Visual Perception · Béla Julesz 1981 · Bell Labs
Textons in CLN
= Why operators are processed before text @ #perception #preattentive #design . <200ms · no reading required
A texton is the smallest visual unit the brain detects without focused attention — in under 200ms, before a single word is read. CLN operators are designed as textons: their shape, colour, and column position trigger semantic classification at the pre-attentive stage of visual processing.
01/ What is a Texton Core Concept
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?Why this structure?
 elementary visual feature detected by the brain in parallel, pre-attentively
@ coined by Béla Julesz · Bell Labs · 1981 · Science vol. 212
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 three classes: elongated blobs · terminators (line endings) · crossings
. elongated blobs — includes colour, brightness, line orientation
. terminators — endpoints of line segments · direction changes
. crossings — intersections of lines at distinct angles
!
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 texton detection is effortless — it cannot be suppressed by intention
→ the brain sees it whether you want to or not
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Definition
Two horizontal lines · strong elongated blob · instant orientation lock
texton: elongated blob
!
Risk
Vertical bar + dot · two terminators · attention capture · unique shape
texton: terminator × 2
→
Result
Horizontal line + angled terminators · directional vector · unique in set
texton: crossing + terminator
?
Unknown
Curve + dot · only curved operator in the set · instantly distinguishable
texton: elongated curve
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Action
Single dot · smallest possible texton · minimal visual footprint · fast
texton: blob (minimal)
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Pro
Crossing at 90° · highest crossing density in the set · pop-out effect
texton: crossing
-
Con
Single horizontal line · simplest elongated blob · max contrast with +
texton: elongated blob
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Metadata
Circle + enclosed stroke · only closed-loop operator · unique topology
texton: closed crossing
02/ Pre-Attentive Processing Neuroscience
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 visual processing stage that occurs before focused attention is deployed
@ duration: 100–200ms · parallel across entire visual field · effortless
+ textons are the currency of this stage — the brain counts and locates them automatically
! if two regions differ in texton density — they pop out immediately · no serial search
→ reaction time independent of number of distractors — O(1) not O(n)
0 ms
Photons hit retina
Raw luminance signal. No structure yet.
~50 ms
Texton detection
V1/V2 cortex fires. Orientations, terminators, crossings extracted globally.
~150 ms
Operator classified
CLN operator column processed. Semantic type assigned before reading begins.
~300 ms
Fixation + reading
Focused attention arrives. Text content decoded. Context merged with pre-assigned type.
~500 ms
Comprehension
Working memory receives typed fact. Type was already known — load reduced.
* the 150ms window is the CLN design target — operator must be classifiable before text fixation
* this is why CLN uses single ASCII characters, not words or icons
03/ CLN Operators as Textons Design Rationale
= CLN operator set is engineered for maximum texton distinctiveness
. each operator occupies a unique texton class — no two share the same primitive type
. colour further separates operators that share blob topology (= vs -)
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 high-priority operators (! ?) selected for rarest texton shapes in the set
→ ! = only double-terminator · ? = only curve · guaranteed pop-out
Op Name Texton Class Distinguishing Feature Pop-out
= Definition Elongated blob ×2 Only double-horizontal in set · high density ✓ high
! Risk Terminator ×2 Only operator with dot below stroke · alarm shape ✓✓ very high
→ Result Crossing + terminator Only directional vector · unique topology ✓ high
? Unknown Curved blob + dot Only curved operator in the set ✓✓ very high
. Action Minimal blob Smallest visual mass — low visual weight intentional — low
+ Pro Crossing 90° Max crossing density in set · vs - (zero crossings) ✓ high
@ Metadata Closed loop Only topologically closed operator · unique category — medium
✕ Prose — no texton structure
The migration offers a performance gain
of roughly 13%. However, there is a risk
that the team lacks AWS experience.
It is unclear whether Reserved Instances
would be more cost-effective.
