Semantic Transparency in Free Stems: The Effect of Orthography-Semantics Consistency on Word Recognition

Abstract

A largely overlooked side result in most studies of morphological priming is a consistent main effect of semantic transparency across priming conditions. That is, participants are faster at recognizing stems from transparent sets (e.g., farm) in comparison to stems from opaque sets (e.g., fruit), regardless of the preceding primes. This suggests that semantic transparency may be also consistently associated with some property of the stem word.We propose that this property might be traced back to the consistency, throughout the lexicon, between the orthographic form of a word and its meaning, here named Orthography-Semantics Consistency (OSC), and that an imbalance in OSC scores might explain the "stem transparency" effect. We exploited distributional semantic models to quantitatively characterize OSC, and tested its effect on visual word identification relying on large-scale data taken from the British Lexicon Project (BLP). Results indicated that (a) the "stem transparency" effect is solid and reliable, insofar it holds in BLP lexical decision times (Experiment 1); (b) an imbalance in terms of OSC can account for it (Experiment 2); and (c) more generally, OSC explains variance in a large item sample from BLP, proving to be an effective predictor in visual word access (Experiment 3).Keywords: Orthography-Semantics Consistency, distributional semantic models, megastudies, visual word identification Orthography-Semantics Consistency 3Semantic transparency in free stems: the effect of Orthography-Semantics Consistency in word recognition.Recent research on morphological processing has focused on the role played by semantic transparency in the recognition of derived words. Indeed, the meaning of a derived form can be more or less associated to the meanings of its constituent morphemes: words like nameless, farmer, bakery can be easily understood given the meaning of their roots (transparent words), whereas in words like courteous, fruitful, cryptic root meanings are not fully maintained (opaque words). How early semantic transparency comes to the stage during visual word processing has been the theoretical issue at the center of a long-standing debate (for a review, see . Priming was the main methodological tool adopted in addressing this question, with the assumption that, if the recognition of a root (e.g., farm) is made quicker by the previous presentation of a related derived form (e.g., farmer ) in comparison to a control prime (e.g., speaker ), this would mean that the root is accessed when processing the derived form. Typically, transparent and opaque derived primes are compared for their effectiveness in facilitating the identification of their (pseudo-)roots. In order to rule out any strategic effect related to prime awareness, the prime is often presented very shortly and preceded by an orthographic mask, making it virtually invisible at an explicit level (masked priming; Forster & Davis, 1984). Under these conditions, priming effect is regularly observed for both transparent and opaque prime-targe...

From sound to meaning: Phonology-to-Semantics mapping in visual word recognition

“…Our approach moves from a recent work by Marelli, Amenta, and Crepaldi (2015), who have proposed a new measure to address the role of orthography in accessing word meaning. Orthography-Semantics Consistency (OSC) is a corpus-based measure that quantifies the relationship between a letter string and the meanings of all the words in a corpus that share that sequence.…”

“…A recent study by Marelli, Amenta, and Crepaldi (2015) showed that the consistency between an orthographic string and the meanings to which it is associated in a large corpus is a relevant predictor in lexical decision experiments. Exploiting irregular mappings between orthography and phonology in English, we were able to compute a phonology-to-semantics consistency measure that dissociates from its orthographic counterpart and tested both measures on lexical decision data taken from the British Lexicon Project (Keuleers et al, 2012).…”

Statistical learning and language (in spite of arbitrariness)

“…As a test case, we considered the construct developed by Marelli, Amenta, and Crepaldi (2015), Orthography-to-Semantics Consistency (OSC). This is a frequency-weighted average of meaning similarity between any given stem (e.g., DIAL) and all words that include that stem in their orthography (e.g., DIALECT, DIALLED, DIALS, DIALLING, DIALOG, DIALYSIS).…”

“…where t is a stem, f rx is the frequency of its k orthographic relatives r x , and t and r x are vectorial representations of meaning as extracted from a distributional semantic model (Marelli et al, 2015).…”

“…This measure was shown to predict word identification times in a large psycholinguistic database (Marelli et al, 2015;Balota et al, 2007). Here we test OSC against a behavioural index of sensitivity to form-meaning regularities, i.e., morphological priming; and against neurophysiological data (Event-Related Potentials, ERP) collected during natural sentence reading (Frank, Otten, Galli, & Vigliocco, 2015).…”