about
How Many Words Do We Know? Practical Estimates of Vocabulary Size Dependent on Word Definition, the Degree of Language Input and the Participant's Age.Word knowledge in the crowd: Measuring vocabulary size and word prevalence in a massive online experimentAssessing the usefulness of google books' word frequencies for psycholinguistic research on word processing.The French Lexicon Project: lexical decision data for 38,840 French words and 38,840 pseudowords.How strongly do word reading times and lexical decision times correlate? Combining data from eye movement corpora and megastudies.Subtlex-pl: subtitle-based word frequency estimates for Polish.Comparing word processing times in naming, lexical decision, and progressive demasking: evidence from chronolex.Representation of Semantic Similarity in the Left Intraparietal Sulcus: Functional Magnetic Resonance Imaging Evidence.Cross-modal representation of spoken and written word meaning in left pars triangularis.The impact of word prevalence on lexical decision times: Evidence from the Dutch Lexicon Project 2.Megastudies, crowdsourcing, and large datasets in psycholinguistics: An overview of recent developments.How useful are corpus-based methods for extrapolating psycholinguistic variables?Testing theories of post-error slowingSUBTLEX-UK: a new and improved word frequency database for British English.Adding part-of-speech information to the SUBTLEX-US word frequencies.SUBTLEX-NL: a new measure for Dutch word frequency based on film subtitles.Wuggy: a multilingual pseudoword generator.What can we learn from monkeys about orthographic processing in humans? A reply to Ziegler et al.Dutch plural inflection: The exception that proves the analogy☆SPALEX: A Spanish Lexical Decision Database From a Massive Online Data CollectionThe relationship between second language acquisition and nonverbal cognitive abilitiesWord prevalence norms for 62,000 English lemmasRecognition Times for 54 Thousand Dutch Words: Data from the Dutch Crowdsourcing ProjectRecognition times for 62 thousand English words: Data from the English Crowdsourcing Project
P50
Q27304343-D101CC7D-BFA1-4E34-944F-9209290C823FQ29029221-672505F1-BE8D-4C23-AF5D-428F69766D8AQ30475037-1C8F1957-6C5E-4229-80E4-0A800DDA5118Q33579845-AB403FC2-DCD6-45B3-A21C-2291353AAC8DQ34242186-B3003982-FE63-42CF-8B2F-512A463B796DQ34425488-258D48E2-53AA-419E-8A1D-30ED76AF294FQ35492237-F316997E-6099-48F5-A6D2-2A1CE4D388E9Q38371895-9F0EB6E8-7B92-4D2B-A91A-50FB7F943456Q38380627-C77283DB-13E4-4F5A-8D2F-DD8E9A0A1C03Q38401421-8A5A9D44-31AF-4B33-AFD1-049F87C762FFQ38411563-F632D9E5-0824-4BBD-A885-45B2636B8E90Q38416054-EA612757-8E5A-402E-A9BA-5AFAD38F3E46Q38480781-E7B06898-D986-4FD3-B07B-54942FE338BCQ44501131-D71DF71C-F3DD-4702-851F-07D00B978D41Q47570764-5923FC6B-6B79-4C1C-99DB-1EA7CAEAECC4Q49091230-FC13EB06-2364-4998-8554-C9C910ED0B85Q49091260-494D5A64-DF23-4B6B-BE00-020F68C04687Q50701142-985CF37B-31E3-495A-8700-92E01D92BC5FQ57404614-7C8E2719-87EA-4DD5-A3DA-829E4E7AD1BCQ59335799-47EB8C0D-2560-4CB0-B2E2-BB0DCCCCE31EQ62492326-0689665D-FC21-4F70-B5E4-ACB8FCB502F1Q62495619-2CB3F83D-2F16-4BF5-A464-C40B3719E831Q92325891-2937F2F3-71A4-4662-AD43-E3580863B525Q92329527-1326C1CA-5334-4503-BD31-B51712004824
P50
description
researcher ORCID ID = 0000-0001-7304-7107
@en
name
Emmanuel Keuleers
@ast
Emmanuel Keuleers
@en
Emmanuel Keuleers
@es
Emmanuel Keuleers
@nl
type
label
Emmanuel Keuleers
@ast
Emmanuel Keuleers
@en
Emmanuel Keuleers
@es
Emmanuel Keuleers
@nl
prefLabel
Emmanuel Keuleers
@ast
Emmanuel Keuleers
@en
Emmanuel Keuleers
@es
Emmanuel Keuleers
@nl
P1153
16241538600
P31
P496
0000-0001-7304-7107