Moving beyond Kučera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English
Open accessAbstract
Word frequency is the most important variable in research on word processing and memory. Yet, the main criterion for selecting word frequency norms has been the availability of the measure, rather than its quality. As a result, much research is still based on the old Kucera and Francis frequency norms. By using the lexical decision times of recently published megastudies, we show how bad this measure is and what must be done to improve it. In particular, we investigated the size of the corpus, the language register on which the corpus is based, and the definition of the frequency measure. We observed that corpus size is of practical importance for small sizes (depending on the frequency of the word), but not for sizes above 16-30 million words. As for the language register, we found that frequencies based on television and film subtitles are better than frequencies based on written sources, certainly for the monosyllabic and bisyllabic words used in psycholinguistic research. Finally, we found that lemma frequencies are not superior to word form frequencies in English and that a measure of contextual diversity is better than a measure based on raw frequency of occurrence. Part of the superiority of the latter is due to the words that are frequently used as names. Assembling a new frequency norm on the basis of these considerations turned out to predict word processing times much better than did the existing norms (including Kucera & Francis and Celex). The new SUBTL frequency norms from the SUBTLEX(US) corpus are freely available for research purposes from http://brm.psychonomic-journals.org/content/supplemental, as well as from the University of Ghent and Lexique Web sites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Words with higher frequency are typically more accessible as they are learned earlier and accessed more frequently in the language acquisition process.
“Words with higher frequency (e.g., “chair” vs. “stepstool”) are typically more accessible as they are learned earlier and accessed more frequently in the language acquisition process. 36”
From Automatic classification of AD pathology in FTD phenotypes using natural speech (2024) · cites this paper as [36]
Cites
Eye movements in reading and information processing: 20 years of research
Rayner, K. (1998) · Psychological Bulletin · doi:10.1037/0033-2909.124.3.372 · Cited by 7,979 · see citing works
Effects of frequency on visual word recognition tasks: Where are they?
Monsell, S., Doyle, M. C., & Haggard, P. N. (1989) · Journal of Experimental Psychology: General · doi:10.1037/0096-3445.118.1.43 · Cited by 432 · see citing works
WordGen: A tool for word selection and nonword generation in Dutch, English, German, and French
Duyck, W., Desmet, T., Verbeke, L. P. C., & Brysbaert, M. (2004) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03195595 · Cited by 350 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Norms of valence, arousal, and dominance for 13,915 English lemmas
Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013) · Behavior Research Methods · doi:10.3758/s13428-012-0314-x · Cited by 1,959 · see citing works
Self-citationConcreteness ratings for 40 thousand generally known English word lemmas
Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014) · Behavior Research Methods · doi:10.3758/s13428-013-0403-5 · Cited by 1,821 · see citing works
Self-citationLinguistic Features Identify Alzheimer’s Disease in Narrative Speech
Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2015) · Journal of Alzheimer's Disease · doi:10.3233/jad-150520 · Cited by 797 · see citing works
The ubiquity of frequency effects in first language acquisition
AMBRIDGE, B., KIDD, E., ROWLAND, C. F., & THEAKSTON, A. L. (2015) · Journal of Child Language · doi:10.1017/s030500091400049x · Cited by 589 · see citing works
How Does Popularity Information Affect Choices? A Field Experiment
Tucker, C., & Zhang, J. (2011) · Management Science · doi:10.1287/mnsc.1110.1312 · Cited by 322 · see citing works
Converging evidence for the neuroanatomic basis of combinatorial semantics in the angular gyrus
Price, A. R., Bonner, M. F., Peelle, J. E., & Grossman, M. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.3446-14.2015 · Cited by 262 · see citing works
