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      Does adding an accent mark hinder lexical access? Evidence from Spanish

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          Abstract

          Recent research has shown that omitting the accent mark in a Spanish word, which is a language in which these diacritics only indicate lexical stress, does not cause a delay in lexical access (e.g., cárcel [prison] ≈ carcel; cárcel- CÁRCELcarcel- CÁRCEL). This pattern has been interpreted as accented and nonaccented vowels sharing the abstract letter representations in Spanish. However, adding an accent mark to a nonaccented Spanish word appears to produce a reading cost in masked priming paradigms (e.g., féliz- FELIZ [happy] > feliz- FELIZ). We examined whether adding an accent mark to a non accented Spanish word slows down lexical access in two semantic categorization experiments to solve this puzzle. We added an accent mark either on the stressed syllable (Experiment 1, e.g., cébra for the word cebra [zebra]) or an unstressed syllable (Experiment 2, e.g., cebrá). While effect sizes were small in magnitude, adding an accent mark produced a cost relative to the intact words, especially when the accent mark was added on an unstressed syllable ( cebrá > cebra). These findings favor the view that letter identity and (to a lesser extent) accent mark information are encoded during word recognition in Spanish. We also examined the practical implications of these results.

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          brms: An R Package for Bayesian Multilevel Models Using Stan

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            Random effects structure for confirmatory hypothesis testing: Keep it maximal.

            Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F 1 and F 2 tests, and in many cases, even worse than F 1 alone. Maximal LMEMs should be the 'gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.
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              The neural code for written words: a proposal.

              How is reading, a cultural invention, coded by neural populations in the human brain? The neural code for written words must be abstract, because we can recognize words regardless of their location, font and size. Yet it must also be exquisitely sensitive to letter identity and letter order. Most existing coding schemes are insufficiently invariant or incompatible with the constraints of the visual system. We propose a tentative neuronal model according to which part of the occipito-temporal 'what' pathway is tuned to writing and forms a hierarchy of local combination detectors sensitive to increasingly larger fragments of words. Our proposal can explain why the detection of 'open bigrams' (ordered pairs of letters) constitutes an important stage in visual word recognition.
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                Author and article information

                Contributors
                Journal
                Journal of Cultural Cognitive Science
                J Cult Cogn Sci
                Springer Science and Business Media LLC
                2520-100X
                2520-1018
                September 2022
                June 20 2022
                September 2022
                : 6
                : 2
                : 219-228
                Article
                10.1007/s41809-022-00104-0
                eb484c2f-a645-4d2e-9ffd-3a26fc9f6ccb
                © 2022

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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