The impact of consonant–vowel transpositions on masked priming effects in Italian and English

Colombo, Lucia; Spinelli, Giacomo; Lupker, Stephen J.

doi:10.1177/1747021819867638

Cited by 11 publications

(22 citation statements)

References 76 publications

(153 reference statements)

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“…It is not clear how C-C or V-V transpositions differ from C-V or V-C transpositions for STEs in RT or accuracy measures. Colombo et al (2020), in their review of the differences in TEs of consonant versus vowels, concluded that the consonant-vowel difference does not arise at the lowest or earliest level of processing in reading, and this can be revealed by a masked priming task; however, they suggest that consonant-vowel difference may arise at later levels of processing, which can be revealed by the simple or unprimed LD task, which we use in the current experiments. Based on this understanding, and the features of Hindi's orthography, we can predict that TEs will be stronger for C-C than M-M (matra-matra, i.e., V-V) transpositions in a simple LD study.…”

Section: Transposition Effectsmentioning

confidence: 70%

“…For example, Perea and Lupker (2004 in Spanish; Experiment 4) and Lupker, et al (2008 in English;Experiment 1b) found TEs for vowels using simple LD tasks but not with the priming version of the task; both tasks showed TEs for consonants. Colombo et al (2020), in their review of the differences in TEs of consonants versus vowels, concluded that the consonant-vowel (CV) difference does not arise at the lowest or earliest level of processing in reading, which can be revealed by a masked priming task. They suggested that CV difference may arise at later levels of processing, which can be revealed by the simple or unprimed LD task, which we use in the current experiments.…”

Section: Transposition Effectsmentioning

confidence: 99%

“…Because linear models are at higher risk for failing to converge than standard linear models, we follow the convention of only fitting random-effect intercepts and avoiding the maximal modeling that incorporates random-effect coefficients as well for each of the fixed effects (Colombo et al, 2020).…”

Section: Statistical Analysis 2211 Mixed-effect Modeling To Temper Estimates Of Ste By Controlling For Items and Subjects Effectsmentioning

confidence: 99%

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Transposition Effects in an Aksharic Writing System: The Case of Hindi

Rimzhim

Johri²,

Kelty-Stephen

et al. 2020

Lang Speech

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We tested for transposition effects (TEs) in Hindi (a Modern Indo-Aryan language) using unprimed lexical decision. TEs are defined as less accurate and slower responses to transposed-nonwords (e.g., ‹psate›, formed from base-word ‹paste›) than corresponding replaced-nonwords (e.g., ‹pzute›). In Hindi’s orthography, letters map transparently to phonemes (except schwa), but the letters are arranged into “akshars,” (‹[Cn]V›) which encode open syllables. This formal characteristic makes Hindi’s orthography typologically “aksharic.” We used TEs to determine whether the orthography’s typological units, letters and akshars, are also functional units for readers. We conducted three visual word recognition experiments with adult readers whose native language was Hindi. In Experiment 1, we found TEs for consonant (‹C›) and matra (‹M›, a vowel diacritic) letters, using different stimulus sets for each type of transposition. In the next two experiments, we used the same base words to form all of the transposed and replaced items. In Experiment 2, we replicated the findings of Experiment 1 in a different stimulus set; additionally, we found TEs for transpositions between a ‹C› letter and a ‹CM› akshar. In Experiment 3, we replicated results of the first two experiments by finding TEs for both consonants and matras in another stimulus set; additionally, we found similar TEs for ‹CM› akshars. These results show that ‹C› and ‹M› letters are functional units for Hindi readers; the transposition results for ‹CM› akshars are tentative. TEs for letters show that the aksharic grouping of letters does not prevent readers from decoding the constituent letters of akshars. Hindi is read alphabetically.

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Section: Transposition Effectsmentioning

confidence: 70%

Section: Transposition Effectsmentioning

confidence: 99%

Section: Statistical Analysis 2211 Mixed-effect Modeling To Temper Estimates Of Ste By Controlling For Items and Subjects Effectsmentioning

confidence: 99%

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Transposition Effects in an Aksharic Writing System: The Case of Hindi

Rimzhim

Johri²,

Kelty-Stephen

et al. 2020

Lang Speech

View full text Add to dashboard Cite

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“…However, nonlinear transformations systematically alter the pattern and size of interaction effects, rendering such transformations inappropriate when the research interest lies in interactions, as it does in the present experiments (Balota, Aschenbrenner, & Yap, 2013; Lo & Andrews, 2015). For this reason, consistent with more recent practices (e.g., Cohen-Shikora, Suh, & Bugg, 2019; Colombo, Spinelli, & Lupker, 2020; Lupker et al, 2019; Spinelli, Perry, & Lupker, 2019; Yang, Chen, Spinelli, & Lupker, 2019), we used a GLMM analysis because generalized linear models, unlike linear models, do not assume a normally distributed dependent variable and can, therefore, better accommodate the distribution of raw RT data without requiring a transformation of those data 9…”

Section: Resultsmentioning

confidence: 99%

Is zjudge a better prime for JUDGE than zudge is?: A new evaluation of current orthographic coding models.

