive maintenance. The headline structural fact is the existence of a sharp upper bound: Miller's (1956) canonical estimate of 7 ± 2 items has been revised by Cowan (2001) and subsequent work to approximately 3–4 chunks under conditions that control for covert rehearsal, and further downward for individuals under cognitive load, stress, fatigue, or distraction. The operative unit is the *chunk* — whatever recoded unit the encoder has constructed from lower-level elements — so the bound is on the count of chunks, not on the raw information those chunks carry; expert chess players recalling board positions, expert radiologists reading scan patterns, and experienced pilots tracking instrument arrays all show performance that looks like high capacity but is in fact high chunking efficiency on familiar material, as Chase and Simon (1973) demonstrated. Baddeley and Hitch's (1974) multicomponent model decomposes working memory into a phonological loop (verbal material held via inner speech rehearsal), a visuospatial sketchpad (spatial and imagistic material), and a central executive (attentional control and cross-domain integration), with an episodic buffer added later; the central executive component is closely associated with fluid intelligence (Engle's executive-attention framework), explaining why working-memory-capacity scores are among the strongest cognitive predictors of fluid reasoning, reading comprehension, and mathematical performance. The capacity bound has direct design implications: educational instruction, interface design, clinical and aviation procedures, and surgical checklists all reduce task errors and improve performance when they are structured to keep the number of independently active chunks below the realistic capacity available for the target user population under realistic working conditions, using offloading (external displays, checklists), chunking (grouping low-level elements into higher-level units), and sequencing (scheduling capacity demands so they do not overlap) to keep operations within budget.

scope_of_application:
Working memory capacity lives across cognitive psychology and the applied human-facing fields that all re-import the same cognitive architecture — a finite active register, chunk-counted and executive-controlled; its reach stays within that substrate, since the substrate-neutral "finite active-processing register" pattern is carried by the parent primes (`attentional_capacity`, `cognitive_load`, `chunking`, `scarcity`, `bottleneck`), not by this construct.

- **Cognitive psychology and neuroscience** — the canonical home: span tasks, the Baddeley-Hitch and Cowan models, and the prefrontal correlates of the central executive.
- **Educational psychology** — the engine of Sweller's cognitive load theory, with its split-attention, redundancy, expertise-reversal, and worked-example effects.
- **Human-computer interaction** — sizing dashboard density, menu length, and form design to the chunks a user must hold at once.
- **Aviation, medicine, and nuclear human factors** — calibrating the operator-load envelope and motivating checklists, displays, and procedures that keep operators below capacity under high stakes.
- **Psychometrics and individual differences** — the link from capacity scores to fluid intelligence, reading comprehension, mathematical reasoning, and academic achievement.

structural_signature:
**Sig role-phrases:**

- **the active register** — the stage holding information *actively available* for simultaneous manipulation, distinct from unbounded-slow long-term memory and brief pre-attentive sensory memory
- **the chunk** — the unit the bound counts: whatever recoded grouping the encoder built, so the limit is on chunk count, not raw information
- **the multicomponent architecture** — the Baddeley-Hitch decomposition (phonological loop, visuospatial sketchpad, central executive, episodic buffer), with the executive coupling to fluid intelligence
- **the sharp upper bound** — a small ceiling on simultaneously active chunks (~3–4 controlling for rehearsal, below Miller's 7 ± 2), the supply term
- **the task load** — the count of independently active chunks a task forces to be held at once, the demand term measured against supply
- **the storage-manipulation trade-off** — manipulating items consumes the very capacity that would otherwise hold them, so concurrent processing shrinks the holdable count
- **the expertise-dependent chunk size** — the same nominal content costs fewer chunks for an expert, so effective budget differs by person and material (high "capacity" is really high chunking efficiency)
- **the baseline-shrinkage split** — capacity under good conditions versus the reduced figure under load, stress, fatigue, sleep loss, or ageing, the regime the design budget must use
- **the budget levers** — chunk (recode to fewer units), offload (move items to an external display/checklist), and sequence (schedule demands so they do not co-occupy slots) to keep demand under supply

what_it_is_not:
- **Not a limit on what one can know.** The bound is on the *actively held and manipulated* register, not on long-term memory (effectively unbounded, slower to access) or sensory memory (high-capacity but pre-attentive and fleeting). It bites only on information that must be kept simultaneously available for manipulation, not on total knowledge.
- **Not a bound on raw items or bits.** The unit counted is the *chunk* — whatever recoded grouping the encoder built — so the limit is on chunk count, not on the information each chunk carries. This is why expert performance that looks like enormous capacity is ordinary capacity operating on better-recoded units, and why the same person has a different effective budget on familiar versus unfamiliar material.
- **Not a fixed personal constant.** Baseline capacity under good conditions is distinct from the *shrinkage* imposed by cognitive load, stress, fatigue, sleep loss, or ageing. A designer who sizes to baseline overloads real users working degraded — the realistic budget must use the reduced figure for the actual operating regime.
- **Not cognitive load.** Capacity is the available *supply* of holding-and-manipulation resource; cognitive load is the *demand* a task places on it. Conflating them makes "is this too hard?" unanswerable; separating them turns it into an arithmetic comparison of demand against supply.
- **Not passive storage.** It is active holding *under manipulation*, distinct from pure maintenance — which is why complex-span tasks (process while remembering) measure it where simple span does not. Manipulating items consumes the very capacity that would otherwise hold them, so adding a processing demand shrinks the holdable count even with item count fixed.
- **Not the precise figure "7 ± 2."** Miller's canonical estimate was revised to roughly 3–4 chunks once covert rehearsal is controlled, and lower still under load. The headline structural fact is the *existence* of a sharp small bound, not any exact value, and the "seven items" rule cited in design is a loose inheritance.
- **Not a generic active register or attentional capacity.** Those broad patterns (a CPU register file, selective-bandwidth allocation) are carried by the parent primes. Working memory capacity's distinctive cargo is the human architecture — the chunk-as-unit, the storage-versus-manipulation distinction, the Baddeley multicomponent model, and the fluid-intelligence linkage — none of which a register or a message queue shares.

clarity:
Naming a design or performance constraint as a *working-memory-capacity* constraint disciplines the analyst to specify what a vaguer worry about "difficulty" leaves implicit: which chunks must be held *simultaneously*, what the chunk size is *for this user population* (so that effective capacity in task units is population-dependent — experts chunk larger, novices smaller), whether the holding interval involves manipulation or pure storage (since manipulating items consumes the very capacity that would otherwise hold them), and what offloading could move demand out of the head. It thereby converts an open-ended question — "is this too hard?" — into a budget question with a denominator, and it locates the failure precisely: not in motivation or intelligence but in the count of independently active chunks exceeding the available slots.

