Vocabulary | Wordlikeness — Why Some New Words Sound and Look More Like English Than Others

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Vocabulary | Wordlikeness — Why Some New Words Sound and Look More Like English Than Others

Vocabulary learners constantly judge unfamiliar forms before they know what those forms mean. A new word can feel immediately plausible, awkward, foreign, technical, playful or impossible. Wordlikeness is the degree to which a spoken or written form resembles possible words in a language. It emerges from phonotactics, syllable structure, stress, orthography, grapheme patterns, morphology, lexical neighbourhoods and accumulated experience with real vocabulary. Wordlikeness matters because unfamiliar forms that fit familiar language patterns can be easier to perceive, repeat, decode or remember than forms that violate those patterns.

People searching wordlikeness, what makes a word sound English, phonotactic probability, pseudowords, nonwords, possible English words, novel word learning, word form learning, orthographic patterns, syllable structure or why nonsense words sound real are investigating the distributional knowledge hidden inside vocabulary. Speakers know far more about their language than explicit word lists: they know which sound sequences are common, which clusters occur in particular positions, which spellings look legal, which affixes belong to which word classes, and which patterns resemble known lexical families.

This complete guide explains wordlikeness as a graded property rather than a yes/no label. It covers phonotactic probability, orthotactic probability, syllable templates, onset and coda patterns, stress, grapheme sequences, morphology, neighbourhood density, frequency, multilingual transfer, pseudoword construction, nonword repetition, lexical decision, novel-word learning, classroom applications and research design. The central proposition is that wordlikeness is the statistical shadow cast by the vocabulary a learner already knows: unfamiliar forms feel possible because they reuse patterns extracted from familiar words.

Start with Vocabulary — The Mastery Club and How Vocabulary Works. This owner connects to Pseudowords and Nonwords, Lexical Decision, and the existing Phonotactic Probability specialist without replacing any of them.

The 50-Second Router

Fast answer: Wordlikeness is the degree to which an unfamiliar form resembles possible or familiar words in a particular language. It depends on language-specific sound, spelling, syllable, stress, morphological and lexical patterns, so the same form can feel highly word-like to one language community and unusual to another.

1. Wordlikeness Is a Gradient

Forms are not simply word-like or not word-like. Some sit near the centre of a language’s familiar pattern space; others sit near its edges. A short pseudoword such as plim may feel more English-like than a sequence that begins with a cluster English rarely permits. Ratings therefore form a continuum, and experimental stimuli should be treated accordingly.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

2. Existing Vocabulary Creates the Standard

A learner can judge a new form because thousands of previous encounters have built expectations. Wordlikeness is learned distributional knowledge. The listener does not need an explicit rulebook for every legal cluster; repeated vocabulary exposure has already shaped probabilities.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

3. Phonotactics

Phonotactics describes language-specific constraints and tendencies on sound sequences. English permits some consonant clusters initially, others finally, and some almost nowhere. Wordlikeness rises when a novel form respects those patterns.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

4. Phonotactic Probability

Probability adds frequency to legality. Two sequences may both be possible, but one may occur far more often. More frequent segment and biphone patterns can provide stronger support during perception and memory.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

5. Syllable Structure

Languages prefer particular syllable shapes. English allows complex onsets and codas, while other languages may favour simpler structures. Novel forms inherit these expectations, making syllable design central to wordlikeness.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

6. Onsets

The onset is the consonantal material before the vowel nucleus. Familiar onset clusters make a pseudoword easier to assimilate into English phonology. Illegal or extremely rare onsets can make the item feel foreign or malformed.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

7. Codas

Final consonant patterns matter just as much. English permits rich coda clusters, but their frequency varies. A form can begin naturally and still feel odd because of its ending.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

8. Stress Patterns

Multisyllabic wordlikeness depends on stress. A segment sequence may be legal while an unusual stress pattern makes it sound less native-like. Stress also interacts with vowel reduction and morphology.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

