This skill encodes expert methodological knowledge for norming linguistic stimuli before running psycholinguistic experiments. A competent programmer without linguistics training would likely construct stimuli based on intuition, failing to control for critical lexical variables (word frequency, length, neighborhood density), skipping cloze norming, using inappropriate rating scales, or under-powering the norming study. Poor stimulus norming is the single most common methodological weakness in psycholinguistic research, because confounds in the materials propagate to every analysis.
When to Use
Use this skill when:
Creating sentence stimuli for reading experiments (self-paced reading, eye-tracking, ERP)
Norming the predictability (cloze probability) of critical words in sentence contexts
Collecting plausibility, naturalness, or acceptability ratings for sentence materials
Controlling lexical properties of critical words across experimental conditions
Designing Latin square counterbalancing for within-item designs
Planning filler items and practice trials
Do not use this skill when:
Working with single-word stimuli without sentence context (use lexical database tools directly)
Analyzing existing normed materials without creating new ones
Research Planning Protocol
Before executing the domain-specific steps below, you MUST:
State the research question -- What specific question is this analysis/paradigm addressing?
Justify the method choice -- Why is this approach appropriate? What alternatives were considered?
Declare expected outcomes -- What results would support vs. refute the hypothesis?
Note assumptions and limitations -- What does this method assume? Where could it mislead?
Present the plan to the user and WAIT for confirmation before proceeding.
For detailed methodology guidance, see the research-literacy skill.
⚠️ Verification Notice
This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.
Cloze Probability Norming
What Is Cloze Probability?
Cloze probability is the proportion of people who complete a sentence fragment with a particular word (Taylor, 1953). It is the standard measure of a word's predictability in context and is a critical control variable in nearly all sentence processing research.
Procedure
Create sentence fragments: Truncate each sentence immediately before the critical word
Present fragments one at a time to participants
Instruct: "Please complete each sentence with the first word that comes to mind. Write only one word."
Score: For each item, cloze probability = (number of completions matching the target word) / (total number of respondents)
Design Parameters
Parameter
Recommended Value
Citation / Rationale
N per item
Minimum 30 raters
Taylor, 1953; Bloom & Fischler, 1980; standard minimum for stable estimates
Preferred N
40-50 raters
More stable estimates, especially for medium-cloze items
Items per participant
50-100 fragments per norming session
Avoid fatigue; pilot to calibrate
Time limit
~10-15 seconds per item or untimed
Untimed is standard; brief limit prevents overthinking
Population
Same as experimental population (e.g., native English speakers, same age range)
Ensures cloze values generalize
Scoring Conventions
Exact match: Only the target word counts (standard)
Morphological variants: Decide a priori whether "run" and "running" count as the same completion. Standard practice: count only the exact form (Staub et al., 2015)
Spelling errors: Accept obvious misspellings of the target
Blank/nonsense responses: Exclude from the denominator (participant did not engage)
Recommendation for online norming: Include 10-15% catch trials (sentences with obvious completions, e.g., "The dog chased the ___") and exclude participants who fail > 20% of catch trials.
Plausibility and Naturalness Ratings
When to Collect
When cloze probability alone is insufficient (e.g., both conditions have low cloze but differ in plausibility)
When manipulating semantic fit or thematic role plausibility
When verifying that "anomalous" conditions are genuinely perceived as odd
Rating Scale Design
Parameter
Recommended
Citation / Rationale
Scale type
Likert scale
Standard for sentence ratings (Schütze & Sprouse, 2014)
Number of points
7-point scale
Balances sensitivity and reliability; standard in psycholinguistics (Schütze & Sprouse, 2014)
Anchors
1 = "very unnatural/implausible" to 7 = "very natural/plausible"
Labeled endpoints with unlabeled intermediate points
N per item
Minimum 20 raters; preferred 30+
Sufficient for stable means per item (Sprouse & Almeida, 2012)
Items per rater
40-80 items per session
Avoid fatigue effects
Practice items
3-5 items spanning the full range before data collection
Calibrate scale use
Instructions Template
"You will read a series of sentences. For each sentence, please rate how natural or plausible it sounds on a scale from 1 to 7, where 1 means 'very unnatural / makes no sense' and 7 means 'perfectly natural / makes complete sense.' There are no right or wrong answers; we are interested in your intuition."
Critical Design Considerations
Within-list design: Each rater sees only one version of each item (Latin square). Raters should never see multiple conditions of the same item, or they will rate contrastively rather than absolutely.
