Frequency effects in language .... 1, Frequency effects in language learning and processing

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Multidimensional analysis: Research methods and current issues. Analyzing spoken and written discourse: A role for natural language processing tools. In A Phakiti, P. De Costa, L. Starfield Eds. London: Palgrave Macmillan.


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The effect of frequency of occurrence of lexical items in incidental vocabulary learning

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In This Article

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Frequency Effects in Grammar - Oxford Research Encyclopedia of Linguistics

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Thus the analysis was based on 50 participants in this group. In the first analysis, to determine the effect of morphological structure, the mean RTs of two sets of compound words i. In the second analysis, transparent-transparent compounds were compared with partially opaque compounds to evaluate semantic contribution. To identify priming effects, the mean RTs for target items preceded by the first and the second constituent primes were compared to those preceded by unrelated primes.

In addition, the priming effects from the first and second constituent primes were compared with each other to identify any differential facilitation from the two constituents. More specifically, mean RTs were compared to examine whether compounds preceded by either their first or second constituent were recognized faster than those preceded by an unrelated prime word. In the last analysis, pseudocompounds were compared with monomorphemic words to assess the lexical status of constituents.

To investigate whether compounds were processed differently from noncompounds, the mean RTs to two types of compound words transparent and partially opaque items were compared with the mean RTs to noncompounds pseudocompound and monomorphemic items Table 4. Across all three analyses, word types and prime types were within-subject variables and group was between-subject variable and Bonferroni test was used as the post-hoc test.

Therefore a Greenhouse-Geisser correction was used. However, in noncompound items, no significant differences were found among prime conditions.

1. Introduction

These results suggest that while compounds are processed in a decomposed fashion, noncompound items are accessed as unanalyzed units by native speakers. This suggests that intermediate-level participants accessed both compounds and noncompounds via decomposition, relying on constituent 1 and constituent 2, respectively.

In sum, the results revealed that all groups processed English compounds significantly faster than noncompounds. In addition, all groups showed a tendency to decompose compounds; while English native speakers and advanced-level learners could access both constituents, intermediate-level learners accessed only constituent 1. Unlike the other groups, intermediate-level learners accessed constituent 2 in noncompound items, indicating a tendency to segment units irrespective of their morphological status. In the second analysis, the mean RTs of transparent compounds were compared with that of partially opaque compounds to evaluate the extent of semantic contribution in compound processing.

Furthermore, the unrelated prime condition was significantly slower than the other two conditions indicating decomposition in compound processing.


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  8. To sum up, the results revealed that all groups processed partially opaque compounds significantly faster than transparent-transparent compounds. They all processed compounds via decomposition regardless of semantic transparency since no significant interaction effect between word types and prime types was obtained. In the final analysis, the mean RTs of pseudocompounds and of monomorphemic words were compared.

    noroi-jusatsu.info/wp-content/2020-05-26/1052-localiser-mon.php Also, the condition with unrelated primes was significantly slower than the other two conditions, indicating decomposition for pseudocompounds and monomorphemic words. Mean RTs and standard deviations in three prime conditions for pseudocompounds and monomorphemic words. However, no significant differences among the primes were obtained for native speakers and advanced-level L2 learners. These results suggest that while native speakers and advanced-level L2 learners process pseudocompounds and monomorphemic words without morphological parsing, intermediate-level learners apply a constituent 2-based decomposition.

    The findings demonstrated that English native speakers, as predicted, were significantly faster than L2 learners in all word categories. Native speakers recognized compounds significantly faster than noncompounds, and both constituents were activated in compound processing. This finding was also reported in earlier studies conducted with native English speakers e. The advanced L2 group displayed native-like processing as they accessed compounds significantly faster than noncompounds while demonstrating decomposition only in the former category.

    As for the role of semantic transparency in compound processing, all groups showed similar patterns. As proposed by the APPLE Model, partially opaque compounds were processed significantly faster than transparent-transparent compounds. This is probably due to the fact that unlike transparent-transparent compounds, in partially opaque compounds, only the meaning of transparent constituent and transparent whole compound are activated and this results in faster RTs for partially opaque compounds.

    Crucially, both constituents served as primes in both compound types in all groups, indicating semantic transparency-independent decomposition both in L1 and L2 compound recognition, a finding similar to what was reported in Li et al.