Two interacting scales model whether you'll be interrupted: Hardness vs Sharpness. A working model of why focus breaks — and why it's rarely just willpower.
Status — interpretive model, not a validated instrument.
The 1–10 scales below are ordinal heuristics, and every point-value (“+1”, “−6”, “~4”) expresses direction and rough relative magnitude — not a measured effect size. “Attention hardness” has no validated measurement yet, so these numbers can’t be precisely right or wrong; they’re an expert-judgment synthesis of the research summarised in Evidence & calibration below. Use them to reason about trade-offs, not to predict.
The Question This Framework Answers
What disrupts what, and why?
Watch Signal vs Noise in Real Time
Gold signal line vs red noise. When noise exceeds signal, disruption occurs. Adjust the sliders to see how signal strength and noise level interact.
4.0
6.0
⚠️ DISRUPTION LIKELY
Noise exceeds signal — on this model, attention is hard to hold.
The Core Hypothesis
If Sharpness > Hardness → Disruption
The claim isn't that this is literal physics — it's that interruption behaves like a threshold. Once a stimulus is sharper than your attention is hard, interruption becomes very likely, and pushing harder rarely closes the gap; the lever that works is changing the inputs. Treat the arrow as a modelling rule, not a measured law.
The Two Interacting Scales
Like Mohs scale measures mineral hardness, these scales measure attention dynamics.
💎
Attention Hardness
Your resistance to interruption. How hard is your current attention state?
1FogDisrupted by anything
2DriftLoosely directed
3SkimSurface engagement
4AttendNormal work attention ← Average
5FocusConcentrated, effort to break
6LockIgnores minor interruptions
7GripOnly major stimuli penetrate
8FlowExternal world fades
9AbsorptionOnly emergencies reach
10SamadhiNothing penetrates
⚡
Stimulus Sharpness
The penetrating power of a stimulus. How sharp is what's trying to interrupt you?
1AmbientBackground hum
2PeripheralMovement in periphery
3PresentNearby conversation
4SalientPhone buzz, name called ← Natural
5IntrusiveNotification sound, hunger
6CompellingBreaking news, social validation ← Engineered
7UrgentPhone ring, child crying
8AlarmingSmoke alarm, scream
9OverwhelmingPain, panic attack
10TotalComplete system capture
⚠️ The Modern Problem
~4
Est. typical hardness
~6
Est. engineered sharpness
~-2
The gap
On this model, the deck is stacked. If engineered stimuli sit a couple of points sharper than typical attention hardness, the mismatch is structural — which reframes “failure of willpower” as a design problem you change by altering the inputs, not by flexing harder. (Figures are illustrative estimates, not measurements — see Evidence & calibration.)
Engineered vs Natural Sharpness
Extraction technology doesn't create new stimuli—it sharpens existing ones beyond natural levels. The values are illustrative ranks; what the research supports is the direction (engineered > natural).
Natural Stimulus
Natural Sharpness
Engineered Version
Engineered Sharpness
Delta
Friend waves at you
3
Social notification
6
+3
Interesting rumor
3
Clickbait headline
5-6
+2-3
Tribal information
4
Outrage content
7
+3
Potential reward
3
Variable reward schedule
6
+3
Novel information
3
Infinite scroll
5
+2
What Degrades Hardness
Your baseline hardness isn't fixed—these factors reduce it. Point-values are illustrative; the direction of each is research-backed (see below).
Sleep Deprivation
-1 to -3
Cumulative effect
Decision Fatigue ⚠ contested
-1 to -2
“Ego depletion” failed a 23-lab replication — treat with caution (why)
Recent Disruption
-1
Attention residue
Chronic Phone Use
-1 to -2 baseline
Trained distractibility
Hunger/Discomfort
-1
Bodily needs compete
Emotional Distress
-1 to -3
Background processing
The Two Levers
You can change the equation from either side. Most people ignore the easier lever. The “+/−” figures rank interventions by rough impact; they’re estimates, not measured effects.
📈 Build Hardness
Meditation practice+1 to +2 baseline
Regular deep work+1 baseline
Sleep optimization+1 to +2
Single-tasking discipline+1
Distraction logging+0.5 (awareness)
📉 Reduce Sharpness
Notifications off-6 (immediate)
Phone in other room-4 (out of sight)
App deletion-6 (permanent)
Grayscale mode-1 to -2
Scheduled check times-3 (batching)
Notice: Reducing sharpness is faster and more effective than building hardness.