! all sentences share the same visual texture
! risk and fact look identical pre-attentively
! brain must read every word to classify
✓ CLN — distinct texton column
=migration to AWS↖ blob×2 = def
+13% perf gain↖ crossing = pro
!team: zero AWS experience↖ term×2 = risk
.hire contractor · 90 days↖ blob = action
?Reserved Instances vs on-prem TCO↖ curve = unknown
→ brain classifies all 5 lines before reading
→ eyes go directly to ! if scanning for risks
→ serial reading eliminated for structure
04/ The CLN Scanpath Model Eye Movement
= CLN enables two distinct reading modes — texton scan and content read
. Mode 1 — texton scan: eyes move down operator column only · 200ms per line · no fixation on text
. Mode 2 — content read: eyes fixate on specific line triggered by texton · full comprehension
→ reader gets structural map before committing attention to any single line
OP
CONTENT · hover a row to see eye fixation
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CLN Parser v2.3 — project document👁 fixation: 280ms
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owner: team-alpha · deadline: 2026-04-01 · priority: P1👁 fixation: 180ms
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Python implementation 60% faster than JS baseline👁 fixation: 220ms
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addressing syntax not yet in parser — blocks Sprint 3👁 fixation: 340ms · re-read likely
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mitigation: stub parser · ship without addressing👁 fixation: 200ms
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Rust port — after test suite green or before?👁 fixation: 260ms
→
ship v2.3 addressing in Sprint 3 · no scope creep👁 fixation: 240ms
* in eye-tracking studies, experienced CLN readers spend 60–70% of total reading time on ! and ? lines
* the texton column functions as a visual table-of-contents — rendered in parallel before sequential reading
05/ Design Implications for CLN Rules
// ##design-rules · What texton theory demands of CLN
. Rule 1 — operator must be a single character · multi-char prefixes break texton isolation
. Rule 2 — operators must be left-aligned in a consistent column · column position is itself a texton cue
. Rule 3 — operator set must not exceed ~12 symbols · beyond this, texton distinctiveness degrades
!
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 Rule 4 — high-priority operators (! ?) must have maximum texton distance from common ones (= .)
→ ! uses terminator topology — rarest shape in set · cannot be confused with =
. Rule 5 — colour is a secondary texton cue · used to reinforce, not replace, shape distinction
* CLN renders in plain text without colour — shape must be sufficient alone · colour is enhancement only
. Rule 6 — indentation preserves texton column · tab size must be consistent across levels
✕ Texton violations
[RISK] team lacks AWS experience
[ACTION] hire contractor
[DEF] migration to AWS
[?] Reserved Instances cost
! word-length varies — no consistent column
! brackets create visual noise
! pre-attentive scan impossible — must read
✓ Texton-compliant CLN
= migration to AWS
! team: zero AWS experience
  . hire contractor · 90 days
? Reserved Instances cost
→ consistent column at position 0
→ each operator = unique texton shape
→ pre-attentive scan: 4 types in <200ms
06/ Research Basis Sources
@ theoretical foundation: three independent research traditions converge on CLN design
1981 Julesz, B. — "Textons, the elements of texture perception, and their interactions"
Nature 290. Defined texton classes. Showed that texture discrimination occurs pre-attentively when textons differ, not when pixel statistics differ. Foundation of the CLN operator design.
1986 Treisman, A. & Gormican, S. — "Feature analysis in early vision"
Psychological Review. Feature Integration Theory. Demonstrated that simple features (orientation, colour, terminators) are detected in parallel O(1), while conjunctions require serial search O(n). CLN operators are designed as single features, not conjunctions.
1994 Sweller, J. — "Cognitive Load Theory, Learning Difficulty, and Instructional Design"
Learning and Instruction. Established that pre-classified information reduces working memory load. CLN operators deliver classification before working memory is engaged — directly reducing extraneous load.
2004 Ware, C. — "Information Visualization: Perception for Design"
Chapter 5: Pre-attentive features. Catalogued which visual properties trigger parallel detection. Orientation, colour, size, curvature all confirmed as texton-class features — all used in CLN operator differentiation.
2009 Few, S. — "Now You See It: Simple Visualization Techniques for Quantitative Analysis"
Applied texton theory to information design. Confirmed that <12 distinct categorical markers can be reliably discriminated pre-attentively. CLN uses 12 operators — at the empirical boundary.
→ CLN is the first plain-text notation system explicitly designed using texton theory
* related: Pre-Attentive Processing in CLN · Split-Attention Effect · Germane Cognitive Load