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Salience

Prime #
1158
Origin domain
Psychology
Subdomain
perception and attention → Psychology

Core Idea

Salience is the bottom-up property by which some items in a field stand out from their surround prior to any deliberate allocation of attention. The defining structure is a comparison: at each position in the field, the item's local features — intensity, contrast, novelty, motion, semantic incongruity, magnitude — are scored against the statistics of the surround, and the result is a scalar saliency value. High-salience items capture processing capacity before goal-directed selection runs; low-salience items must be searched for. The pattern is fundamentally about signal-against-background: salience is not a property of an item in isolation but of an item relative to a contrast field, which is why the same item can be highly salient in one context and invisible in another.

The structural force comes from three commitments that survive across substrates. First, salience is bottom-up: it is computed from the stimulus and its surround, not from the receiver's current goals, which makes it dissociable from attention (the receiver's selective allocation) and from emphasis (the producer's deliberate act of foregrounding). Second, salience is relational: it depends on the surround, so the same nominal feature value can be highly salient or barely visible depending on context. Third, salience is competitive: under a limited-capacity processor, raising salience for one item lowers the effective salience of others, so the structure naturally produces winner-take-some dynamics. Together these three commitments make salience a recognizable structural target for intervention: change the surround or the local contrast, and you change which items get captured. The pattern is pure local-contrast relational structure — identical across perception and computing substrates — and imports no home vocabulary or normative load.

How would you explain it like I'm…

Pop-Out

Imagine a page full of black dots and one bright red dot. Your eyes jump to the red one without even trying — it just pops out. But it only pops because everything around it is different from it; if the whole page were red, the red dot would be plain again.

What Stands Out

Salience is how much something stands out from the stuff around it, before you even decide to look for anything. Things grab your attention when they're brighter, louder, moving, surprising, or just different from their neighbors. The important part is that it's about the difference, not the thing by itself: one red dot pops on a page of black dots, but the very same red dot would be invisible on a page of red dots. It also comes from the thing and its surroundings, not from what you happen to be searching for. And because you can only pay attention to so much, making one thing pop makes the others fade a little.

Standing Out From Background

Salience is the bottom-up property by which some items in a field stand out from their surroundings before you deliberately aim your attention. The structure is a comparison: at each spot, the item's features — intensity, contrast, novelty, motion, oddness, size — are scored against the statistics of the surround, giving a single 'how much it pops' value. High-salience items grab processing capacity before goal-directed searching even runs; low-salience ones must be hunted for. It's crucially relational — not a property of an item alone but of an item against its background — which is why the same thing can pop in one context and vanish in another. Note it's distinct from attention (your own choice of where to look) and from emphasis (a sender deliberately foregrounding something); salience is computed from the stimulus, not your goals. And because capacity is limited, it's competitive: raising one item's salience lowers everyone else's.

 

Salience is the bottom-up property by which some items in a field stand out from their surround prior to any deliberate allocation of attention. The defining structure is a comparison: at each position, the item's local features — intensity, contrast, novelty, motion, semantic incongruity, magnitude — are scored against the statistics of the surround, yielding a scalar saliency value; high-salience items capture processing capacity before goal-directed selection runs, while low-salience items must be searched for. The pattern is fundamentally signal-against-background: salience is not a property of an item in isolation but of an item relative to a contrast field, which is why the same item can be highly salient in one context and invisible in another. Three commitments give it structural force across substrates. First, it is bottom-up: computed from the stimulus and its surround, not the receiver's goals, which makes it dissociable from attention (the receiver's selective allocation) and from emphasis (the producer's deliberate foregrounding). Second, it is relational: it depends on the surround, so the same nominal feature value can be highly or barely salient by context. Third, it is competitive: under a limited-capacity processor, raising salience for one item lowers the effective salience of others, producing winner-take-some dynamics. Together these make salience a recognizable target for intervention — change the surround or the local contrast and you change which items get captured — and the pattern is pure local-contrast relational structure, identical across perception and computing substrates, importing no home vocabulary or normative load.