The construct's two sharpest clarifications follow from holding the right things apart. First, by pinning the bound to *chunks* rather than raw items, it explains why expert performance that looks like enormous capacity is in fact ordinary capacity operating on better-recoded units — so the sharper question is never "how much can this person hold?" in the abstract but "how is *this material* chunked for *this person*?", the same individual having different effective capacities on familiar versus unfamiliar content. Second, it separates the *baseline* capacity an individual brings under good conditions from the *shrinkage* imposed by cognitive load, stress, fatigue, sleep loss, or ageing — a distinction whose neglect is precisely how designers who size to baseline overload real users working under realistic conditions. Holding supply (capacity) apart from demand (the task's load) and baseline apart from degraded is what makes the budget computable rather than nominal.

manages_complexity:
A wide range of cognitive-design and performance failures — an instruction sequence that won't stick, a dense dashboard that breeds errors, a surgical or cockpit procedure that overloads its operator, a clinical visit in which a junior physician loses track of interacting conditions — arrive as a heterogeneous list of "too hard" problems, each apparently demanding its own diagnosis and its own fix. The working-memory-capacity construct compresses that list into a single budget question with a denominator: how many independently active chunks must be held *simultaneously* to do this task, and does that count fit the slots realistically available to this user population under these conditions? Once the task is posed that way, the analyst stops reasoning case by case about difficulty and instead tracks a supply quantity (available chunk slots) against a demand quantity (the chunks the task forces active at once), reading the predicted failure straight off whether demand exceeds supply. The same demand-versus-supply comparison predicts the qualitative outcome across lectures, interfaces, checklists, and high-stakes procedures, locating the failure not in motivation or intelligence but in an active-chunk count over budget.

The compression becomes computable rather than nominal only because two pairs of quantities are held apart, and those separations generate the branch structure the analyst works from. First, supply versus demand: capacity is the available holding-and-manipulation resource, load is what the task places on it — conflating them makes "is this too hard" unanswerable, while separating them turns it into an arithmetic comparison. Second, on the supply side, *baseline* capacity under good conditions versus the *shrinkage* imposed by load, stress, fatigue, sleep loss, or ageing — the neglected distinction by which a designer who sizes to baseline overloads real users working degraded. And the demand side is counted in *chunks*, not raw items, with chunk size set by expertise on the specific material: the expert's larger chunks lower the effective demand for the same nominal content, which is why performance that looks like high capacity is ordinary capacity operating on better-recoded units, and why the same person has different effective budgets on familiar versus unfamiliar material. Because the imbalance can be attacked from either side, the levers fall out directly and are chosen by which term is binding: shrink demand below the line by chunking (recode to fewer active units), offloading (move items to an external display or checklist so they leave the head entirely), or sequencing (schedule demands so they do not overlap and co-occupy slots); or respect a reduced supply by sizing to degraded rather than baseline conditions. The move is from an open-ended catalogue of "difficult" tasks to one supply-and-demand budget, counted in expertise-dependent chunks, with baseline held apart from shrinkage and a definite intervention attached to whichever side of the inequality is over the line.

abstract_reasoning:
Working memory capacity equips the cognitive-design analyst with a *budget* calculation and the inferences that hang off it, each turning on holding supply and demand apart. The *diagnostic* move reads a performance failure as an over-budget condition: a junior physician losing track of interacting medications, a dense dashboard breeding errors, an instruction sequence that won't stick, are inferred not as failures of motivation or intelligence but as the count of independently active chunks exceeding the available slots — and the analyst confirms the diagnosis by counting what must be held *simultaneously* against the slots realistically available to this population under these conditions. The *predictive* move runs the comparison forward: estimate the chunks a task forces active at once, estimate the slots the user brings, and predict failure where demand exceeds supply across lectures, interfaces, checklists, and high-stakes procedures alike — one inequality standing in for case-by-case judgments of "difficulty."

Three sharp inferences are specific to the construct and follow from how the two sides are measured. First, the *chunk-versus-item* move: because the bound is on chunks, not raw elements, and chunk size is set by expertise on the specific material, the analyst predicts that performance which looks like enormous capacity — the chess master reading a board, the radiologist reading a scan — is ordinary capacity operating on better-recoded units, so the same person is forecast to have a *different effective budget* on familiar versus unfamiliar content, and the operative question is never "how much can this person hold?" but "how is this material chunked for this person?" Second, the *storage-versus-manipulation* trade-off: because manipulating items consumes the very capacity that would otherwise hold them, the analyst predicts that a task requiring concurrent processing (the complex-span structure: solve while remembering) leaves fewer slots for storage than pure maintenance, so adding a manipulation demand is forecast to shrink the holdable count even with item count fixed. Third, the *expertise-reversal* prediction: a scaffold that reduces simultaneous in-head holding helps a novice but is forecast to *harm* an expert, because the expert's chunking has already done the offloading work and the scaffold now adds redundant material to process — so the analyst predicts the sign of a support's effect flips with the user's expertise.

The *interventionist* moves are chosen by which side of the inequality is binding, and the construct fixes the menu. To pull demand below the line: chunk (recode to fewer active units), offload (move items to an external display or checklist so they leave the head entirely), or sequence (schedule demands so they do not co-occupy slots). To respect a reduced supply: size to *degraded* rather than baseline conditions — and here the *baseline-versus-shrinkage* boundary does load-bearing work, since the analyst reasons that capacity sized to good conditions overloads real users working under load, stress, fatigue, sleep loss, or ageing, so the design budget must use the shrunk figure for the realistic operating regime. The *boundary-drawing* move fixes the construct's scope to the actively-held-and-manipulated stage: it bounds neither what one can know (long-term memory, effectively unbounded) nor what briefly registers (sensory memory, high-capacity but pre-attentive), so the analyst predicts the limit bites only on information that must be kept simultaneously available for manipulation, and reasons that demand which can be made sequential, externalized, or recoded into fewer chunks effectively exits the budget.

knowledge_transfer:
Within cognitive psychology the construct transfers as mechanism, across every setting that rests on the same human cognitive architecture. The supply-versus-demand budget, the chunk-as-unit (with expertise-dependent chunk size), the baseline-versus-shrinkage distinction, the storage-versus-manipulation trade-off, and the expertise-reversal prediction all carry intact. In **cognitive psychology and neuroscience** it is the canonical home — span tasks, the Baddeley-Hitch and Cowan models, prefrontal correlates. In **educational psychology** it is the engine of Sweller's cognitive load theory, with its split-attention, redundancy, expertise-reversal, and worked-example effects. In **HCI** it sizes dashboard density and menu length. In **aviation, medicine, and nuclear human factors** it calibrates the operator-load envelope and motivates checklists and displays. In **psychometrics** it links to fluid intelligence, reading comprehension, and mathematical reasoning. Across all of these the diagnostics and the design menu (chunk, offload, sequence, scaffold; size to degraded not baseline conditions) carry without translation, because the substrate — a human working memory with a central executive operating on chunks — is the same; the interface, meeting, and instruction "applications" re-import that one cognitive substrate, not a new one.

Beyond human cognition the honest reading is **shared abstract mechanism, not the named construct** — and the entry is unusually candid that the structural content WMC carries is *already housed elsewhere*. The general pattern — *a substrate has a finite active-processing register; tasks must fit; chunking enlarges effective capacity, offloading reduces demand, depletion shrinks the register under load* — is real and recurs in non-cognitive substrates (a CPU register file, IPC channel capacity, a meeting agenda's item count, an air-traffic controller's attended-flight count). But that pattern lives at the parent-prime layer: it is carried by `attentional_capacity` (selective allocation of fixed bandwidth), `cognitive_load` (demand against a finite budget), `chunking` (grouping low-level items into higher-level units), `scarcity` (finite resource under competing demand), `bottleneck` (the binding-constraint stage), and `cognitive_resource_depletion` (within-session shrinkage). Tellingly, the much-praised intervention catalogue — chunking into functions or departments or milestones, offloading to whiteboards or checklists or external state, sequencing in project and thread design, scaffolding in instruction and workflows — *travels because those broader primes are substrate-independent, not because working-memory capacity is*. What stays home-bound is the construct's unique cargo: the *chunk-as-unit*, the *storage-versus-manipulation* distinction, the *Baddeley multicomponent architecture* (phonological loop, visuospatial sketchpad, central executive), and the *individual-differences-as-fluid-intelligence* linkage. Stripped of that human architecture, what remains of working-memory capacity is just the active-processing-bandwidth pattern, already covered. So invoking "working-memory capacity" for a CPU register or a message queue is at best **loose metaphor** — the finite-register *shape* without the chunk-and-executive mechanism — and the substantive cross-domain lesson should carry the parent primes (`scarcity` / `attentional_capacity` / `bottleneck` / `chunking` / offloading), not "working-memory capacity" by name. The discipline is to carry those parents wherever a finite active register must be fit, and to reserve "working-memory capacity" for the human cognitive system whose chunk-counted, executive-controlled budget it actually measures (see *Structural Core vs. Domain Accent*).


