The Italian Crowdsourcing Project: Visual word recognition times for 130,495 Italian words
- PMID: 39733067
- DOI: 10.3758/s13428-024-02548-4
The Italian Crowdsourcing Project: Visual word recognition times for 130,495 Italian words
Abstract
Despite being largely spoken and studied by language and cognitive scientists, Italian lacks large resources of language processing data. The Italian Crowdsourcing Project (ICP) is a dataset of word recognition times and accuracy including responses to 130,465 words, which makes it the largest dataset of its kind item-wise. The data were collected in an online word knowledge task in which over 156,000 native speakers of Italian took part. We validated the ICP dataset by (1) showing that ICP reaction times correlate strongly (r = .78) with lexical decision latencies collected in a traditional lab experiment, (2) showing that the effect of major psycholinguistic variables (e.g., frequency, length, etc.) can be replicated in this dataset, and (3) replicating the effect of word prevalence, which we compute here for the first time for Italian. Given the inclusion of many inflectional forms of verbs, adjectives, and nouns, we further showcase the potential of this dataset by exploring two phenomena (inflectional entropy in verb paradigms and the clitic effect in isolated word recognition) that build on the peculiar properties of Italian. In this paper we present the ICP resource and release response times, accuracy, and prevalence estimates for all the words included.
Keywords: Crowdsourcing; Lexical decision; Megastudy; Prevalence; Word recognition.
© 2024. The Psychonomic Society, Inc.
Conflict of interest statement
Declarations. Ethics approval: Approval was obtained from the ethics committee of the University of Milano-Bicocca (see corresponding sections in the manuscript). The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Consent to participate: Informed consent was obtained from all individual participants included in the study. Consent for publication: No data allowing the identification of participants were collected, included in this paper or in any of the available materials. Participants agreed to the publication of their behavioral data in anonymous and/or aggregated form. Competing interests: Marco Marelli and Simona Amenta serve as Associate Editor and Consulting Editor, respectively, on the Editorial Board of this Journal. The authors have no competing interests to declare that are relevant to the content of this article.
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