Filler items: Include filler sentences spanning the full rating range. This prevents range restriction.
Order effects: Randomize item order per participant.
Acceptability Judgments
When to Collect
When manipulating syntactic structure (grammaticality, island constraints, movement dependencies)
When testing formal linguistic predictions about sentence well-formedness
For factorial designs crossing syntactic factors (e.g., 2x2 designs testing island effects; Sprouse et al., 2012)
Rating Methods
Method
Description
Pros
Cons
Citation
Likert scale (7-point)
Rate acceptability 1-7
Simple; familiar; sufficient for most purposes
Ceiling/floor possible; ordinal data
Schütze & Sprouse, 2014
Magnitude estimation (ME)
Assign a number proportional to perceived acceptability relative to a reference sentence
Unbounded scale; ratio-level data (in theory)
More complex; participants need training; debated whether it outperforms Likert
Bard et al., 1996; Sprouse, 2011
Forced choice
Choose the more acceptable of two sentences
Binary; easy; avoids scale-use differences
Low sensitivity; many trials needed
Sprouse & Almeida, 2012
Yes/No judgment
"Is this sentence acceptable?"
Simple; binary
Very low sensitivity; cannot distinguish degrees of unacceptability
--
Recommendation: Use 7-point Likert as the default. It provides sufficient sensitivity for most research questions and has been shown to replicate formal linguistic judgments as reliably as magnitude estimation (Sprouse & Almeida, 2012; Sprouse, 2011).
Sample Size for Acceptability
Design
Minimum N
Rationale
Citation
Simple grammatical/ungrammatical
20 participants
Large effect sizes (d > 1.0 typical)
Sprouse & Almeida, 2012
Factorial (2x2) with interaction
30-40 participants
Interaction effects are smaller
Sprouse et al., 2012
Subtle contrasts
50+ participants
Small effect sizes require more power
Power analysis recommended
Lexical Controls
Variables That Must Be Controlled Across Conditions
Every critical word manipulation must control for confounding lexical variables. The target word and its condition-matched alternatives should be equated on the following:
Variable
Database / Source
Why It Matters
Citation
Word frequency
SUBTLEX-US (log10 word frequency per million)
Most powerful predictor of reading time; ~30-60 ms effect for high vs. low (Brysbaert & New, 2009)
Brysbaert & New, 2009
Word length
Character count
Longer words = longer reading times; ~20-30 ms per character (Rayner, 2009)
Rayner, 1998
Orthographic neighborhood density (N)
N-Watch; CLEARPOND
Number of words differing by one letter; affects lexical access (Coltheart et al., 1977)
Andrews, 1997
Concreteness
Brysbaert et al. (2014) ratings
Concrete words processed faster than abstract words
Brysbaert et al., 2014
Age of acquisition (AoA)
Kuperman et al. (2012) ratings
Earlier-acquired words processed faster
Kuperman et al., 2012
Number of syllables
Any pronunciation dictionary
Affects phonological processing time
Rayner, 1998
Morphological complexity
Manual coding
Derived words (e.g., un-happi-ness) processed differently than monomorphemic words
Taft, 2004
Frequency Database Selection
Database
Language
Measure
Recommended?
Citation
SUBTLEX-US
English (US)
Subtitle-based frequency per million
Yes -- best predictor of processing times
Brysbaert & New, 2009
SUBTLEX-UK
English (UK)
Subtitle-based frequency
Yes, for British English materials
van Heuven et al., 2014
HAL
English
Usenet corpus frequency
Outdated; SUBTLEX preferred
Lund & Burgess, 1996
CELEX
English, Dutch, German
Mixed corpus frequency
Acceptable but less predictive than SUBTLEX
Baayen et al., 1995
Key recommendation: Use SUBTLEX log frequency values. They explain more variance in lexical decision and naming times than older norms (Brysbaert & New, 2009).
How to Match Across Conditions
Select critical words for each condition
Retrieve lexical metrics from SUBTLEX-US and norming databases
Compute condition means for each metric
Test for differences: Run t-tests or ANOVAs across conditions on each lexical variable
Criterion: No significant differences (p > 0.20 is a reasonable threshold; some use p > 0.30) on any controlled variable
If matching fails: replace items or add the unmatched variable as a covariate in the analysis
Latin Square Counterbalancing
Purpose
In a within-item design, each item appears in all conditions, but each participant sees each item in only one condition. A Latin square assigns items to conditions across participant lists.