9. Orthotactics

Written wordlikeness reflects knowledge of legal and common letter sequences. Readers learn that certain graphemes occur in particular positions and that some spellings strongly cue particular word classes or morphemes.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

10. Grapheme–Phoneme Consistency

A spelling can look plausible yet support several pronunciations. Wordlikeness therefore includes both visual pattern familiarity and the ease with which print maps to sound.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

11. Morphological Familiarity

A novel stem can feel more word-like when combined with familiar prefixes or suffixes. Forms ending in -ness, -able or -tion evoke grammatical and semantic expectations even before the whole word is known.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

12. Lexical Neighbourhoods

A novel form surrounded by many similar known words can benefit from familiar pattern support while also creating competition. Neighbourhood density therefore changes both processing and subjective plausibility.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

13. Analogy

Readers often process a new form by analogy to known words. A pseudoword that resembles a familiar rime or spelling family can be pronounced more confidently because existing vocabulary supplies a template.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

14. Frequency and Familiarity

Wordlikeness is not the same as word frequency, because the form is novel. But high-frequency patterns inside known words influence how plausible new combinations feel.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

15. Spoken and Written Wordlikeness Can Diverge

A sequence may sound natural but look unusual under standard English spelling, or look plausible while supporting awkward pronunciation. Researchers should specify whether they are measuring phonological, orthographic or combined wordlikeness.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

16. Pseudowords

Pseudowords are usually designed to be word-like nonwords. Their usefulness depends on controlled resemblance to the target language. If they are too obvious, they fail to engage lexical competition; if too strange, they become sequence-memory puzzles.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

17. Nonword Repetition

More word-like nonwords are often repeated more accurately because they can recruit familiar long-term lexical and phonotactic knowledge. This is one reason nonword repetition is not a pure memory measure.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

18. Lexical Decision

Highly word-like nonwords can take longer to reject in lexical decision because they activate patterns associated with real words. Wordlikeness therefore directly changes the difficulty of the word/nonword boundary.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

19. Novel Word Learning

Wordlike forms may be easier to encode because their structure fits existing language knowledge. Yet strong similarity to existing words can also create interference. Learning depends on support and competition together.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

20. Form–Meaning Binding

Once a new label receives meaning, wordlikeness can help stabilise form but does not supply the concept. A plausible-looking word still needs semantic instruction and retrieval.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

21. Multilingual Learners

Wordlikeness is language-specific. A cluster natural in one language may be rare in another. Multilingual learners therefore evaluate new forms through several phonological and orthographic systems.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

22. Cognates and Borrowings

Borrowed vocabulary shows how forms can begin outside native pattern expectations and become progressively integrated. Borrowings may retain foreign-looking features or adapt to local phonology and spelling.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

23. Proper Names

Names often violate ordinary lexical expectations. A low-wordlikeness surname can still be legitimate and highly familiar. This demonstrates why wordlikeness is not a truth test for lexical status.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

24. Technical Terms

Scientific and academic terms may use Greek or Latin combining forms that feel highly plausible within specialist vocabulary but rare in everyday English. Domain knowledge shifts the learner’s pattern space.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

25. Brand Names and Coinages

Effective coinages often balance novelty with wordlikeness. Too familiar and the form may disappear into existing vocabulary; too unusual and it may be difficult to pronounce or remember.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

26. Child Language

Children’s wordlikeness judgements change as their lexicon grows. More vocabulary provides more distributional evidence and richer neighbourhood structure.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

27. Adult Second-Language Learning

Adult learners may carry strong phonotactic expectations from the first language. Explicit comparison can help them notice which sound and spelling patterns differ in the target language.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

28. Measuring Wordlikeness

Researchers can use human ratings, phonotactic probability, neighbourhood measures, corpus statistics and model-based scores. No single metric captures every dimension.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

29. Stimulus Matching

When comparing conditions, length, syllable count, stress, neighbourhood and segment probabilities should be controlled as far as the research question requires. Otherwise wordlikeness becomes an unintended confound.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