Working-Memory Updating

one_liner: The executive function of monitoring a capacity-limited short-term store and revising its contents in real time — adding, replacing, transforming, and evicting items by relevance to the current task, distinct from passively maintaining them.

core_idea:
Working-memory updating is the cognitive executive function of actively monitoring the contents of a capacity-limited short-term store and revising those contents in real time as new task-relevant information arrives — adding incoming items, replacing stored items that have become outdated, transforming held representations, and evicting items whose relevance has lapsed — so that what is currently maintained reflects what the current task currently requires. Miyake and colleagues (2000) identified it as one of three separable executive functions alongside inhibition and set-shifting, confirmed by factor-analytic unity-and-diversity studies, and it is operationalised through n-back tasks, keep-track tasks, and letter-memory tasks that require participants to revise a maintained set rather than merely hold it.

The structural commitments are specific to the human cognitive-buffer system: a capacity of approximately four chunks, a second-by-second timescale governed by phonological and visuospatial subsystems, decay without rehearsal, and a dorsolateral prefrontal substrate indexed by neuroimaging and disrupted by frontal lesions. The updating function is logically distinct from passive maintenance (the mere retention of items over brief delays, which requires no revision), from inhibition (suppressing prepotent responses or intrusive representations), and from shifting (reallocating processing resources between task sets). Updating specifically requires an online monitoring process that evaluates the relevance of incoming information against the current task state and executes replacement or eviction decisions accordingly — the cognitive analogue of a cache manager that must balance currency of held information against capacity constraints, with the critical addition that relevance, not merely recency, governs eviction.

scope_of_application:
Working-memory updating lives across the subfields of cognitive psychology that assay the human cognitive buffer — second-by-second, with its phonological and visuospatial subsystems and dorsolateral-prefrontal seat; its reach is bounded to that substrate (the capacity-bounded-state-revision structure travels far further under its `buffering`, `state`/state-transition, and eviction-policy parents — to caches, routing tables, agent state, situational awareness — which the receiving disciplines already name differently).

- **Developmental psychology** — tracking the maturation of the updating function across childhood with n-back, keep-track, and letter-memory paradigms.
- **Aging research** — age-graded decline in dynamic-memory tasks read as a monitoring/revision rather than maintenance failure.
- **ADHD research** — updating deficits as a component of the executive-function profile, separable from inhibition and shifting.
- **Clinical neuropsychology** — frontal-lesion and disorder-linked updating impairment indexed against the same dorsolateral-prefrontal substrate.
- **Education** — working-memory-training and remediation interventions targeting the revision process rather than raw storage capacity.

structural_signature:
**Sig role-phrases:**

- **the capacity-bounded active store** — a short-term buffer of roughly four chunks, on a second-by-second timescale with phonological and visuospatial subsystems and a dorsolateral-prefrontal seat
- **the relevance-tagged arrival stream** — incoming items, each evaluated for task-relevance against the current task state
- **the monitoring process** — the online executive that assesses the ongoing relevance of held and incoming items against what the task currently requires
- **the update policy** — the revision step: add, replace, or transform held items so contents reflect current task needs (the active operation distinguishing this from passive maintenance)
- **the relevance-driven eviction** — the load-bearing commitment: items leave by *relevance*, not recency or decay, so a recently-arrived but now-irrelevant item can be dropped while an older relevant one is kept
- **the decay process** — the background erosion of held items without rehearsal, the maintenance failure mode held distinct from eviction
- **the executive separability** — the function set apart from inhibition and shifting by factor analysis, so a deficit localises to revision rather than to suppression or set-switching

what_it_is_not:
- **Not passive maintenance.** Updating is the *active revision* of buffer contents — adding, replacing, transforming, evicting — not the mere retention of items across a delay, which requires no revision. The two are separable: a person can hold four items well yet fail to swap a stale one for a fresh one, so a pure hold-over-delay task is outside the updating regime.
- **Not eviction by recency or decay.** What leaves the buffer is governed by *relevance to the current task state*, not by arrival order or the passage of time. A recently-arrived but now-irrelevant item can be the one dropped while an older, still-relevant item is kept — so "why did this item leave?" is answerable only inside the monitoring/relevance account, and a loss attributable to time alone belongs to decay instead.
- **Not undifferentiated "working memory" or "bad memory."** Updating is one of three executive functions separable by factor analysis from inhibition (suppressing intrusions) and shifting (reallocating between task sets). A dynamic-memory failure localises to a specific sub-process — revision, suppression, set-switching, or maintenance/capacity — rather than collapsing into a single monolithic store.
- **Not Bayesian belief updating.** Despite the shared word, this is capacity-bounded revision of *items* in an active buffer, not revision of *probability weights* over hypotheses under evidence. The constraints here are slots, decay, and task-relevance; the constraints there are priors and likelihoods, with no four-item buffer or eviction at all.
- **Not the substrate-free buffer-with-update pattern.** Stripped of the ~four-chunk capacity, the second-by-second phonological loop, and the dorsolateral-prefrontal seat, the residue — a maintained active state revised as relevant information arrives, with capacity-pressured eviction — simply *is* the `buffering` / `state` / eviction-policy family, which the receiving disciplines already name cache, scratchpad, or situation report. "Organisational working memory" is metaphor when it imports the cognitive specifics (no organisation has a four-item limit); the portable structure travels under the parent, not this construct.

clarity:
The decisive clarity updating brings is prying apart two things that the umbrella term "working memory" silently fused: the *passive maintenance* of items across a delay and the *active revision* of what is held. Before the function was isolated, a low score on a complex span task could implicate either — a person might fail because they cannot keep four items alive or because they cannot swap a stale item for a fresh one. By naming updating as a distinct executive function, separable by factor analysis from inhibition and set-shifting, the field gains a vocabulary in which "this task taxes maintenance" and "this task taxes updating" are different, testable claims. That is what lets the n-back and keep-track families be designed as updating assays specifically — they deliberately demand revision rather than mere retention — and it lets diagnostic taxonomies and training interventions target the revision process rather than the store's raw capacity.

