Written word frequency is a key variable used in many psycholinguistic studies and is central in explaining visual word recognition. Indeed, methodological advances on single-word frequency estimates have helped to uncover novel language-related cognitive processes, fostering new ideas and studies. In an attempt to support and promote research on a related emerging topic, visual multi-word recognition, we extracted from the exhaustive Google Ngram datasets a selection of millions of multi-word sequences and computed their associated frequency estimate. Such sequences are presented with part-of-speech information for each individual word. An online behavioral investigation making use of the French 4-gram lexicon in a grammatical decision task was carried out. The results show an item-level frequency effect of word sequences. Moreover, the proposed datasets were found useful during the stimulus selection phase, allowing more precise control of the multi-word characteristics.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.3758/s13428-022-02018-9 | DOI Listing |
Behav Res Methods
December 2023
Laboratoire de Psychologie Cognitive (UMR7290), CNRS & Aix-Marseille Université Case D, 3, place Victor HUGO, 13331, Marseille Cedex 3, France.
Written word frequency is a key variable used in many psycholinguistic studies and is central in explaining visual word recognition. Indeed, methodological advances on single-word frequency estimates have helped to uncover novel language-related cognitive processes, fostering new ideas and studies. In an attempt to support and promote research on a related emerging topic, visual multi-word recognition, we extracted from the exhaustive Google Ngram datasets a selection of millions of multi-word sequences and computed their associated frequency estimate.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!