30. Classroom Assessment

Teachers can use graded pseudowords to see whether a learner handles taught patterns in unfamiliar forms. Difficulty should increase along one dimension at a time.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

31. Creative Writing

Wordlikeness is a useful tool for inventing believable place names, technologies and fictional terms. Writers can deliberately make a coinage sound familiar, archaic, technical or alien by manipulating language patterns.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

32. AI and Generated Words

AI systems can generate highly plausible nonwords because they model statistical patterns in text. Plausibility is not evidence that a term is established; verification is still required.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

33. Why Intuition Can Be Wrong

A form may feel impossible only because it is unfamiliar to one speaker. Dictionaries, corpora and multilingual evidence can reveal legitimate words outside personal experience.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

34. Teaching Principle

Use wordlikeness to teach the hidden pattern system of vocabulary, then return to genuine words. The educational goal is not mastery of invented strings but stronger decoding, pronunciation, spelling and word-learning awareness.

A useful teaching move is to compare two matched unfamiliar forms and ask which feels more possible in English, then require evidence. Learners should name the sound sequence, syllable pattern, spelling pattern, morpheme, stress cue or real-word neighbour that shaped the judgement. Verification matters: intuition becomes valuable when it can be linked to observable language structure rather than treated as authority.

35. Sixty Wordlikeness Micro-Labs

Lab 1: plim vs tromp

Rate plim and tromp on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 2: sproke vs shral

Rate sproke and shral on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 3: ngaf vs vemp

Rate ngaf and vemp on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 4: frand vs blane

Rate frand and blane on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 5: zilm vs skrit

Rate zilm and skrit on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 6: tromp vs marn

Rate tromp and marn on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 7: shral vs drope

Rate shral and drope on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 8: vemp vs calverin

Rate vemp and calverin on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 9: blane vs remicate

Rate blane and remicate on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 10: skrit vs glest

Rate skrit and glest on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 11: marn vs plim

Rate marn and plim on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 12: drope vs sproke

Rate drope and sproke on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 13: calverin vs ngaf

Rate calverin and ngaf on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 14: remicate vs frand

Rate remicate and frand on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 15: glest vs zilm

Rate glest and zilm on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 16: plim vs tromp

Rate plim and tromp on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 17: sproke vs shral

Rate sproke and shral on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 18: ngaf vs vemp

Rate ngaf and vemp on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 19: frand vs blane

Rate frand and blane on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 20: zilm vs skrit

Rate zilm and skrit on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 21: tromp vs marn

Rate tromp and marn on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 22: shral vs drope

Rate shral and drope on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 23: vemp vs calverin

Rate vemp and calverin on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 24: blane vs remicate

Rate blane and remicate on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 25: skrit vs glest

Rate skrit and glest on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 26: marn vs plim

Rate marn and plim on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 27: drope vs sproke

Rate drope and sproke on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 28: calverin vs ngaf

Rate calverin and ngaf on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 29: remicate vs frand

Rate remicate and frand on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 30: glest vs zilm

Rate glest and zilm on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 31: plim vs tromp

Rate plim and tromp on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 32: sproke vs shral

Rate sproke and shral on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 33: ngaf vs vemp

Rate ngaf and vemp on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 34: frand vs blane

Rate frand and blane on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 35: zilm vs skrit

Rate zilm and skrit on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 36: tromp vs marn

Rate tromp and marn on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 37: shral vs drope

Rate shral and drope on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 38: vemp vs calverin

Rate vemp and calverin on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 39: blane vs remicate

Rate blane and remicate on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 40: skrit vs glest

Rate skrit and glest on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 41: marn vs plim

Rate marn and plim on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 42: drope vs sproke

Rate drope and sproke on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 43: calverin vs ngaf

Rate calverin and ngaf on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 44: remicate vs frand

Rate remicate and frand on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 45: glest vs zilm

Rate glest and zilm on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 46: plim vs tromp

Rate plim and tromp on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 47: sproke vs shral

Rate sproke and shral on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 48: ngaf vs vemp

Rate ngaf and vemp on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 49: frand vs blane