It also sharpens the eviction question in a way that the storage view obscures. The clarifying insight is that what leaves the buffer is governed by *relevance to the current task state*, not simply by *recency* or decay: an item is dropped because monitoring has judged it no longer task-relevant, and a recently-arrived item can be the one evicted while an older, still-relevant one is kept. Holding "relevance-driven eviction" distinct from "decay" turns a vague sense that working memory "loses things" into the precise question a practitioner can now pose — *is this a monitoring/relevance failure or a maintenance/decay failure?* — which point to different deficits and different remedies.

manages_complexity:
The behavioral phenomena that updating is recruited to explain are heterogeneous: failures on n-back as the load climbs, the drift and intrusions of keep-track performance, the way a held set degrades when distractors arrive, age-graded and ADHD-linked decline in dynamic-memory tasks, the dissociation between people who can hold information and people who can revise it. Approached as a storage problem alone, each of these invites its own account — too few slots, too fast a decay, too weak a rehearsal loop — and the picture fragments into capacity stories that do not, by themselves, predict which task taxes a person and which does not. Updating compresses that field by recasting the buffer as a managed process with a small set of governing parameters an analyst can track: how many chunks the store holds at once (the roughly-four capacity), how fast held items decay without rehearsal, and — the parameter the storage view omits — what the monitoring process uses to decide what stays and what is evicted, namely *relevance to the current task state* rather than recency. Given those, the qualitative behavior of a task largely follows. The analyst reads off the branch directly: a task that merely demands retention over a delay loads maintenance and capacity; a task that demands swapping stale items for fresh ones loads the monitoring-and-revision process; and a given person's failure is diagnosable as one or the other rather than as a single undifferentiated "bad memory."

The deeper compression is that "working memory," an umbrella that silently fused holding-and-revising, is split into separable functions — updating set beside inhibition and shifting by factor analysis — so that complex, dynamic-memory behavior decomposes into a few executive contributions instead of one monolithic store. Within that decomposition the eviction question, which under pure decay has no determinate answer (why did *this* item leave and not that one?), becomes parameterized: track what monitoring has judged task-relevant, and the eviction reads off — a recently-arrived but now-irrelevant item can be the one dropped while an older, still-relevant one is kept. What would otherwise be a sprawl of task-specific capacity puzzles collapses onto a buffer-with-relevance-driven-revision model whose handful of parameters (capacity, decay, relevance-governed eviction, monitoring) let an analyst predict which tasks tax which sub-process and localize a deficit without re-deriving the cognitive architecture for each new paradigm.

abstract_reasoning:
Working-memory updating licenses reasoning that treats the short-term store as a *managed buffer* with a few governing parameters — capacity around four chunks, decay without rehearsal, and a monitoring process that evicts by *relevance to the current task state* rather than recency — so the cognitive scientist reasons from which parameter a task or person loads to the behavior, and back from a failure to the sub-process that produced it.

*Diagnostic (localize a deficit to monitoring/revision versus maintenance/decay).* The defining inference goes from a dynamic-memory failure back to the function that failed, refusing the undifferentiated "bad memory" reading. A low score on a task that demands swapping stale items for fresh ones is read as a *monitoring-and-revision* failure; a low score on a task that merely demands holding items over a delay is read as a *maintenance/capacity* failure — and because updating is separable from inhibition and shifting by factor analysis, an updating deficit is further distinguished from a failure to suppress intrusions (inhibition) or to reallocate between task sets (shifting). The eviction pattern is itself diagnostic: an item that left the buffer is read as having been judged no longer task-relevant, so observing that a *recently-arrived* item was dropped while an older one was kept signals relevance-driven eviction rather than decay. The inference runs task failure → the specific executive sub-process (and item-loss → relevance judgment), never task failure → "too few slots" by default.

*Interventionist (load a parameter or train the process, predict the effect).* Because the buffer's behavior is set by its parameters, manipulations have forecasts. Raise the rate at which task-relevant items arrive and demand revision, and updating load is predicted to rise, degrading performance for someone weak in the revision process specifically while leaving a strong-maintenance/weak-updating profile's mere-retention performance intact. Design an n-back or keep-track task and the prediction is that it assays updating rather than storage, because it deliberately requires revision; design a simple span task and the prediction is that it taxes maintenance. Target a training or remediation at the revision process and the prediction is improvement on updating-loaded tasks specifically, distinct from any change in raw capacity. Each manipulation pairs a change in which parameter is stressed with a predicted, sub-process-specific change in performance.

*Boundary-drawing (revision, not retention; relevance, not decay; the cognitive buffer's specifics).* The concept fixes its scope by three lines. It is logically distinct from passive maintenance — mere retention requires no revision, so a pure hold-over-delay task is outside the updating regime. It is governed by relevance, not recency or decay — so "why did this item leave?" is answerable only inside the monitoring account, where eviction follows a relevance judgment, and a loss attributable to time alone belongs to decay instead. And its quantitative commitments are those of the human cognitive buffer — roughly four chunks, a second-by-second timescale, phonological and visuospatial subsystems, a dorsolateral prefrontal substrate — which bound where the construct's parameters literally apply. Those boundaries tell the practitioner which deficits and remedies the updating account governs and which belong to maintenance, inhibition, or shifting.

*Predictive / branch-ordering.* From the parameter set the analyst forecasts task and person behavior before testing: a retention-only task loads maintenance and capacity; a swap-stale-for-fresh task loads monitoring-and-revision; load climbing past the roughly-four capacity predicts failure first where revision is also demanded; and eviction is predicted from what monitoring has marked irrelevant, so the item dropped is forecast by task-relevance rather than by arrival order. Which task taxes which sub-process, and which item leaves the buffer, both read off the buffer-with-relevance-driven-revision parameters.

knowledge_transfer:
Within cognitive psychology the construct transfers *as mechanism*, because the managed-buffer parameters (capacity ~four chunks, decay without rehearsal, relevance-governed eviction, online monitoring) and the separability of updating from inhibition and shifting apply unchanged across the subfields that use it. Developmental psychology, aging research, ADHD research, clinical neuropsychology, and education all assay the *same* function with the same paradigms (n-back, keep-track, letter-memory) and read the same diagnostic split — monitoring/revision failure versus maintenance/decay failure — off the same parameters. The vocabulary (*executive function*, *updating versus maintenance*, *relevance-driven eviction*, *monitoring*, *n-back load*) and the Miyake unity-and-diversity framing carry intact across that cluster because the substrate is constant: the human cognitive buffer, second-by-second, with its phonological and visuospatial subsystems and dorsolateral-prefrontal seat.

Beyond that cognitive substrate the entry is a clear **shared abstract mechanism (B)**, and the boundary is unusually clean because the general pattern is *already catalogued as primes*. What genuinely generalises is *capacity-bounded state revision* — a maintained active state, an arrival stream of relevance-tagged candidates, an update-and-eviction policy, a monitoring process — which the catalogue carries as `state` / state-transition, `buffering`, and the eviction-policy family. That pattern really recurs across substrates as co-instances: database transaction state, agent state and scratchpads in software, a BGP routing table updated from advertisements, an air-traffic controller's tracked sector picture, organisational situational awareness. So when the cross-domain lesson is wanted — "hold a bounded active state, revise it as relevant information arrives, evict by relevance under capacity pressure" — it is carried by the buffering / state / eviction primes, **not** by "working-memory updating," whose distinctive cargo (the ~four-chunk capacity, the prefrontal substrate, the dual-task interference signatures, the n-back and keep-track measures) is human-cognitive furniture that does not transfer. Tellingly, the receiving disciplines already name the shared content differently — cache, state, scratchpad, situation report — which is the surest sign that what travels is the parent pattern, not this construct.

The failure mode to mark is the metaphor that smuggles the cognitive specifics across. "Organisational working memory" or "interface working memory" are **analogy (A)** when they import the construct's measurement-and-substrate content: an organisation does not have a four-item limit, a second-by-second phonological loop, or a dorsolateral-prefrontal bottleneck, so those properties do not constrain it, and treating them as if they did over-reads the construct. The legitimate version of that move drops the cognitive specifics and keeps only the buffer-with-update structure — at which point it simply *is* the buffering/state parent, and should be named as such. The clean boundary, then: literal transfer of working-memory updating across the cognitive subfields wherever the human buffer is being assayed; and beyond cognition, the capacity-bounded-state-revision structure travels under its `buffering`, `state`/state-transition, and eviction-policy parents (to caches, routing tables, agent state, situational awareness), not under the named cognitive function. (See *Structural Core vs. Domain Accent*.)