Structural Signature

the field of candidate items — the feature space carrying contrast — the surround statistic defining what is typical — the local-contrast operator — the scalar saliency value — the limited-capacity competition rule — the adaptation dynamic eroding sustained signal

A field exhibits salience when each of the following holds:

  • A field of candidates. Multiple items occupy positions in some field, competing for downstream processing capacity.
  • A feature space. Items carry values along dimensions — intensity, contrast, novelty, motion, magnitude, semantic fit — that can differ from their neighbours.
  • A surround statistic. At each locus the pattern computes what is typical of the surround; salience is defined against this baseline, not against an absolute.
  • A local-contrast operator. A rule scores each item by how its features deviate from the surround statistic, yielding a scalar bottom-up saliency — computed from the stimulus, not the receiver's goals.
  • A competition rule. Under limited capacity the saliency values compete, so raising one item's salience lowers others' effective salience; the quantity is differential by construction.
  • An adaptation dynamic. A sustained high-salience signal erodes its own salience as the surround statistic adapts to include it.

The components compose a relational, competitive, adapting computation: features scored against a surround and combined under capacity limits, so "standing out" is a property of an item relative to its context and its competitors, not an intrinsic feature value.

What It Is Not

  • Not attention. attention is the receiver-side selective allocation of limited capacity, driven partly by goals. Salience is the stimulus-side bottom-up score computed before goal-directed selection runs. A goal-relevant item can be attended despite low salience; a high-salience item can be suppressed by top-down control.
  • Not emphasis. emphasis_focal_point (and emphasis) is the producer-side deliberate act of foregrounding. Salience is the property that results from contrast against a surround — a producer raises salience, but salience also arises from unintended stimuli the producer never emphasized.
  • Not processing fluency. processing_fluency is the ease with which an item is processed once attended — driving liking and truth judgments. Salience governs whether an item is captured in the first place; a fluent item can be low-salience, and a salient item can be disfluent.
  • Not priming. priming is a temporal carry-over: prior exposure pre-activates related representations. Salience is a spatial-relational contrast computed against the current surround, with no requirement of prior exposure — indeed novelty (the opposite of prior exposure) raises it.
  • Not an intrinsic feature value. Salience is relational by construction: the same nominal feature (a red button, a loud tone) is highly salient against a quiet surround and invisible against a saturated one. Treating conspicuity as a fixed attribute drops the load-bearing variable.
  • Common misclassification. Reasoning "I made it salient, therefore they will attend to it." If a motivated viewer fixates the item but does not act, the deficit is top-down override, not low salience — the fix is relevance framing, not more contrast.

Broad Use

Salience, read as signal-against-background, recurs across an unusually wide range of substrates. In vision and perception, visual saliency maps predict where eyes fixate from feature-contrast across color, orientation, motion, and luminance. In neuroscience, the salience network detects behaviorally relevant signals — interoceptive, emotional, or external — and gates switching between resting and executive networks. In behavioral economics, salience theory shows that choice can be reshaped by which attributes are made salient, with the salient attribute over-weighted. In linguistics, information structure marks which constituents are discourse-salient, driving anaphora resolution. In advertising and UI design, signage and calls-to-action are engineered to be high-salience against the surround to capture pre-attentive processing. In computing and machine learning, saliency maps support explainability, anomaly detection asks "what's locally unusual against the surround?", and keypoint detection and feed ranking exploit the same score. In memory and learning, distinctive or surprising events are preferentially encoded, the isolation effect. In security and monitoring, alarm design is the engineering of salience against background events, and alarm fatigue is its collapse under uniformly high signal. And in ecology, predator detection of prey against background — broken by camouflage, exploited by warning coloration — is the same computation.

Clarity

Naming salience as its own structural quantity separates three things that surface vocabulary persistently blurs. Salience is the stimulus-side bottom-up signal-against-background score; attention is the receiver-side selective allocation of limited capacity; emphasis is the producer-side act of designing items to be salient. These are three distinct moments in the same pipeline: a producer can raise salience, salience can capture attention, and attention can override salience for goal-directed reasons. Treating them as one concept loses the leverage points — a designer who confuses "make it salient" with "make them attend to it" cannot reason about top-down override, and a researcher who confuses salience with emphasis cannot study involuntary capture by unintended stimuli. A second clarifying move: salience makes visible the relational character of standing out. Asking "is the warning sign salient?" is incomplete; the question is "salient against what surround?" This single move dissolves a long list of design failures — alarms that lose salience under high background-alarm load, chart elements that disappear against a busy dashboard, species that vanish against a noisy baseline.