Lupker¹,

Spinelli²,

Davis³

2020

Journal of Experimental Psychology: Human Perception and Perfor

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Three masked priming paradigms, the conventional masked priming lexical decision task (Forster & Davis, 1984), the sandwich priming task (Lupker & Davis, 2009) and the masked priming same-different task (Norris & Kinoshita, 2008) were used to investigate priming for a given target (e.g., JUDGE) from primes created by either adding a letter to the beginning of the target (e.g., zjudge) or replacing the target's initial letter (e.g., zudge). Virtually all models of orthographic coding that allow calculation of orthographic similarity measures predict that zjudge should be the better prime because zjudge contains all the letters in JUDGE in their correct order whereas zudge does not. Nonetheless, Adelman et al.'s (2014) megastudy data indicated no difference in the effectiveness of these two prime types. The present experiments provide additional support for the conclusion of no difference between these two prime types with the only observed difference being a small zudge prime advantage in Experiment 1b (sandwich priming). These results suggest that models of orthographic coding/word recognition may be well served by allowing inconsistent information (e.g., the "z" in both zjudge and zudge indicates that the presented prime is not JUDGE) to be given considerable weight during the orthographic coding/word recognition process.

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“…Perea and Acha (2009), for example, failed to find a C-V TL difference in a masked priming same-different task, a task in which a masked prime is presented between the reference stimulus and the target. As another example, Colombo, Spinelli, and Lupker (2019) failed to find a difference between adjacent consonant-vowel transpositions and consonant-consonant transpositions in a masked priming LDT in either Italian or English. Additionally, although, as noted, Carreiras, Vergara, et al (2009), Comesaña et al (2016), and Perea and Acha (2009) have produced masked priming LDT data replicating Perea and Lupker’s (2004) pattern, the only one of those experiments to collect Evoked Response Potential (ERP) data (Carreiras, Vergara, et al, 2009) produced ERP data inconsistent with that pattern.…”

mentioning

confidence: 99%

A reexamination of consonant–vowel differences in masked transposed letter priming effects in the lexical decision task.

Yang¹,

Lupker

2020

Canadian Journal of Experimental Psychology / Revue canadienne

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Most orthographic coding models are based on the assumption that the orthographic code does not distinguish between vowels and consonants and, therefore, those models predict no difference between vowel (cisano-CASINO) and consonant (caniso-CASINO) transposed-letter (TL) effects. The available data, however, do provide some evidence for a consonant–vowel distinction at the level of the orthographic code. Most centrally, masked priming lexical decision tasks, mainly carried out in Spanish, have shown priming from consonant TL primes (e.g., caniso) but not from vowel TL primes (e.g., cisano). The present experiments were an investigation of this pattern. Experiment 1, based on Schubert, Kinoshita, and Norris’ (2018) stimuli which showed no consonant–vowel differences in an unprimed same-different task, also showed no consonant–vowel differences in masked TL priming effects in lexical decision showing, for the first time, a vowel TL priming effect in that task. Experiment 2, using Lupker, Perea, and Davis’ (2008) Experiment 1a stimuli, also showed a small but significant vowel TL priming effect (a nonreplication of that experiment), while replicating the consonant TL priming effect that those authors originally reported. In Experiment 3, TL priming was again essentially unaffected by the consonant-vowel status of the letters involved as well as by target frequency, a variable on which the Experiment 1 and 2 stimuli differed. These results, supported by evoked response potential (ERP) results from other labs, suggest that consonant–vowel TL differences, when they do emerge in English, are likely are not due to the nature of the orthographic code.

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The impact of consonant–vowel transpositions on masked priming effects in Italian and English

Cited by 11 publications

References 76 publications

Transposition Effects in an Aksharic Writing System: The Case of Hindi

Transposition Effects in an Aksharic Writing System: The Case of Hindi

Is zjudge a better prime for JUDGE than zudge is?: A new evaluation of current orthographic coding models.

A reexamination of consonant–vowel differences in masked transposed letter priming effects in the lexical decision task.

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