Worse-than-Average Effect

one_liner: The reversal of self-enhancement on hard, expert-dominated tasks, where most people rate themselves below average because self-ratings anchor on one's own absolute perceived skill and ignore the distribution of others — the sign of the bias set by perceived task difficulty.

core_idea: The worse-than-average effect is the empirically observed reversal of self-enhancement bias: on tasks perceived as difficult, unfamiliar, or heavily skewed toward expertise — juggling, chess, programming, complex surgical procedures — people on average rate their own ability as *below* the population average, even though by definition the majority cannot be below the median. The effect is the direct counterpart to the better-than-average effect on easy tasks, and both arise from the same underlying mechanism: Kruger (1999) demonstrated that self-assessments are anchored primarily on one's own *absolute* perceived skill level, rather than on an accurate model of the *distribution* of others' skills. On an easy task where nearly everyone has some competence, anchoring on absolute self-assessed skill places most respondents above what they estimate the average to be; on a hard task dominated by a few clear experts, the same anchoring places most respondents below it. The distributional incoherence in both directions — more than 50% cannot simultaneously be above or below the median — is the diagnostic signature that the self-assessment process is not tracking the actual comparative distribution.

scope_of_application:
The worse-than-average effect lives across social and cognitive psychology and behavioural economics wherever a self-assessing agent rates itself against an imagined population on a difficulty-varying task; its reach is bounded to that self-assessment substrate (the underlying reference-class-neglect anchoring travels further under its `anchoring_bias` parent, and the within-family neighbours Dunning–Kruger and impostor syndrome make different claims).

- **Social psychology of self-assessment** — a canonical exhibit in the biased-self-evaluation literature, the difficulty-modulated obverse of the better-than-average effect.
- **Educational assessment** — systematic under-rating on objectively hard subjects (advanced calculus, organic chemistry), with downstream effects on course-taking and persistence.
- **Career self-selection** — under-application by competent candidates to skilled-technical domains where the imagined distribution is expert-dominated.
- **Comparative health-risk perception** — below-average self-ratings on rare, high-skill protective behaviours (e.g. recognising skin cancer).
- **Survey methodology** — a known comparative-judgment artifact requiring researcher correction.

structural_signature:
**Sig role-phrases:**

- **the self-assessor** — the person rating their own ability against others
- **the difficulty-varying task** — a task whose perceived difficulty is the one moderator that sets the sign (hard/expert-dominated here, vs. the easy case for the better-than-average twin)
- **the reference class** — the imagined population distribution the self-rating is supposed to be compared against
- **the absolute-skill anchoring** — the load-bearing mechanism: self-ratings anchor on one's own absolute perceived skill and largely ignore the distribution of others
- **the missing-distribution stage** — the localised defect: self-judgment splits into a roughly-accurate absolute-self read and an often-absent model of where others stand, and the bias lives in the second
- **the below-average reversal** — the signature on hard tasks: most people rate themselves below average, the obverse of the better-than-average effect
- **the population-incoherence diagnostic** — the tell that the reference class was never consulted: more than half cannot truly be below the median
- **the difficulty cross-over** — the unifying prediction: reframe a task harder or easier and the sign flips, the two effects one curve indexed by difficulty

what_it_is_not:
- **Not accurate modesty.** A sample in which most people rate themselves below average is not realistic self-knowledge: by definition the majority cannot be below the median, so the pattern is the population-incoherence signature of an absolute-anchoring process, not a population that genuinely "sells itself short." The below-average reversal is an artifact of how the comparison is made, not a true read of comparative standing.
- **Not a fixed, unidirectional self-evaluation bias.** Self-assessment bias does not always run one way: its *direction* is set by perceived task difficulty. Easy tasks yield over-rating (the better-than-average effect), hard tasks yield under-rating (this effect), and the two are one cross-over curve — so "people flatter themselves" is only half the picture, and the sign is predictable from the task.
- **Not a defect in self-knowledge of one's own ability.** The bias lives in the *missing model of where others stand*, not in the read of one's own competence, which is often roughly accurate. So the operative question is not "are people miscalibrated about themselves?" but "did the judgment consult a reference class at all?" — which is why supplying distributional information about others is the repair.
- **Not Dunning–Kruger, impostor syndrome, or regression to the mean.** It is the average *direction* of self-rating set by task difficulty across all competence levels — distinct from Dunning–Kruger's skill-quartile claim that the least competent overestimate most, from impostor syndrome's felt sense of fraudulence, and from regression to the mean's statistical artifact of noisy repeated measurement. These share the self-assessment neighbourhood but make different claims.
- **Not the substrate-free anchoring pattern.** Stripped of the self-rating paradigm and the population-incoherence diagnostic, the residue — a judgment anchored on the focal object's absolute properties that misses the comparative distribution — simply *is* `anchoring_bias`, of which this effect and its better-than-average twin are the difficulty-modulated obverse halves. The cross-domain lesson belongs to the parent; outside self-assessment, "worse-than-average" is just reference-class-neglect anchoring under a borrowed name.

clarity:
Recognizing the worse-than-average effect corrects the field's once-tidy story that self-assessment bias runs in a single direction — that "people flatter themselves." Naming the below-average reversal makes visible that the *direction* of the bias is not a fixed feature of self-evaluation but a function of perceived task difficulty: easy tasks yield over-rating, hard tasks yield under-rating. That reframing dissolves an apparent contradiction in the literature — over-confidence in some studies, under-confidence in others — that looked like inconsistent findings until the difficulty axis was made the organizing variable. Once it is, the two effects are seen as a single cross-over rather than two unrelated curiosities, and a researcher can *predict the sign* of the bias from a task's difficulty instead of measuring it case by case.

The deeper clarity is locating the defect precisely. The label points the analyst at the *anchoring* step: self-ratings track one's own absolute perceived skill and largely ignore the comparative distribution of others. This separates two things comparative-judgment surveys otherwise blur — a person's read of their own competence (often accurate) from their model of where others stand (often absent) — and shows the bias lives in the second, not the first. That makes the population-incoherence pattern (more than half claiming to be below the median) legible as a *diagnostic* that the comparative distribution was never consulted, and it sharpens the operative question for debiasing: not "are people miscalibrated about themselves?" but "did the judgment incorporate a reference class at all?" — predicting that supplying distributional information about others should pull both the easy-task and hard-task biases toward coherence.

manages_complexity:
The self-assessment literature, read superficially, is a contradiction: some studies find people grossly over-rate themselves (driving, getting along with others, common sense), others find them under-rate themselves (chess, surgery, programming), and the two bodies of results seem to license opposite generalizations about human self-knowledge — "people flatter themselves" versus "people sell themselves short." Catalogued effect by effect, every comparative-judgment domain becomes its own finding, and the sign of the bias has to be measured anew each time. The worse-than-average effect, taken together with its better-than-average twin, compresses that whole field onto a single mechanism with one continuous moderator the analyst can track: self-ratings anchor on one's *absolute* perceived skill and largely ignore the *distribution* of others, and the only thing that varies across tasks is perceived difficulty. Given a task's position on that one difficulty axis, the sign of the bias reads off directly — easy tasks (nearly everyone competent, the imagined distribution low-variance and high) place most self-ratings above the estimated average; hard tasks (a few clear experts dominating the imagined distribution) place most self-ratings below it. What looked like two unrelated curiosities, or an inconsistent literature, collapses to one cross-over curve indexed by difficulty, and the population-incoherence signature (more than half claiming to be below the median) is the same diagnostic in both regimes that the comparative distribution was never consulted.