Construction
For a design with k conditions and n items (where n is divisible by k):
Divide items into k groups of n/k items each
Create k lists; in each list, assign each item group to a different condition
Each participant receives one list
Result: every item appears in every condition across participants; each participant sees an equal number of items per condition
Example: 2-Condition Design
With 40 items and 2 conditions (A, B):
List
Items 1-20
Items 21-40
List 1
Condition A
Condition B
List 2
Condition B
Condition A
Requirements
Parameter
Value
Rationale
Minimum items per condition per list
16-24
Standard for psycholinguistic experiments; fewer items = lower power (Brysbaert & Stevens, 2018)
Recommended items
24-40 per condition
More stable estimates, especially for eye-tracking
Participants per list
Equal across lists; minimum 4-6 per list
Ensures balanced representation
Total participants
Divisible by number of lists
Critical for balanced design
Filler Items
Purpose
Fillers prevent participants from noticing the experimental manipulation and adopting strategies.
Design Parameters
Parameter
Recommended Value
Rationale
Filler-to-target ratio
2:1 or 3:1 (fillers:targets)
Standard in psycholinguistics; prevents pattern detection (Schütze & Sprouse, 2014)
Filler diversity
Fillers should span the full range of sentence types, lengths, and structures
Prevents target sentences from standing out
Filler acceptability range
Include some clearly good and some mildly awkward fillers
Prevents raters from using only part of the scale
Filler length
Match the average length of target sentences
Controls for sentence length expectations
Filler Construction Tips
Use fillers from different syntactic constructions than your targets
Include some fillers with comprehension questions (for reading studies) to maintain attentive reading
If targets are semantically anomalous, include some fillers that are also slightly odd (but in different ways) so anomaly is not a cue
Practice and Warm-Up Items
Parameter
Recommended Value
Rationale
Number of practice items
4-6 items (minimum 3)
Familiarize participants with the task and interface
Practice item composition
Span the range of difficulty/acceptability
Calibrate participant expectations
Practice data
Always exclude from analysis
Practice responses are contaminated by learning effects
Warm-up items at start of main experiment
2-3 additional filler items
Allow settling into the task; exclude from analysis
Online Norming Considerations
Platform Recommendations
Platform
Pros
Cons
Typical Pay Rate
Prolific
Diverse participants; pre-screening; good data quality
Smaller pool than MTurk
~$10-15/hour (Prolific minimum: $8/hour)
Amazon MTurk
Large pool; fast recruitment
Lower data quality; less diverse; requires careful screening
~$10-15/hour recommended
PCIbex / Ibex Farm
Free hosting; designed for linguistics
Requires programming; no built-in recruitment
(hosting only)
Gorilla
GUI-based; good for complex designs
Subscription cost
(hosting only)
Quality Control for Online Studies
Measure
Implementation
Threshold
Catch trials
Include 10-15% filler items with obvious answers
Exclude participants failing > 20%
Completion time
Record total time
Exclude participants completing in < 50% of median time
Straight-lining
Check for same response on all items
Exclude participants with zero variance in ratings
Bot detection
Include reCAPTCHA or similar
Exclude flagged responses
Native speaker check
Self-report + brief language background questionnaire
Exclude non-native speakers (unless studying L2)
Common Pitfalls
Not norming cloze probability: Claiming words are "predictable" or "unpredictable" based on experimenter intuition rather than empirical cloze norms. Always collect cloze data (Taylor, 1953).
Too few raters per item: With N < 20 raters for cloze, individual item estimates are unstable. A word with true cloze of 0.50 could yield observed cloze of 0.20-0.80 with only 10 raters. Use minimum 30 raters (Bloom & Fischler, 1980).
Not controlling word frequency: Frequency is the strongest single predictor of reading time. A 1 log-unit difference in SUBTLEX frequency corresponds to ~30-40 ms in gaze duration (Brysbaert & New, 2009; Rayner, 1998). Always match or control.
Using the wrong frequency database: HAL and Kucera-Francis norms are outdated. SUBTLEX-US explains significantly more variance in behavioral data (Brysbaert & New, 2009).
Showing raters multiple conditions of the same item: This introduces contrastive evaluation. Raters must see each item in only one condition (Latin square for norming too).