Manages Complexity

Salience compresses an enormous range of "stands-out" phenomena into a small operational schema: a feature space (what dimensions can carry contrast), a surround statistic (what's typical here), a contrast operator (how locally unusual is this), and a competition rule (how candidates combine into a single saliency landscape under limited capacity). Once the schema is named, otherwise-unrelated phenomena — pop-out search, anomaly detection, headline design, predator-prey camouflage, alarm engineering — collapse onto the same axes. The intervention vocabulary becomes portable: change the surround, change the contrast operator, or change the feature space, and you change which items become salient. The complexity managed is the apparent unrelatedness of these phenomena: rather than a separate theory of conspicuity for vision, for dashboards, for alarms, and for camouflage, there is one four-part schema that applies to all of them, so an analysis built in one domain transfers as a set of named handles to any other.

Abstract Reasoning

Treating salience as the unit enables a family of substrate-independent moves. The anomaly-detection isomorphism: any system that asks "what's locally unusual?" is computing a saliency map, so techniques from one — background subtraction, statistical-process-control charts, autoencoder reconstruction error — port to others. The adaptation argument: a sustained signal that was once salient erodes its own salience as the surround statistic adapts, the structural reason that alarms with steady-state false-positive load lose their grip, that shocking design choices habituate, and that sensory neurons show response decay. The camouflage/conspicuity duality: an agent that wants to evade detection minimizes signal-against-background while an agent that wants to be detected maximizes it, two operations on the same saliency function with opposite signs, which is why deception and signaling theories share machinery. And the competition consequence: raising salience uniformly raises nothing, since salience is differential by construction, so universal emphasis is equivalent to no emphasis — the alarm-fatigue and everything-in-bold result. The reasoner who holds the prime therefore treats "make it stand out" as a relational, competitive, adapting computation rather than an absolute property, and reaches for the surround and the contrast operator as the real levers.

Knowledge Transfer

Because salience is substrate-clean local-contrast structure — feature space, surround statistic, contrast operator, competition rule, adaptation dynamic — an insight found in one substrate transfers to another by re-identifying those roles, and the prime's reach is the reach of that re-identification. The vision research on feature-contrast that drives visual pop-out transfers to dashboard design, predicting which elements get noticed and which lose grip under busy backgrounds through surround-normalization. Salience theory in choice transfers to policy disclosure design: making true costs salient by removing distractors — point-of-purchase calorie labels, salient opt-outs — shifts choice predictably, with the structural prediction that what is made salient dominates what is most relevant. The salience network's gating-on-behavioral-relevance function transfers to alert-system design as a template for when monitoring should switch between background and central-executive modes. The isolation effect transfers to instructional design, advertising recall, and exception-marking in documentation, since distinctive items in a uniform list are preferentially remembered. Adaptation and habituation transfer to alarm policy, predicting why high-false-positive alarms lose effective salience and pointing to the intervention family — raise the threshold, change the modality, vary the surround. And camouflage/conspicuity reasoning transfers from biology to operational concealment and steganography on one hand and to advertising and warning design on the other. In every transfer the practitioner runs the same diagnosis — name the field of candidates, the feature dimensions, the surround statistic, the contrast operator, and the competition rule, then check the adaptation dynamic — and the transfer holds because none of these steps mentions the substrate: a radiologist whose lung nodule pops against clean anatomy but vanishes against scarring and an air-traffic controller whose lone aircraft is salient against an empty sector but lost in rush-hour traffic are facing the same relational computation, and in both the fix is to change the surround or the feature space rather than to "look harder."

Examples

Formal/abstract

The Itti-Koch computational saliency map makes every role of the prime explicit as an algorithm and exposes its relational, competitive, adapting character. The field of candidates is every pixel location in an image. The feature space is a fixed set of dimensions — luminance, red-green and blue-yellow color opponency, and local orientation — each computed at multiple spatial scales. The surround statistic and the local-contrast operator are realized together by center-surround difference operators: for each feature, a fine-scale ("center") response is subtracted from a coarse-scale ("surround") response, so a pixel scores high only where its feature value departs from the average of its neighborhood, never on its absolute value. This is the relational commitment made arithmetic — a bright patch in a bright field produces near-zero contrast, while the same bright patch on a dark field produces a large one. The competition rule is a normalization operator that suppresses feature maps containing many comparable peaks and promotes maps with a single strong peak, so the maps combine into one master saliency landscape under an implicit capacity limit; uniformly raising every location's feature value raises the final map nowhere, because the normalization is differential by construction. A winner-take-all network then reads off the most salient location, and crucially applies inhibition of return — the just-selected peak is transiently suppressed so attention can move on. That suppression is the adaptation dynamic: a sustained winner erodes its own salience. The diagnosis this model enables is concrete. If a target fails to pop out, the model says either its feature contrast against the local surround is low (camouflage), or many distractors share its feature value so normalization suppresses the whole map (conjunction search), and the intervention is to add a unique feature dimension or thin the distractor set — change the surround or the feature space, not the search effort.