The deeper compression is in *where* the defect is located, which turns a vague "people are miscalibrated" into a structured two-stage account the analyst can run. Self-judgment is split into an absolute-self read (often roughly accurate) and a distribution-of-others model (often simply absent), and the entire bias is assigned to the second stage. That decomposition lets a researcher do three things from a small set of tracked quantities — perceived task difficulty, whether a reference class was supplied — without re-deriving each domain: predict the sign of the bias before measuring it; predict that the bias flips as a task is reframed harder or easier (the cross-over); and predict the single intervention that should pull *both* the easy-task and hard-task biases toward coherence, namely supplying distributional information about others. The sprawl of direction-specific self-assessment findings reduces to one anchoring mechanism, one difficulty moderator, and a localized second-stage failure whose remedy is the same wherever the effect appears.

abstract_reasoning:
The worse-than-average effect licenses reasoning that treats comparative self-judgment as *anchoring on absolute perceived skill while ignoring the distribution of others*, with perceived task difficulty the one moderator that sets the sign — so the researcher reasons from a task's difficulty to the direction of the bias, and from a population-incoherence pattern back to a specific stage of the judgment that failed.

*Diagnostic (read the sign from difficulty; read the failed stage from incoherence).* The defining inference goes from a comparative-judgment result to the mechanism behind it. A sample in which most people rate themselves *below* average is read not as accurate modesty but as the absolute-anchoring process applied to a hard, expert-dominated task; the same process on an easy task is read as producing the better-than-average pattern — so the bias's *direction* is diagnosed from the task's difficulty rather than from any property of self-evaluation in general. The population-incoherence signature — more than half claiming to be below (or above) the median — is the diagnostic that the comparative distribution was never consulted, and it points the analyst at the second of two stages: self-judgment splits into an absolute-self read (often roughly accurate) and a distribution-of-others model (often simply absent), and the bias is localized to the latter. The inference runs result + task difficulty → absolute-anchoring on a hard task (and incoherence → "the reference class was skipped"), never result → "people sell themselves short."

*Interventionist (reframe difficulty or supply a reference class, predict the shift).* Because the sign is set by perceived difficulty and the defect by a missing distribution, both are levers with forecasts. Reframe a task as harder and average self-ratings are predicted to shift downward, crossing over from the better-than-average regime toward the worse-than-average one; reframe it easier and they shift upward — the cross-over is a manipulation, not just an observation. Supply distributional information about how others actually perform and the prediction is that *both* the easy-task and hard-task biases pull toward coherence, because the intervention repairs exactly the second stage the bias lives in. Each manipulation pairs a change in difficulty or in reference-class availability with a predicted, signed change in the comparative judgment.

*Boundary-drawing (the defect is the distribution stage, not the self-read; difficulty is the axis).* The concept draws two lines. It separates a person's read of their own competence (often accurate) from their model of where others stand (often absent), and assigns the bias to the second — so "are people miscalibrated about themselves?" is the wrong question and "did the judgment incorporate a reference class at all?" is the right one. And it makes difficulty the organizing axis that bounds where each direction of bias is expected: easy tasks over the cross-over yield over-rating, hard tasks under it yield under-rating, and the two are one curve rather than two unrelated effects. Those boundaries also separate the effect from neighbors — it is the average *direction* of self-rating by task difficulty, not the confidence-competence miscalibration of the least skilled, nor a felt sense of fraudulence, nor a statistical regression artifact.

*Predictive / ordering.* From a task's position on the single difficulty axis the analyst forecasts the sign of the bias before measuring it, and forecasts that the same population flips as the task is reframed across the cross-over; from whether a reference class was supplied, the analyst forecasts whether the incoherence will persist or resolve. Direction, the cross-over, and the debiasing response all read off difficulty plus reference-class availability, with the absolute self-read held fixed as the part the bias does not touch.

knowledge_transfer:
Within social and cognitive psychology and behavioural economics the effect transfers *as mechanism*, because the anchoring account (self-ratings track absolute perceived skill and ignore the distribution of others) and its single moderator (perceived task difficulty) apply unchanged across the cases. It is a canonical exhibit in the biased-self-evaluation literature; in educational assessment it predicts systematic under-rating on objectively hard subjects, with downstream effects on course-taking and persistence; in career self-selection it predicts under-application by competent candidates to skilled-technical domains; in comparative health-risk research it predicts below-average self-ratings on rare, high-skill protective behaviours; and in survey methodology it is a known comparative-judgment artifact requiring correction. The vocabulary (*better/worse-than-average cross-over*, *absolute-skill anchoring*, *reference class*, *population incoherence*, *difficulty moderator*) and the two-part prediction (read the sign off difficulty; repair the bias by supplying a distribution) carry intact across that cluster because the substrate is constant: a self-assessing agent rating itself against an imagined population distribution.

Beyond that self-assessment substrate the entry is a clean **shared abstract mechanism (B)**, and the parent is already in the catalogue. The genuinely substrate-spanning structure is not the worse-than-average effect but the anchoring pattern it instantiates — *a judgment anchored on absolute properties of the focal object misses the comparative distribution* — which the catalogue carries as `anchoring_bias`, and of which this effect and its better-than-average twin are the difficulty-modulated obverse halves (with `dunning_kruger_effect` the neighbouring miscalibration finding). So when the cross-domain lesson is wanted — "rating something by its own absolute attributes, without consulting the reference class, produces distributionally incoherent comparative judgments" — it should be carried by `anchoring_bias` (and the difficulty-modulated self-assessment family), **not** by "worse-than-average effect," whose distinctive cargo (the self-rating paradigm, the population-incoherence diagnostic, the specific better/worse cross-over indexed by task difficulty) is self-assessment furniture that does not travel. The effect's *interventions* — reframe difficulty to flip the sign, supply distributional information to restore coherence — are likewise specific to comparative self-judgment and do not port; the portable move is the anchoring-on-absolute-property insight the parent already states.

The boundary to mark is twofold. First, the cross-domain reach beyond psychology and behavioural economics is genuinely shallow: the effect needs a self-assessing agent with a reference class, so invoking "worse-than-average" outside that setting borrows the label for what is, structurally, plain reference-class-neglect anchoring — to be named at the parent, not as this effect. Second, the within-family neighbours must not be conflated with it: Dunning–Kruger (the least competent overestimate most, a skill-quartile claim) and impostor syndrome (a felt sense of fraudulence) are *different* claims from the worse-than-average effect (an average *direction* of self-rating set by task difficulty, across all competence levels), even though all sit in the self-assessment literature. The clean boundary, then: literal transfer of the worse-than-average effect across comparative self-assessment contexts wherever an agent rates itself against a population on a difficulty-varying task; and beyond that, the reach belongs to `anchoring_bias` and the difficulty-modulated self-assessment family it instantiates, not to the named effect. (See *Structural Core vs. Domain Accent*.)


name: Wrong Direction (Reverse Causation Fallacy)

one_liner: Catch the error of assigning a causal arrow A→B when it actually runs B→A on the strength of a symmetric association alone, by refusing any oriented claim until temporal precedence or interventional response fixes the direction.