Insufficient filler items: A 1:1 target-to-filler ratio makes the manipulation transparent. Use at least 2:1 fillers to targets (Schütze & Sprouse, 2014).
Not piloting the norming study: Always pilot with 5-10 participants to catch unclear instructions, ambiguous items, and timing issues before running the full norming sample.
Ignoring age of acquisition: AoA effects are independent of frequency (Kuperman et al., 2012). Failing to control AoA can introduce confounds, especially for studies comparing concrete vs. abstract words.
Minimum Reporting Checklist
Based on Schütze & Sprouse (2014) and current psycholinguistic standards:
Number of items per condition
Cloze probability values: mean, SD, and range per condition (if collected)
Cloze norming details: N raters, population, procedure, scoring criteria
Plausibility/acceptability ratings: scale type, N raters, mean and SD per condition
Lexical control variables: list each controlled variable, database source, and condition means
Statistical test confirming conditions do not differ on controlled variables
Latin square design: number of lists, items per list per condition, participants per list
Filler-to-target ratio and description of filler types
Number of practice/warm-up items
For online norming: platform, pay rate, attention check procedure, exclusion criteria and N excluded
Full item list (in supplementary materials or online repository)
References
Andrews, S. (1997). The effect of orthographic similarity on lexical retrieval: Resolving neighborhood conflicts. Psychonomic Bulletin & Review, 4, 439-461.
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59, 390-412.
Baayen, R. H., Piepenbrock, R., & Gulikers, L. (1995). The CELEX lexical database (CD-ROM). Linguistic Data Consortium, University of Pennsylvania.
Bard, E. G., Robertson, D., & Sorace, A. (1996). Magnitude estimation of linguistic acceptability. Language, 72, 32-68.
Bloom, P. A., & Fischler, I. (1980). Completion norms for 329 sentence contexts. Memory & Cognition, 8, 631-642.
Brysbaert, M., & New, B. (2009). Moving beyond Kucera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41, 977-990.
Brysbaert, M., & Stevens, M. (2018). Power analysis and effect size in mixed effects models: A tutorial. Journal of Cognition, 1, 9.
Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46, 904-911.
Coltheart, M., Davelaar, E., Jonasson, J. T., & Besner, D. (1977). Access to the internal lexicon. In S. Dornic (Ed.), Attention and performance VI. Hillsdale, NJ: Erlbaum.
Kuperman, V., Stadthagen-Gonzalez, H., & Brysbaert, M. (2012). Age-of-acquisition ratings for 30,000 English words. Behavior Research Methods, 44, 978-990.
Kutas, M., & Hillyard, S. A. (1984). Brain potentials during reading reflect word expectancy and semantic association. Nature, 307, 161-163.
Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments, & Computers, 28, 203-208.
Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124, 372-422.
Rayner, K. (2009). Eye movements and attention in reading, scene perception, and visual search. Quarterly Journal of Experimental Psychology, 62, 1457-1506.
Schütze, C. T., & Sprouse, J. (2014). Judgment data. In R. J. Podesva & D. Sharma (Eds.), Research methods in linguistics. Cambridge University Press.
Sprouse, J. (2011). A test of the cognitive assumptions of magnitude estimation: Commutativity does not hold for acceptability judgments. Language, 87, 274-288.
Sprouse, J., & Almeida, D. (2012). Assessing the reliability of textbook data in syntax: Adger's Core Syntax. Journal of Linguistics, 48, 609-652.
Sprouse, J., Schütze, C. T., & Almeida, D. (2012). A comparison of informal and formal acceptability judgments using a random sample from Linguistic Inquiry 2001-2010. Lingua, 134, 219-248.
Staub, A., Grant, M., Astheimer, L., & Cohen, A. (2015). The influence of cloze probability and item constraint on cloze task response time. Journal of Memory and Language, 82, 1-17.
Taft, M. (2004). Morphological decomposition and the reverse base frequency effect. Quarterly Journal of Experimental Psychology, 57A, 745-765.
Taylor, W. L. (1953). "Cloze procedure": A new tool for measuring readability. Journalism Quarterly, 30, 415-433.
van Heuven, W. J. B., Mandera, P., Keuleers, E., & Brysbaert, M. (2014). SUBTLEX-UK: A new and improved word frequency database for British English. Quarterly Journal of Experimental Psychology, 67, 1176-1190.
See references/lexical-databases-guide.md for detailed instructions on accessing and querying lexical control databases.