Rate frand and blane on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 50: zilm vs skrit

Rate zilm and skrit on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 51: tromp vs marn

Rate tromp and marn on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 52: shral vs drope

Rate shral and drope on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 53: vemp vs calverin

Rate vemp and calverin on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 54: blane vs remicate

Rate blane and remicate on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 55: skrit vs glest

Rate skrit and glest on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 56: marn vs plim

Rate marn and plim on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 57: drope vs sproke

Rate drope and sproke on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 58: calverin vs ngaf

Rate calverin and ngaf on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 59: remicate vs frand

Rate remicate and frand on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

Lab 60: glest vs zilm

Rate glest and zilm on a five-point wordlikeness scale. Do not ask whether they are “good” words. Ask which dimensions make each one sound or look more English-like: onset legality, coda structure, vowel pattern, spelling sequence, syllable shape, stress, morphology or similarity to existing words. Then search an appropriate dictionary or corpus to confirm that neither item has accidentally been treated as a target real word.

On a second pass, change exactly one feature and rerate the item. Replace one consonant, move a cluster, add a familiar suffix, change the spelling while preserving pronunciation, or alter stress. The comparison teaches that wordlikeness is a structured gradient. Finish by pairing one form with a fictional meaning and testing whether the more word-like form is actually easier for that learner to remember; prediction and outcome need not always match.

36. Twelve-Week Programme

Week 1

Week 1 isolates one contributor to wordlikeness: sound legality, segment probability, syllable structure, stress, orthography, morphology, neighbourhoods, multilingual comparison, technical vocabulary, coinages, novel-word learning or research design. Learners collect examples from real texts, construct controlled pseudowords, make predictions and justify them with evidence.

Each week ends with real vocabulary transfer. Select ten unfamiliar real words that contain the week’s pattern, learn their meanings and retrieve them in context. This keeps the invented material subordinate to genuine vocabulary growth.

Week 2

Week 2 isolates one contributor to wordlikeness: sound legality, segment probability, syllable structure, stress, orthography, morphology, neighbourhoods, multilingual comparison, technical vocabulary, coinages, novel-word learning or research design. Learners collect examples from real texts, construct controlled pseudowords, make predictions and justify them with evidence.

Each week ends with real vocabulary transfer. Select ten unfamiliar real words that contain the week’s pattern, learn their meanings and retrieve them in context. This keeps the invented material subordinate to genuine vocabulary growth.

Week 3

Week 3 isolates one contributor to wordlikeness: sound legality, segment probability, syllable structure, stress, orthography, morphology, neighbourhoods, multilingual comparison, technical vocabulary, coinages, novel-word learning or research design. Learners collect examples from real texts, construct controlled pseudowords, make predictions and justify them with evidence.

Each week ends with real vocabulary transfer. Select ten unfamiliar real words that contain the week’s pattern, learn their meanings and retrieve them in context. This keeps the invented material subordinate to genuine vocabulary growth.

Week 4

Week 4 isolates one contributor to wordlikeness: sound legality, segment probability, syllable structure, stress, orthography, morphology, neighbourhoods, multilingual comparison, technical vocabulary, coinages, novel-word learning or research design. Learners collect examples from real texts, construct controlled pseudowords, make predictions and justify them with evidence.

Each week ends with real vocabulary transfer. Select ten unfamiliar real words that contain the week’s pattern, learn their meanings and retrieve them in context. This keeps the invented material subordinate to genuine vocabulary growth.

Week 5

Week 5 isolates one contributor to wordlikeness: sound legality, segment probability, syllable structure, stress, orthography, morphology, neighbourhoods, multilingual comparison, technical vocabulary, coinages, novel-word learning or research design. Learners collect examples from real texts, construct controlled pseudowords, make predictions and justify them with evidence.

Each week ends with real vocabulary transfer. Select ten unfamiliar real words that contain the week’s pattern, learn their meanings and retrieve them in context. This keeps the invented material subordinate to genuine vocabulary growth.