Most people do neither. The rare ones do both.
🧪 Quick Calibration Test
1
Put your phone face-down, on silent, visible on the table
A rough self-check, not a validated test — the time-to-urge bands are a heuristic, and a single trial varies with sleep, stress, and setting.
Evidence & calibration
This page is an illustrative model — a vocabulary for thinking about attention, not a measured instrument. The point-values, deltas, and modifiers are heuristic; what the research supports is the direction of each claim, not the specific numbers.
Every reference below was verified against the journal or publisher record. They establish which way each effect runs — they do not validate the cardinal numbers on this page, and no study returns a population “attention hardness.” Books and contested constructs are flagged.
Notifications & interruptionsDirection: strong
Stothart, Mitchum & Yehnert (2015), J. Experimental Psychology: HPP 41(4), 893–897 — a phone notification alone disrupted an attention task even when the phone was never touched, comparable to actively using it.
Mark, Gudith & Klocke (2008), CHI ’08, 107–110 — after interruption, work was finished faster and at no loss of quality, but with significantly more stress, frustration, and effort.
Attention residue (a recent disruption keeps costing you)Direction: strong
Leroy (2009), Organizational Behavior and Human Decision Processes 109(2), 168–181 — switching tasks leaves “attention residue” that measurably degrades performance on the next task.
Schüll (2012), Addiction by Design, Princeton Univ. Press — variable-ratio reinforcement (rewards on an unpredictable schedule) is the most engaging schedule and is deliberately engineered into machines and feedback loops. (Academic book, not a journal article.)
Brady et al. (2017), PNAS 114(28), 7313–7318 — across 563,312 posts, each added moral-emotional word raised a message’s diffusion by ~20%.
Crockett (2017), Nature Human Behaviour 1(11), 769–771 — digital platforms magnify the triggers and reach of moral outrage while lowering its personal cost.
Fogg (2003), Persuasive Technology, Morgan Kaufmann — foundational text establishing that digital products are intentionally designed to shape attention. (Book, not peer-reviewed.)
Phone presence lowers available attention (“phone in another room”)Direction: strong · magnitude contested
Ward, Duke, Gneezy & Bos (2017), J. Assoc. for Consumer Research 2(2), 140–154 — the mere presence of one’s phone reduced available cognitive capacity, most for the most phone-dependent; capacity was best with the phone in another room. (Later replications find a smaller effect — direction holds, size debated.)
Lim & Dinges (2010), Psychological Bulletin 136(3), 375–389 — meta-analysis: sustained/vigilant attention is the cognitive domain most robustly impaired by short-term sleep loss.
Meditation / single-tasking can build attentionDirection: strong, but effect is small
Sumantry & Stewart (2021), Mindfulness 12, 1332–1349 — meta-analysis: meditation gives small-to-moderate attention gains. The small size is exactly why removing the stimulus is the faster lever than slowly building resistance.
Mrazek et al. (2013), Psychological Science 24(5), 776–781 — a 2-week mindfulness course (RCT) improved working memory and reading comprehension while reducing mind-wandering.
The model’s premise: attention is depletable and restorableDirection: strong
Kaplan (1995), J. Environmental Psychology 15(3), 169–182 — Attention Restoration Theory: directed attention fatigues with use and recovers with rest. Underwrites treating “hardness” as something that depletes and rebuilds rather than a fixed trait.
⚠️ What the literature does not support
The cardinal point-values, deltas, and “gap” are an illustrative model, not measurements — no instrument returns a population “average hardness.”
“Decision fatigue” maps to ego depletion, which failed a 23-laboratory preregistered replication (Hagger et al., 2016, Perspectives on Psychological Science 11(4), 546–573). Treat that row as contested.
The grayscale-mode, distraction-logging, and hunger modifiers, and the time-to-urge self-test cut-points, have no verified empirical magnitude — they’re illustration only.
Attention is probabilistic, not deterministic. Engineered stimuli reliably out-compete willpower, and changing the environment beats trying harder — but nothing “guarantees” disruption.
Connections to Other Frameworks
→ Periodic Table
States correlate with hardness. Flow state = Hardness 8. Scroll state = Hardness 2.
→ Entropy
High entropy = low hardness. Inverse relationship. Disorder reduces resistance.
→ Isotopes
Heavy isotopes have higher hardness. Earned states resist more.
→ Density
Dense communication = fewer attack surfaces. High density increases effective hardness.