Mapped back: The saliency map is the prime made executable — center-surround operators are the surround-statistic-plus-contrast-operator, normalization is the limited-capacity competition rule, and inhibition of return is the adaptation dynamic — so its pop-out failures are exactly the prime's relational and competitive consequences rather than failures of attention.

Applied/industry

Two unrelated applied domains — clinical alarm design in an intensive-care unit and anomaly detection in a fraud-monitoring pipeline — run the identical computation. In the ICU, the field of candidates is the stream of physiological events; the feature space includes heart-rate excursion, oxygen-saturation drop, and rate-of-change; the surround statistic is the patient's recent baseline and the ambient rate of alarms on the unit. The contrast operator scores how far a reading deviates from that baseline, and a well-designed alarm fires when contrast is high. The pathology the prime predicts is alarm fatigue: when the surround is itself saturated with alarms — every monitor beeping — the effective salience of any one alarm collapses, because salience is differential, and clinicians habituate as the adaptation dynamic folds the steady alarm stream into the expected surround. The intervention the prime hands the designer is not "tell staff to pay attention" but to change the surround and the contrast operator: raise thresholds so only genuine deviations fire, vary the alarm modality so a critical event contrasts against routine ones, and suppress redundant alarms so the background is quiet enough for a true signal to stand out. The fraud pipeline maps cleanly: candidates are transactions, the feature space is amount, velocity, geolocation, and merchant category, the surround statistic is the cardholder's own historical pattern plus the population baseline, and the contrast operator (a reconstruction error or local-outlier score) flags transactions that deviate. The same adaptation pathology appears — if the model's baseline drifts to include a fraudster's slow ramp, the activity stops being salient against its own surround — and the same fix applies: re-anchor the surround statistic and add a feature dimension on which the fraud is locally unusual. Both designers are tuning a saliency computation, and in both the lever is the surround, not the receiver.

Mapped back: ICU alarms and fraud detection both instantiate field, feature space, surround statistic, contrast operator, and competition rule, and both fail through the same differential-and-adaptation logic (fatigue, baseline drift), so the prime's intervention — change the surround or the feature space — transfers between medicine and finance unchanged.

Structural Tensions

T1 — Bottom-Up Salience versus Top-Down Attention (scopal). Salience is computed from the stimulus; attention is allocated by the receiver's goals, and the two can disagree. Where a goal-relevant item is low-salience and an irrelevant one pops, top-down control must override capture — and salience stops being the whole story. The failure mode is a designer reasoning "I made it salient, therefore they will attend to it," ignoring that a motivated viewer suppresses even strong signals. Diagnostic: ask whether the observed neglect is a salience deficit or an override. Eye-tracking that shows fixation-without-action implicates top-down suppression, not low salience; the lever then is relevance framing, not contrast.

T2 — Relative versus Absolute Standing-Out (scalar). Salience is a property of an item against its surround, never an intrinsic feature value. The failure mode is treating conspicuity as a fixed attribute — "the red button is salient" — and being surprised when it vanishes against a red-heavy dashboard or a high-alarm ward. Reasoning about the item in isolation drops the load-bearing variable. Diagnostic: always complete the sentence "salient against what?" If a design specifies the item's features but not the surround statistics it will sit in, its salience is unpredictable; measure the contrast against the actual deployment background, not a blank canvas.

T3 — Sustained Signal versus Adaptation (temporal). A high-salience signal erodes its own salience as the surround statistic adapts to include it; what stood out at first folds into the expected baseline. The failure mode is engineering a strong initial signal and assuming its grip persists — the permanent banner, the always-on alarm — only to watch habituation hollow it out. Diagnostic: ask how the signal's effectiveness decays over repeated exposure, not just at first sight. If a stimulus has been constant long enough to enter the surround, its measured salience is near zero regardless of its raw intensity; restoring grip requires novelty or modality change.