Week 6

Week 6 isolates one contributor to wordlikeness: sound legality, segment probability, syllable structure, stress, orthography, morphology, neighbourhoods, multilingual comparison, technical vocabulary, coinages, novel-word learning or research design. Learners collect examples from real texts, construct controlled pseudowords, make predictions and justify them with evidence.

Each week ends with real vocabulary transfer. Select ten unfamiliar real words that contain the week’s pattern, learn their meanings and retrieve them in context. This keeps the invented material subordinate to genuine vocabulary growth.

Week 7

Week 7 isolates one contributor to wordlikeness: sound legality, segment probability, syllable structure, stress, orthography, morphology, neighbourhoods, multilingual comparison, technical vocabulary, coinages, novel-word learning or research design. Learners collect examples from real texts, construct controlled pseudowords, make predictions and justify them with evidence.

Each week ends with real vocabulary transfer. Select ten unfamiliar real words that contain the week’s pattern, learn their meanings and retrieve them in context. This keeps the invented material subordinate to genuine vocabulary growth.

Week 8

Week 8 isolates one contributor to wordlikeness: sound legality, segment probability, syllable structure, stress, orthography, morphology, neighbourhoods, multilingual comparison, technical vocabulary, coinages, novel-word learning or research design. Learners collect examples from real texts, construct controlled pseudowords, make predictions and justify them with evidence.

Each week ends with real vocabulary transfer. Select ten unfamiliar real words that contain the week’s pattern, learn their meanings and retrieve them in context. This keeps the invented material subordinate to genuine vocabulary growth.

Week 9

Week 9 isolates one contributor to wordlikeness: sound legality, segment probability, syllable structure, stress, orthography, morphology, neighbourhoods, multilingual comparison, technical vocabulary, coinages, novel-word learning or research design. Learners collect examples from real texts, construct controlled pseudowords, make predictions and justify them with evidence.

Each week ends with real vocabulary transfer. Select ten unfamiliar real words that contain the week’s pattern, learn their meanings and retrieve them in context. This keeps the invented material subordinate to genuine vocabulary growth.

Week 10

Week 10 isolates one contributor to wordlikeness: sound legality, segment probability, syllable structure, stress, orthography, morphology, neighbourhoods, multilingual comparison, technical vocabulary, coinages, novel-word learning or research design. Learners collect examples from real texts, construct controlled pseudowords, make predictions and justify them with evidence.

Each week ends with real vocabulary transfer. Select ten unfamiliar real words that contain the week’s pattern, learn their meanings and retrieve them in context. This keeps the invented material subordinate to genuine vocabulary growth.

Week 11

Week 11 isolates one contributor to wordlikeness: sound legality, segment probability, syllable structure, stress, orthography, morphology, neighbourhoods, multilingual comparison, technical vocabulary, coinages, novel-word learning or research design. Learners collect examples from real texts, construct controlled pseudowords, make predictions and justify them with evidence.

Each week ends with real vocabulary transfer. Select ten unfamiliar real words that contain the week’s pattern, learn their meanings and retrieve them in context. This keeps the invented material subordinate to genuine vocabulary growth.

Week 12

Week 12 isolates one contributor to wordlikeness: sound legality, segment probability, syllable structure, stress, orthography, morphology, neighbourhoods, multilingual comparison, technical vocabulary, coinages, novel-word learning or research design. Learners collect examples from real texts, construct controlled pseudowords, make predictions and justify them with evidence.

Each week ends with real vocabulary transfer. Select ten unfamiliar real words that contain the week’s pattern, learn their meanings and retrieve them in context. This keeps the invented material subordinate to genuine vocabulary growth.

37. FAQ

What is wordlikeness?

The degree to which an unfamiliar form resembles possible or familiar words in a language.

Is wordlikeness the same as being a real word?

No. A pseudoword can be highly word-like without being established vocabulary.

What makes a word sound English?

Phonotactics, syllable structure, stress, familiar morphemes and similarity to known words.

What makes a word look English?