core_idea: The wrong-direction fallacy — also called reverse causation or causal reversal, catalogued in informal logic as one reading of *non causa pro causa* — is the reasoning error of assigning a causal arrow from A to B when the actual causal direction runs from B to A, on the basis of an observed association alone. The error is a *direction error*, not an existence error: there is genuine causation in the system; the reasoner has simply oriented the arrow backwards. This distinguishes it from the other two classic interpretations of a correlation (common cause, where a third variable drives both; and chance, where there is no causal structure at all): wrong-direction concedes causation and concedes the correlation, but gets the which-way wrong. The structural mechanism of the error is that correlation is symmetric — A correlates with B exactly as strongly as B correlates with A — so an observed association carries no inherent signal about which variable is upstream without additional evidence. The additional evidence that can fix direction is of two kinds: *temporal precedence* (if B reliably precedes A in time, B is the more plausible upstream cause, though even this is defeasible) and *interventional response* (if intervening on A — holding it fixed or randomizing it — does not move B, while intervening on B does move A, the causal arrow runs B→A). Absent such evidence, the practitioner who concludes A→B from a positive correlation has committed the wrong-direction fallacy, and any policy built on A causes B will be ineffective or actively counterproductive. Canonical examples in argumentation and epidemiology include: the inference that diet soda causes obesity because obese people consume more diet soda (the obesity drives the soda choice, not the reverse); the inference that police presence causes crime because crime-heavy neighborhoods have more police (deployment follows crime, not the reverse); the inference that hospitalization causes death because death rates are higher in hospitals. In each case, the correlational evidence is real and the causation is real but the direction of inference is inverted.

scope_of_application:
Because the wrong-direction fallacy is a named *diagnostic* — a standing trap plus the two asymmetry tests (temporal precedence, interventional response) that escape it — rather than a causal mechanism, it applies wherever its minimal precondition holds: an oriented causal claim drawn from a symmetric association. That precondition is met across the whole empirical-science substrate, so the fields below are genuine literal uses of the identical diagnostic, not metaphor; what travels is the named trap, while the more general insight it crystallizes belongs to the parent primes `correlation` and `causality`.

- **Informal logic and argumentation theory** — the catalog home, where it is the stock fallacy filed under *non causa pro causa* / "wrong direction" (Walton, Govier) and taught as one of the three escape-hatches a correlation cannot rule out.
- **Epidemiology** — the bedrock motivation for prospective cohort designs and Mendelian randomization, instruments chosen precisely to fix the arrow that cross-sectional association leaves underdetermined.
- **Economics and policy analysis** — a recurring misdiagnosis (does loose money cause inflation or inflation loose money? does welfare cause unemployment or unemployment welfare uptake?), where a policy built on the reversed arrow is counterproductive, not merely inert.
- **Clinical reasoning** — the standing error of confusing a symptom-of-disease with a cause-of-disease when the two merely co-occur.
- **Quantitative social science** — psychology, sociology, and educational research, where the same residual error and the same two remedies apply unchanged to observational findings.
- **Network analysis and ML interpretability** — where attributing a directional influence from an observed co-variation invites the identical reversal, and the asymmetry probes are the same escape.

structural_signature:
**Sig role-phrases:**

- **the observed association** — a symmetric correlation between A and B, which carries no inherent which-way signal (A correlates with B exactly as strongly as B with A)
- **the inferred arrow** — the asserted causal claim A→B drawn from that association alone
- **the true arrow** — the actual direction B→A (or bidirectional): a *direction* error, not an existence error — causation and correlation are both conceded
- **the forced disjunction** — the exhaustive menu the association cannot adjudicate among (A→B, B→A, common cause, chance), with wrong-direction staking one cell as the standing trap
- **the temporal-precedence test** — if B reliably precedes A in time, B is the more plausible upstream cause (defeasible)
- **the interventional-response test** — moving the alleged cause should move the alleged effect while moving the effect leaves the cause unmoved; which intervention bites fixes the arrow
- **the suspended verdict** — where neither asymmetry test has run, the disciplined reading is "direction unknown," not a confident orientation
- **the asymmetric-stakes warning** — a policy built on A→B when truly B→A is not merely inert but counterproductive, so the unexamined-direction case is owed the probes most urgently

what_it_is_not:
- **Not the bare "correlation is not causation" warning.** That cruder lesson cautions against inferring *any* causal link from association, and so leaves untouched the reasoner who concedes a real causal relationship and still orients the arrow backwards. Wrong-direction picks out exactly that residual error — it grants the correlation *and* grants the causation, faulting only the which-way.
- **Not an existence error.** The reasoner has not hallucinated structure into noise; there is genuine causation in the system. The fault is one of *orientation*, not of asserting a link where none exists. This is what separates it from the chance reading of a correlation, where there is no causal structure at all.
- **Not confounding by a common cause.** A third variable driving both A and B is a different escape-hatch entirely: confounding leaves *neither* an arrow between A and B, whereas wrong-direction is a genuine A–B causal arrow pointing the wrong way. The remedy differs too — adjust for the third variable versus fix the orientation.
- **Not the *post hoc* fallacy.** Inferring "A caused B because A came first" reads causation out of temporal succession alone; wrong-direction reads the *wrong arrow* out of a symmetric association whose temporal order may be unclear or itself reversed. Where *post hoc* over-trusts sequence, wrong-direction has typically ignored sequence (and intervention) altogether.
- **Not as harmless as admitting ignorance.** Confidently asserting the reversed arrow is *worse* than conceding "direction unknown," not merely as bad: a policy built on "A causes B" when in truth B causes A is not inert but counterproductive, acting on the cause to move an effect that is actually upstream of it.
- **Not settled by temporal precedence alone.** That B reliably precedes A makes B the more plausible upstream cause, but the test is defeasible — anticipation, reverse-running mechanisms, and measurement lag can all break it. Temporal order *shifts* the arrow's probability; only interventional response, where available, more firmly fixes it.

clarity:
Naming the wrong-direction fallacy makes legible that *an observed association underdetermines causal direction even after causation has been granted*. The cruder lesson — "correlation is not causation" — warns only against inferring any causal link at all, and so leaves untouched the reasoner who concedes a real causal relationship and still confidently orients the arrow. Wrong-direction picks out exactly that residual error: it concedes the correlation and concedes the causation, faulting only the which-way. The reason it can do so is the symmetry of correlation itself — A correlates with B precisely as strongly as B correlates with A — so the association, however strong, carries no internal signal about which variable is upstream. The sharp question the label forces before any causal conclusion is drawn is therefore not "is there a relationship?" but "which way does the arrow run, and what besides the correlation tells me so?"

The fallacy also sharpens the taxonomy of what a bare correlation cannot rule out, by separating three claims an undisciplined inference fuses. Once an association is seen, the candidate explanations are forced apart: A→B, B→A, a common cause driving both, or chance — and wrong-direction names the specific failure of asserting the first when the truth is the second, distinct from the *existence* error of reading structure into noise and from the *third-variable* error of confounding. Holding these apart matters because they demand different remedies and carry different stakes: confidently inferring the wrong direction is worse than admitting the direction is unknown, because policy built on "A causes B" when in fact B causes A is not merely inert but can be counterproductive. The label simultaneously tells the practitioner what evidence *would* settle direction — *temporal precedence* (does the supposed cause reliably precede the supposed effect?) and *interventional response* (does moving the alleged cause move the alleged effect, while moving the alleged effect does not move the cause?) — converting a flat "X is linked to Y" into a set of asymmetry tests whose answers fix the arrow.

manages_complexity:
When a practitioner meets a fresh "X is linked to Y" finding, the interpretive space is wide and ill-defined: an association can be spurious, real-but-confounded, real-and-direct, or real-and-reversed, and within "real" the arrow can run either way or both — so the question "what does this correlation mean?" invites an open-ended hunt that, undisciplined, tends to collapse straight to the most readable story (the cause that comes first to mind becomes the cause). Naming the wrong-direction fallacy is one cut that tames that space. It does not try to settle the whole interpretation; it forces the otherwise-fused readings of a correlation apart into a fixed, exhaustive menu — A→B, B→A, common cause, chance — and stakes out one specific cell of that menu as a standing trap. The boundless question "is my causal story right?" is thereby compressed into a finite disjunction the data must adjudicate among, and the reasoner who has not consciously placed a finding into one of those four cells knows exactly what work remains. The sprawl of possible misreadings collapses to a small typology, and the wrong-direction label marks the one cell where causation and correlation are both conceded and only the orientation is wrong — the residual error that the cruder "correlation is not causation" leaves entirely untouched.