T4 — Raising One versus Raising All (coupling). Salience is differential under limited capacity, so emphasizing everything emphasizes nothing — uniform contrast cancels. The failure mode is the everything-in-bold dashboard or the alarm system where every event is "critical," producing zero effective prioritization. The competition rule couples items: lifting one's salience necessarily depresses others'. Diagnostic: count how many items are simultaneously marked high-priority. If the salient set is large relative to capacity, the design has spent its salience budget into noise; the fix is to suppress competitors, not amplify the target further.

T5 — Detection versus Concealment (sign/direction). The same saliency function runs in both directions: a signaller maximizes signal-against-background while an evader minimizes it, and a designer must know which sign they are on. The failure mode is optimizing for conspicuity when the operational goal is concealment (or vice versa), or failing to anticipate an adversary running the function in the opposite direction — camouflage defeating an alarm, steganography hiding a payload. Diagnostic: identify whether the system wants its items found or hidden, and whether an opponent is optimizing the opposite. A detection design that ignores an adversary minimizing salience will be systematically blind to deliberately low-contrast threats.

T6 — Feature-Contrast versus Semantic Relevance (measurement). Bottom-up salience scores perceptual contrast — intensity, motion, color — but the item that matters may be semantically critical yet perceptually bland. The failure mode is a system that reliably surfaces what is loud rather than what is important, capturing attention with high-contrast irrelevance while a quiet but consequential signal passes. Diagnostic: check whether the feature dimensions the salience operator scores actually correlate with the relevance the task cares about. If the most salient items are not the most relevant, the feature space is mis-chosen, and the fix is to add a feature dimension on which the relevant signal is locally unusual — not to raise contrast on the wrong axis.

Structural–Framed Character

Salience sits at the structural end of the structural–framed spectrum. Despite a home discipline in perception and attention, the pattern it names — a bottom-up, relational score by which an item stands out from its surround — is pure local-contrast structure, and on every diagnostic it reads structural, matching the frontmatter's all-zero criteria and aggregate of 0.0.

Walking the five diagnostics with this prime's substrates: vocabulary travels freely. The same surround-statistic-plus-contrast-operator computation is told in saliency maps and center-surround difference in vision, in the salience network's gating in neuroscience, in attribute over-weighting in behavioral economics, in discourse-salient constituents in linguistics, in reconstruction error in anomaly detection, and in predator-against-background in ecology — each substrate names the parts in its own words, importing no "psychology of attention" lexicon. Evaluative weight is absent: standing out is neither good nor bad until you specify the task, which is why the identical function describes a warning sign engineered to be conspicuous and a predator's careless motion that betrays it. Institutional origin is formal — the structure is fully stated as features scored against a surround statistic under a capacity-limited competition rule, with no appeal to norms, roles, or human institutions. It is not human-practice-bound: it runs indifferently in sensory neurons, in camouflaged prey, in machine-learning saliency maps, and in fraud-detection pipelines, none mediated by a human practice. And invoking it recognizes a pattern already present rather than importing a frame — to say an item is salient is to assert a measurable local contrast against its surround, testable by changing that surround, not to overlay an interpretation. Every diagnostic points the same way, and the prime is structural without qualification.

Substrate Independence

Salience is about as substrate-independent as a prime can be — composite 5 / 5 on the substrate-independence scale. Its signature — a signal standing out against a background by contrast along some dimension, capturing limited processing capacity bottom-up — is stated cleanly in relational terms with no commitment to any particular medium, so it is recognized rather than translated when it appears in a new field. Its domain breadth is maximal: the same figure-against-ground structure operates with identical force in visual and auditory perception, neuroscience (saliency maps, stimulus-driven attention), behavioral economics (attention-weighting of attributes), linguistics (markedness and prominence), interface and UI design, machine learning (attention and saliency methods), memory (distinctiveness effects), security (alarm and alerting), and ecology (conspicuousness and signaling). Its structural abstraction is high because the signature is a contrast-against-baseline relation that carries no domain-specific vocabulary — what counts as "background" and "dimension of contrast" is filled in per substrate without altering the pattern. And the transfer is concretely documented, with the saliency-map formalism itself migrating between vision neuroscience and machine learning. Maximal breadth, a clean relational signature, and documented cross-domain transfer all converge on a canonical 5.