Automated classification of primary progressive aphasia subtypes from narrative speech transcripts
Fraser, K. C., Meltzer, J. A., Graham, N. L., Leonard, C., Hirst, G., Black, S. E., & Rochon, E. (2014) · Cortex · doi:10.1016/j.cortex.2012.12.006 · Cited by 229 · see citing works
Automatic quantitative analysis of spontaneous aphasic speech
Le, D., Licata, K., & Mower Provost, E. (2018) · Speech Communication · doi:10.1016/j.specom.2018.04.001 · Cited by 97 · see citing works
Navigational Aids and Spatial Memory Impairment: The Role of Divided Attention
Gardony, A. L., Brunyé, T. T., & Taylor, H. A. (2015) · Spatial Cognition & Computation · doi:10.1080/13875868.2015.1059432 · Cited by 82 · see citing works
Shields, G. S., Doty, D., Shields, R. H., Gower, G., Slavich, G. M., & Yonelinas, A. P. (2017) · Stress · doi:10.1080/10253890.2017.1380620 · Cited by 71 · see citing works
Cross-linguistic adaptations of<i>The Comprehensive Aphasia Test</i>: Challenges and solutions
Fyndanis, V., Lind, M., Varlokosta, S., Kambanaros, M., Soroli, E., Ceder, K., Grohmann, K. K., Rofes, A., Simonsen, H. G., Bjekić, J., et al. (2017) · Clinical Linguistics & Phonetics · doi:10.1080/02699206.2017.1310299 · Cited by 63 · see citing works
Lexical and Acoustic Speech Features Relating to Alzheimer Disease Pathology
Cho, S., Cousins, K. A. Q., Shellikeri, S., Ash, S., Irwin, D. J., Liberman, M. Y., Grossman, M., & Nevler, N. (2022) · Neurology · doi:10.1212/wnl.0000000000200581 · Cited by 51 · see citing works
Automated analysis of lexical features in frontotemporal degeneration
Cho, S., Nevler, N., Ash, S., Shellikeri, S., Irwin, D. J., Massimo, L., Rascovsky, K., Olm, C., Grossman, M., & Liberman, M. (2021) · Cortex · doi:10.1016/j.cortex.2021.01.012 · Cited by 41 · see citing works
Lists with and without syntax: a new approach to measuring the neural processing of syntax
Law, R., & Pylkkänen, L. (2021) · The Journal of Neuroscience · doi:10.1523/jneurosci.1179-20.2021 · Cited by 37 · see citing works
Identifying and Avoiding Confusion in Dialogue with People with Alzheimer's Disease
Chinaei, H., Currie, L. C., Danks, A., Lin, H., Mehta, T., & Rudzicz, F. (2017) · Computational Linguistics · doi:10.1162/coli_a_00290 · Cited by 30 · see citing works
Ease-of-learning judgments are based on both processing fluency and beliefs
Jemstedt, A., Schwartz, B. L., & Jönsson, F. U. (2018) · Memory · doi:10.1080/09658211.2017.1410849 · Cited by 24 · see citing works
Talk2Me: Automated linguistic data collection for personal assessment
Komeili, M., Pou-Prom, C., Liaqat, D., Fraser, K. C., Yancheva, M., & Rudzicz, F. (2019) · PLOS ONE · doi:10.1371/journal.pone.0212342 · Cited by 23 · see citing works
Automated detection of progressive speech changes in early Alzheimer's disease
Robin, J., Xu, M., Balagopalan, A., Novikova, J., Kahn, L., Oday, A., Hejrati, M., Hashemifar, S., Negahdar, M., Simpson, W., et al. (2023) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1002/dad2.12445 · Cited by 21 · see citing works
Sleep deprivation impairs binding of information with its context
Kurinec, C. A., Whitney, P., Hinson, J. M., Hansen, D. A., & Van Dongen, H. P. A. (2021) · SLEEP · doi:10.1093/sleep/zsab113 · Cited by 22 · see citing works
Selective attention in perfectionism: Dissociating valence from perfectionism-relevance
Howell, J. A., McEvoy, P. M., Grafton, B., Macleod, C., Kane, R. T., Anderson, R. A., & Egan, S. J. (2016) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/j.jbtep.2016.01.004 · Cited by 20 · see citing works
Language and motor cortex response to comprehending accidental and intentional action sentences
Kana, R. K., Ammons, C. J., Doss, C. F., Waite, M. E., Kana, B., Herringshaw, A. J., & Ver Hoef, L. (2015) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2015.08.020 · Cited by 15 · see citing works
Automatic classification of AD pathology in FTD phenotypes using natural speech
Cho, S., Olm, C. A., Ash, S., Shellikeri, S., Agmon, G., Cousins, K. A. Q., Irwin, D. J., Grossman, M., Liberman, M., & Nevler, N. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.13748 · Cited by 13 · see citing works
“Word frequency was calculated from the SUBTLEX-US corpus 36 and word frequency per million words was transformed with a log 10 scale for simplicity.”