The deeper compression is that direction itself, which feels like it should be visible in a strong enough association, is shown to be invisible there in principle — and that one structural fact replaces case-by-case agonizing with a uniform procedure. Because correlation is symmetric, A correlating with B exactly as strongly as B with A, no amount of correlational evidence carries a which-way signal; the practitioner therefore never has to ask, for each new finding, whether *this* correlation happens to reveal direction, because the answer is always no. What the analyst tracks instead reduces to two asymmetry probes that the symmetric association cannot supply on its own: temporal precedence (does the supposed cause reliably precede the supposed effect?) and interventional response (does moving the alleged cause move the alleged effect, while moving the alleged effect leaves the alleged cause unmoved?). The arrow reads off the result of those probes — and where neither has been run, the disciplined verdict is "direction unknown," not a confident orientation. The branch structure carries the stakes: confidently asserting the wrong arrow is not merely as bad as admitting ignorance but worse, because a policy built on "A causes B" when in truth B causes A is not inert but counterproductive — so the typology does not merely sort errors, it ranks them, telling the practitioner that the unexamined-direction case is the one whose downside is asymmetric and therefore the one the probes are most urgently owed.

abstract_reasoning:
The wrong-direction fallacy licenses a set of moves in causal inference from observational data, all anchored to the fact that a symmetric correlation carries no which-way signal even after causation is granted. The signature *boundary-drawing* move is to refuse any oriented causal claim from association alone: before concluding "A causes B," the analyst asks "which way does the arrow run, and what besides the correlation tells me so?" — and where neither asymmetry test has been run, the disciplined verdict is *direction unknown*, not a confident orientation. This is the regime edge the concept enforces: correlation entitles the reasoner to "there is a relationship," and to nothing about its direction, so the analyst reasons from the absence of asymmetry evidence to a suspension of the directional claim rather than defaulting to the reading that comes first to mind.

The two *interventional/predictive* tests are the moves that actually fix the arrow, and each generates a checkable prediction. The interventional test reasons by manipulation asymmetry: *if* A→B, then holding A fixed or randomizing it should move B, while intervening on B should leave A unmoved; the reversed pattern — intervening on A does nothing to B, intervening on B moves A — diagnoses B→A. So the analyst predicts the result of an experiment before running it and reads the direction off which intervention bites: a randomized trial assigning diet soda versus sugar soda to non-obese subjects that shows no obesity effect refutes the soda→obesity arrow and locates the cause upstream in the obesity. The temporal test reasons by precedence: if B reliably precedes A in time, B is the more plausible upstream cause, so prospective and temporally-ordered data can discriminate direction where cross-sectional data cannot — and the analyst predicts that a cohort design will settle what a snapshot leaves underdetermined. Both predictions are defeasible (temporal precedence especially), so the analyst treats them as evidence that *shifts* the arrow's probability, not as proof, and prefers the interventional signal where the two are available.

A *diagnostic* move supplies a cheaper, prior check: read the candidate mechanism in each direction and ask which is more plausible. Confronting "police presence causes crime," the analyst reasons that deployment plausibly *follows* crime (resources sent where crime is high), which flips the proposed arrow on mechanistic grounds before any data are gathered; confronting "hospitalization causes death," that the sick are sent to hospitals, so illness is upstream of both. This generation of the reverse story — narrating how B could produce A — is the move that surfaces wrong-direction as a live possibility in the first place, converting an association into a competition between two oriented mechanisms. Finally, an *interventionist* move attends to the asymmetric stakes the fallacy carries: because a policy built on "A causes B" when in truth B causes A is not merely inert but counterproductive, the analyst reasons that the unexamined-direction case is the one whose downside is worst, and so treats the asymmetry probes as most urgently owed exactly where a costly intervention is about to be staked on the unverified arrow — directing scarce verification effort to the inferences whose reversal would do active harm.

knowledge_transfer:
The wrong-direction fallacy is a named *diagnostic* — a standing trap plus the two asymmetry tests that escape it — rather than a causal mechanism of its own, and its precondition is minimal: any oriented causal claim drawn from a symmetric association. That precondition is met across the entire empirical-science substrate, so the diagnostic transfers *literally and as the same thing* wherever correlational data are interpreted, not by analogy. In its catalog home, informal logic and argumentation theory (Walton, Govier), it is the stock fallacy filed under *non causa pro causa* / "wrong direction." But the place it actually bites is the observational sciences, and there the construct carries intact: in epidemiology it is the bedrock motivation for prospective cohort designs and Mendelian randomization (instruments chosen precisely to fix the arrow); in economics and policy it is the recurring misdiagnosis (does loose money cause inflation or inflation loose money? does welfare cause unemployment or unemployment welfare uptake?); in clinical reasoning it is confusing symptom-of-disease with cause-of-disease; in psychology, sociology, educational research, network analysis, and ML interpretability the same residual error and the same two remedies — temporal precedence and interventional response — apply unchanged. Across all of these the diagnostic question ("which way does the arrow run, and what besides the correlation tells me so?"), the suspended verdict where neither asymmetry test has run ("direction unknown"), and the asymmetric-stakes warning (a policy built on A→B when truly B→A is not inert but counterproductive) transfer without translation, because the structural fact underneath — correlation is symmetric and so carries no which-way signal — is substrate-independent.

This wide reach is exactly why the entry is *not* a prime, and the honest accounting is case (B): the thing that genuinely recurs across all those sciences is not "the wrong-direction fallacy" as a named rhetorical category but the more general structural insight that *a correlation underdetermines causal direction, which only temporal or interventional evidence can settle* — and that insight is already carried by the parent primes `correlation` (whose treatment explicitly lists reverse causation as one of correlation's escape-hatches, alongside common-cause and chance) and `causality`. The general pattern travels; the home-bound cargo is the *fallacy framing* — the label, its place in the three-escape-hatch taxonomy of informal logic, the *non causa pro causa* lineage, the rhetorical packaging as a "named pattern of bad reasoning." Those belong to fallacy taxonomy and do not themselves do work outside it. So the cross-domain lesson should be stated as the parent prime (correlation does not fix direction; demand precedence or intervention), with "wrong direction" understood as the argumentation-domain crystallization of that insight — the name a rhetorician gives the trap, useful for cataloguing and teaching, but adding no structural content beyond what `correlation` and `causality` already encode. The boundary to mark is therefore not instrument-versus-metaphor (the diagnostic is no mere metaphor anywhere its precondition holds) but instrument-naming versus mechanism-ownership: the cross-domain force is owned by the parent, the rhetoric-specific label is what is local. See *Structural Core vs. Domain Accent*.