Orthographic patterns, grapheme sequences, morphology and familiar spelling families.

Is wordlikeness universal?

No. It depends on language and speaker experience.

Why do some nonsense words feel easier?

They may reuse higher-probability sound or spelling patterns and familiar chunks.

What is phonotactic probability?

The frequency or likelihood of sounds and sound sequences in particular positions.

What is orthotactic probability?

The frequency or legality of written letter or grapheme sequences.

Does a familiar suffix make a nonword more word-like?

Often, because morphology provides a known structural template.

Can a nonword be too word-like?

Yes for some assessment purposes, especially if it strongly triggers a real neighbour.

Can a nonword be too unword-like?

Yes; then it may test unusual sequence processing instead of normal word learning.

Does wordlikeness affect memory?

It can, partly because familiar language knowledge supports unfamiliar forms.

Does wordlikeness affect lexical decision?

Highly word-like nonwords can be harder to reject.

Does it affect new word learning?

Yes, though the direction can depend on competition and task.

Can multilingual learners rate forms differently?

Yes, because they bring different phonological and orthographic systems.

Are borrowed words less word-like?

They may initially violate native expectations but can become integrated through use.

Can brand names use wordlikeness deliberately?

Yes, coinages often balance familiarity and distinctiveness.

Can AI create word-like nonwords?

Yes, but generated plausibility does not prove lexical status.

How should teachers use wordlikeness?

As a way to make hidden sound and spelling patterns explicit, followed by real-word transfer.

What is the final goal?

Better processing and learning of genuine vocabulary, not memorising pseudowords.

38. Research and Further Reading

• Gathercole (1995): wordlikeness and nonword repetition
• Storkel & Maekawa: phonotactic probability and novel word learning
• Word learning, phonological memory and phonotactic probability
• eduKateSG — Phonotactic Probability
• eduKateSG — Lexical Decision

39. Final Synthesis

Wordlikeness is the learner’s accumulated language statistics made perceptible. It tells us that vocabulary knowledge extends beyond dictionary meaning into expectations about sounds, spellings, syllables, morphemes and neighbours. Those expectations help process genuinely new words, but they can also mislead. A convincing form may still be invented, and a strange form may still be real.

The Mastery Club principle: Wordlikeness predicts plausibility, not truth. Use it to understand form, then verify meaning and status.

Extended Application 1: phonics

Build a small comparison set for phonics around legal sound patterns. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 2: spelling

Build a small comparison set for spelling around orthographic patterns. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 3: morphology

Build a small comparison set for morphology around affix structure. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 4: pronunciation

Build a small comparison set for pronunciation around stress and syllables. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 5: academic vocabulary

Build a small comparison set for academic vocabulary around Greek and Latin combining forms. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 6: science terms

Build a small comparison set for science terms around specialist wordlikeness. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 7: creative writing

Build a small comparison set for creative writing around credible coinages. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 8: multilingual learning

Build a small comparison set for multilingual learning around cross-language contrast. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 9: assessment

Build a small comparison set for assessment around matched pseudowords. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 10: AI literacy

Build a small comparison set for AI literacy around plausibility versus verification. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 11: phonics

Build a small comparison set for phonics around legal sound patterns. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 12: spelling

Build a small comparison set for spelling around orthographic patterns. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 13: morphology

Build a small comparison set for morphology around affix structure. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 14: pronunciation

Build a small comparison set for pronunciation around stress and syllables. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 15: academic vocabulary

Build a small comparison set for academic vocabulary around Greek and Latin combining forms. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 16: science terms

Build a small comparison set for science terms around specialist wordlikeness. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 17: creative writing

Build a small comparison set for creative writing around credible coinages. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 18: multilingual learning

Build a small comparison set for multilingual learning around cross-language contrast. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 19: assessment

Build a small comparison set for assessment around matched pseudowords. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 20: AI literacy

Build a small comparison set for AI literacy around plausibility versus verification. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 21: phonics

Build a small comparison set for phonics around legal sound patterns. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 22: spelling