  • Composite substrate independence — 5 / 5
  • Domain breadth — 5 / 5
  • Structural abstraction — 5 / 5
  • Transfer evidence — 5 / 5

Relationships to Other Primes

One-hop neighborhood: parents above, mutual partners to the right, children below.Saliencesubsumption: ContrastContrast

Parents (1) — more general patterns this builds on

  • Salience is a kind of, typical Contrast

    Salience is a local-contrast-against-surround computation (signal against background) applied to capture processing capacity — a specialization of contrast.

Path to root: Salience → Contrast → Comparison → Self Checking

Neighborhood in Abstraction Space

Salience sits in a moderately populated region (52nd percentile for distinctiveness): it has near-neighbors but no dense thicket of synonyms.

Family — Selectivity & Bounded Windows (18 primes)

Nearest neighbors

Computed from structural-signature embeddings · 2026-06-14

Not to Be Confused With

Salience is most persistently confused with attention, its nearest neighbor, because both concern what gets processed out of a crowded field. The distinction is which side of the pipeline each names. Salience is a stimulus-side property: a bottom-up scalar computed from each item's local contrast against its surround, before any goal-directed selection runs and independent of what the receiver wants. Attention is a receiver-side operation: the selective allocation of limited processing capacity, shaped by current goals, task set, and expectations as much as by the stimulus. The two are dissociable in both directions — a high-salience flash can capture attention involuntarily, but a motivated observer can also suppress strong salience to attend a bland but goal-relevant target, and a low-salience item can be searched out deliberately. The practical consequence is decisive: a designer who conflates them reasons "I made it salient, so they will attend," and is then surprised when eye-tracking shows fixation without action (top-down suppression) or when a critical-but-quiet signal is never found. The lever for salience is the surround and the contrast operator; the lever for attention is relevance, goals, and task framing — different problems with different fixes.

Salience must also be distinguished from emphasis_focal_point, with which it shares the vocabulary of "standing out." Emphasis is a producer-side act: the deliberate design choice to foreground something — bolding text, enlarging a call-to-action, spotlighting a stage element. Salience is the resulting relational property of standing out against a surround, and crucially it can arise without any producer at all: an unintended flicker, an accidental color clash, or a predator's careless motion all become salient with nobody emphasizing them. Emphasis is one cause of salience, not salience itself, and it is neither necessary (natural contrast produces salience) nor sufficient (emphasis against a saturated surround produces none — the everything-in-bold collapse). Separating them gives the designer two distinct questions: "what am I deliberately foregrounding?" (emphasis) and "what actually stands out against the deployment background, intended or not?" (salience), and explains why universal emphasis cancels — because salience is differential by construction while emphasis is applied item-by-item.

A subtler confusion is with processing_fluency. Both are pre-deliberative and both shape downstream judgment, but they operate at different stages and with opposite relationships to contrast. Salience governs capture — whether an item wins the competition for processing capacity in the first place — and it is raised by novelty, incongruity, and high contrast. Fluency governs the ease of processing an item once it is being processed — and it is raised by familiarity, repetition, and low contrast, feeding feelings of liking, truth, and confidence. The two can pull against each other: a disfluent, hard-to-parse item can be highly salient (it pops because it is locally unusual) yet feel effortful and untrustworthy, while a fluent, familiar item can be barely salient (it blends in) yet feel right. A practitioner who collapses them might assume that making something stand out also makes it feel credible, when salience and fluency can move in opposite directions, and the adaptation dynamics differ — sustained salience habituates toward zero, whereas repeated exposure increases fluency.

These distinctions matter because each separates a different stage of the stimulus-to-judgment pipeline: a producer's emphasis can raise an item's salience, salience governs whether attention is captured, attention governs what is selected for deeper processing, and fluency governs how that processing feels. Collapsing any pair loses a leverage point — confusing salience with attention drops top-down override, confusing it with emphasis drops the relational surround, and confusing it with fluency drops the capture-versus-ease distinction. Holding salience as the specific bottom-up, relational, competitive, adapting score keeps the analyst asking the one question that surface vocabulary buries: salient against what surround?

Solution Archetypes

No catalogued solution archetypes reference this prime yet.