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Volume 137, August 2024, 104517
Author links open overlay panel, , , Highlights•Lexical stress position is unpredictable in languages such as English or Italian.
•A corpus analysis revealed the existence of sublexical cues to stress in Italian.
•Results from a megastudy show Italian readers use those cues and lexical ones.
•Our results can serve as benchmark for computational models of reading.
•Our study provides a large database of stimuli and responses.
AbstractWhen reading polysyllabic words, assignment of lexical stress is a challenge for readers, especially in languages, such as English or Italian, in which stress position is not strictly determined even though words as well as nonwords typically contain several sublexical cues to stress that readers might use. Here, we attempted to identify such cues using a corpus analysis and to examine their impact on human performance in a megastudy in which participants (N = 45) assigned stress to nonwords (N = 800), stimuli particularly revealing of stress cue use because they have no predefined stress pattern. Hierarchical regression results confirmed an impact of sublexical cues examined in former studies and revealed a role for cues not previously examined, including similarity to real words. These results are informative for computational models of reading as they indicate that readers assign stress to nonwords based on not only sublexical but also lexical information.
Section snippetsStudy 1: Corpus analysisWe begin by discussing the general characteristics of Italian with respect to stress. We then review the sublexical cues to stress highlighted by the relevant literature and, finally, we describe how we examined the impact of those cues in the present corpus analysis.
Study 2: MegastudyThe corpus analysis highlighted several potential associations between sublexical units and stress patterns Italian readers might learn. The questions we now move on to address are, first, do Italian readers actually use those associations when assigning stress, and second, is there a role also for lexical information (i.e., information less easily explained by statistical learning) in the stress-assignment process? We sought to answer these questions with a reading aloud megastudy involving
Study 3: Comparison with computational models of readingAfter the examination of the cues to stress in the word corpus and in the nonword megastudy, what we would like to address is how the two computational models of polysyllabic word reading for Italian, Pagliuca and Monaghan’s (2010) model and CDP++ Italian (Perry et al., 2014), which we will now review in greater detail than in the Introduction, would be able to explain the present results.
Pagliuca and Monaghan’s (2010) model is a parallel distributed processing (PDP) model of reading in
Summary of resultsA current mandate in reading research is that researchers should move beyond the examination of only monosyllabic stimuli in order to reach a more complete understanding of how words are read, as words in almost all languages are mainly polysyllabic (Mousikou et al., 2017). Doing so requires understanding the processes regulating stress assignment, especially in languages such as English or Italian in which the position of stress within a word is not readily predictable by rules, although cues
CRediT authorship contribution statementGiacomo Spinelli: Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. Sonia Trettenero: Formal analysis, Investigation, Methodology, Software, Writing – original draft. Stephen J. Lupker: Conceptualization, Methodology, Supervision, Writing – original draft, Writing – review & editing. Lucia Colombo: Conceptualization, Formal analysis, Methodology, Project administration, Resources,
Declaration of Competing InterestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
AcknowledgementsThis research was partially supported by Grant DOR (Dotazione Ordinaria per Ricerca, Università degli Studi di Padova, year 2019) to Lucia Colombo, by Natural Sciences and Engineering Research Council of Canada Grant A6333 to Stephen J. Lupker, and by the Ontario Trillium Scholarship to Giacomo Spinelli. None of these funding sources had a role in study design, in the collection, analysis and interpretation of data, in the writing of the report, and in the decision to submit the article for
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