BackgroundMethodMethodological weakness of the death-word-fragment task: Alternative implicit death anxiety measures
Naidu, P. A., Hine, T. J., & Glendon, A. I. (2022) · Death Studies · doi:10.1080/07481187.2020.1846228 · Cited by 13 · see citing works
Tsuboi, N., Francis, W. S., & Jameson, J. T. (2021) · Memory · doi:10.1080/09658211.2020.1845740 · Cited by 12 · see citing works
Concreteness and disagreement: Comment on Pollock (2018)
Neath, I., & Surprenant, A. M. (2020) · Memory & Cognition · doi:10.3758/s13421-019-00992-8 · Cited by 10 · see citing works
Vonk, J. M. J., Morin, B. T., Pillai, J., Rosado Rolon, D., Bogley, R., Baquirin, D. P., Ezzes, Z., Tee, B. L., de Leon, J., Wauters, L., et al. (2025) · Neurology · doi:10.1212/wnl.0000000000213556 · Cited by 10 · see citing works
Universality of representation in biological and artificial neural networks
Hosseini, E., Casto, C., Zaslavsky, N., Conwell, C., Richardson, M., & Fedorenko, E. (2024) · doi:10.1101/2024.12.26.629294 · Cited by 10 · see citing works
Differential effects of semantic distance, distractor salience, and relations in verbal analogy
Jones, L. L., Kmiecik, M. J., Irwin, J. L., & Morrison, R. G. (2022) · Psychonomic Bulletin & Review · doi:10.3758/s13423-022-02062-8 · Cited by 10 · see citing works
Comprehension exposures to words in sentence contexts impact spoken word production
Francis, W. S., Gurrola, B. V., & Martínez, M. (2022) · Memory & Cognition · doi:10.3758/s13421-021-01214-w · Cited by 6 · see citing works
Don, H. J., Boustani, S., Yang, C., & Shanks, D. R. (2024) · Journal of Experimental Psychology: Learning, Memory, and Cognition · doi:10.1037/xlm0001382
- Authors
- Brysbaert, M., & New, B.
- Affiliations
- Royal Holloway University of London (GB)
- Université Paris Descartes (FR)
- Corresponding author
- Brysbaert, Marc
- Venue
- Behavior Research Methods
- Published
- 2009-11
- Volume
- 41
- Issue
- 4
- Pages
- 977-990
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W1997161938
- Cited by
- 2,821
- References
- 3
- Citations by year
- 2026: 165 · 2025: 197 · 2024: 165 · 2023: 201 · 2022: 236 · 2021: 236 · 2020: 210 · 2019: 265 · 2018: 223 · 2017: 168 · 2016: 200 · 2015: 140 · 2014: 131 · 2013: 107 · 2012: 99
- FWCI
- 101.90
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Behavior Research Methods (publishedVersion)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, other-oa)
- Funders
- Universiteit Gent
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Social Sciences › Psychology › Developmental and Educational Psychology › Second Language Acquisition and Learning
- Keywords
- CELEX, word processing, corpus size, register, lemma frequency, contextual diversity, proper names, American English, psycholinguistic norms
- MeSH terms
- Data Interpretation, Statistical, Humans, Language, Memory, Mental Processes, Reference Standards, Vocabulary, Recognition, Psychology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review