Build a small comparison set for spelling around orthographic patterns. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 23: morphology

Build a small comparison set for morphology around affix structure. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 24: pronunciation

Build a small comparison set for pronunciation around stress and syllables. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 25: academic vocabulary

Build a small comparison set for academic vocabulary around Greek and Latin combining forms. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 26: science terms

Build a small comparison set for science terms around specialist wordlikeness. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 27: creative writing

Build a small comparison set for creative writing around credible coinages. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 28: multilingual learning

Build a small comparison set for multilingual learning around cross-language contrast. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 29: assessment

Build a small comparison set for assessment around matched pseudowords. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 30: AI literacy

Build a small comparison set for AI literacy around plausibility versus verification. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 31: phonics

Build a small comparison set for phonics around legal sound patterns. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 32: spelling

Build a small comparison set for spelling around orthographic patterns. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 33: morphology

Build a small comparison set for morphology around affix structure. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 34: pronunciation

Build a small comparison set for pronunciation around stress and syllables. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 35: academic vocabulary

Build a small comparison set for academic vocabulary around Greek and Latin combining forms. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 36: science terms

Build a small comparison set for science terms around specialist wordlikeness. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 37: creative writing

Build a small comparison set for creative writing around credible coinages. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 38: multilingual learning

Build a small comparison set for multilingual learning around cross-language contrast. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 39: assessment

Build a small comparison set for assessment around matched pseudowords. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 40: AI literacy

Build a small comparison set for AI literacy around plausibility versus verification. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 41: phonics

Build a small comparison set for phonics around legal sound patterns. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 42: spelling

Build a small comparison set for spelling around orthographic patterns. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 43: morphology

Build a small comparison set for morphology around affix structure. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 44: pronunciation

Build a small comparison set for pronunciation around stress and syllables. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 45: academic vocabulary

Build a small comparison set for academic vocabulary around Greek and Latin combining forms. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 46: science terms

Build a small comparison set for science terms around specialist wordlikeness. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 47: creative writing

Build a small comparison set for creative writing around credible coinages. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 48: multilingual learning

Build a small comparison set for multilingual learning around cross-language contrast. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 49: assessment

Build a small comparison set for assessment around matched pseudowords. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 50: AI literacy

Build a small comparison set for AI literacy around plausibility versus verification. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 51: phonics

Build a small comparison set for phonics around legal sound patterns. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 52: spelling

Build a small comparison set for spelling around orthographic patterns. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 53: morphology

Build a small comparison set for morphology around affix structure. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 54: pronunciation

Build a small comparison set for pronunciation around stress and syllables. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 55: academic vocabulary

Build a small comparison set for academic vocabulary around Greek and Latin combining forms. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 56: science terms

Build a small comparison set for science terms around specialist wordlikeness. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 57: creative writing

Build a small comparison set for creative writing around credible coinages. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 58: multilingual learning

Build a small comparison set for multilingual learning around cross-language contrast. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 59: assessment

Build a small comparison set for assessment around matched pseudowords. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 60: AI literacy

Build a small comparison set for AI literacy around plausibility versus verification. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 61: phonics

Build a small comparison set for phonics around legal sound patterns. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 62: spelling

Build a small comparison set for spelling around orthographic patterns. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 63: morphology

Build a small comparison set for morphology around affix structure. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 64: pronunciation

Build a small comparison set for pronunciation around stress and syllables. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.

Extended Application 65: academic vocabulary

Build a small comparison set for academic vocabulary around Greek and Latin combining forms. Include two real words, two high-wordlikeness pseudowords and one deliberately low-wordlikeness form. Ask learners to predict which forms will be easiest to pronounce, spell, repeat and remember. Require a separate prediction for lexical status, because plausibility and real-word status are different questions.

After the task, verify the real words, analyse errors and map each judgement to a specific linguistic cue. Then teach one genuine unfamiliar word that shares the useful pattern. The sequence turns implicit pattern sensitivity into explicit vocabulary-learning strategy while preventing invented forms from becoming an end in themselves.