NIM: A Web-based Swiss army knife to select stimuli for psycholinguistic studies

Abstract

NIM is Web-based software developed to help experimenters with some of the usual tasks carried out in psycholinguistic studies. It allows the user to search for words according to several variables, such as length, matching substrings, lexical frequency, or part of speech, in English, Spanish, and Catalan. NIM also provides the user with the possibilities to obtain different word metrics, such as lexical frequency, length, and part of speech; to find intralanguage and cross-language lexical neighbors; and to get control words for critical stimuli. Regardless of the language used, the program also enables the user to get the orthographic similarity between word pairs and to identify repeated items in lists of experimental stimuli. NIM is free and is publicly available at

Spanish norms for affective and lexico-semantic variables for 1,400 words

“…Descriptive statistics for the six variables included in the database are presented in Table 3, including data for two relevant indices in psycholinguistics: word length and word frequency per million (available in NIM; Guasch et al, 2013).…”

“…Additionally, in order to optimize the use of this database for different types of psycholinguistic studies, we did not discard words either because of their lexical frequency (i.e., we did not exclude lowfrequency words) or because they belonged to a particular part of speech (i.e., we did not restrict our stimuli to nouns). According to the NIM search engine (Guasch, Boada, Ferré, & Sánchez-Casas, 2013), the selected words have frequencies per million ranging from 0.18 to 414.44 (M = 24.57, SD = 38.41) and a length range of 3-13 letters (M = 7.41, SD = 2.06). Concerning parts of speech, our set contains 94.29 % nouns, 24.43 % verbs (including both infinitive and conjugated forms), 17.00 % adjectives, and 0.36 % of other categories (i.e., adverbs and interjections).…”

Accent mark and visual word recognition in Spanish

“…The pseudowords were also distributed into the two lists and appeared with and without accent mark. Contrary to Experiment 1, the pseudowords (with or without accent mark) had no orthographic neighbors (according to NIM; Guasch et al, 2013). Furthermore, we made sure that the bigram frequency (computed with CLEARPOND; Marian, Bartolotti, Chabal, & Shook, 2012) was similar between pseudowords and words in each list 10 .…”

“…For the creation of the two lists, care was taken that the number of letters, the word frequency (using NIM; Guasch et al, 2013), the number of orthographic neighbors (using NIM; Guasch et al, 2013) did not differ across the two lists and across the two conditions (Baseline and Omitted) 9 , since these variables are known to affect the visual recognition of the words (e.g., González-Nosti et al, 2014, for lexical decision in Spanish). Furthermore, most of the words were nouns in both lists.…”

“…For example, we excluded words such as mártir 'martyr', since the Omitted word martir presented an orthographic neighbor with a higher frequency (e.g., partir 'to break, to leave') than the Baseline word (mártir). The examination of the word neighbourhood was performed using NIM (Guasch, Boada, Ferré, & Sánchez-Casas, 2013).…”