Modeling Morphological Priming in German With Naive Discriminative Learning
Both localist and connectionist models, based on experimental results obtained for English and French, assume that the degree of semantic compositionality of a morphologically complex word is reflected in how it is processed. Since priming experiments using English and French morphologically related...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-04-01
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| Series: | Frontiers in Communication |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fcomm.2020.00017/full |
| _version_ | 1818042676850720768 |
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| author | R. Harald Baayen Eva Smolka |
| author_facet | R. Harald Baayen Eva Smolka |
| author_sort | R. Harald Baayen |
| collection | DOAJ |
| description | Both localist and connectionist models, based on experimental results obtained for English and French, assume that the degree of semantic compositionality of a morphologically complex word is reflected in how it is processed. Since priming experiments using English and French morphologically related prime-target pairs reveal stronger priming when complex words are semantically transparent (e.g., refill–fill) compared to semantically more opaque pairs (e.g., restrain–strain), localist models set up connections between complex words and their stems only for semantically transparent pairs. Connectionist models have argued that the effect of transparency should arise as an epiphenomenon in PDP networks. However, for German, a series of studies has revealed equivalent priming for both transparent and opaque prime-target pairs, which suggests mediation of lexical access by the stem, independent of degrees of semantic compositionality. This study reports a priming experiment that replicates equivalent priming for transparent and opaque pairs. We show that these behavioral results can be straightforwardly modeled by a computational implementation of Word and Paradigm Morphology (wpm), Naive Discriminative Learning (ndl). Just as wpm, ndl eschews the theoretical construct of the morpheme. Ndl succeeds in modeling the German priming data by inspecting the extent to which a discrimination network pre-activates the target lexome from the orthographic properties of the prime. Measures derived from an ndl network, complemented with a semantic similarity measure derived from distributional semantics, predict lexical decision latencies with somewhat improved precision compared to classical measures, such as word frequency, prime type, and human association ratings. We discuss both the methodological implications of our results, as well as their implications for models of the mental lexicon. |
| first_indexed | 2024-12-10T08:50:07Z |
| format | Article |
| id | doaj.art-09fb834302cb496ba96f0c562dca8211 |
| institution | Directory Open Access Journal |
| issn | 2297-900X |
| language | English |
| last_indexed | 2024-12-10T08:50:07Z |
| publishDate | 2020-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Communication |
| spelling | doaj.art-09fb834302cb496ba96f0c562dca82112022-12-22T01:55:36ZengFrontiers Media S.A.Frontiers in Communication2297-900X2020-04-01510.3389/fcomm.2020.00017503593Modeling Morphological Priming in German With Naive Discriminative LearningR. Harald Baayen0Eva Smolka1Department of Linguistics, University of Tübingen, Tübingen, GermanyDepartment of Linguistics, University of Konstanz, Konstanz, GermanyBoth localist and connectionist models, based on experimental results obtained for English and French, assume that the degree of semantic compositionality of a morphologically complex word is reflected in how it is processed. Since priming experiments using English and French morphologically related prime-target pairs reveal stronger priming when complex words are semantically transparent (e.g., refill–fill) compared to semantically more opaque pairs (e.g., restrain–strain), localist models set up connections between complex words and their stems only for semantically transparent pairs. Connectionist models have argued that the effect of transparency should arise as an epiphenomenon in PDP networks. However, for German, a series of studies has revealed equivalent priming for both transparent and opaque prime-target pairs, which suggests mediation of lexical access by the stem, independent of degrees of semantic compositionality. This study reports a priming experiment that replicates equivalent priming for transparent and opaque pairs. We show that these behavioral results can be straightforwardly modeled by a computational implementation of Word and Paradigm Morphology (wpm), Naive Discriminative Learning (ndl). Just as wpm, ndl eschews the theoretical construct of the morpheme. Ndl succeeds in modeling the German priming data by inspecting the extent to which a discrimination network pre-activates the target lexome from the orthographic properties of the prime. Measures derived from an ndl network, complemented with a semantic similarity measure derived from distributional semantics, predict lexical decision latencies with somewhat improved precision compared to classical measures, such as word frequency, prime type, and human association ratings. We discuss both the methodological implications of our results, as well as their implications for models of the mental lexicon.https://www.frontiersin.org/article/10.3389/fcomm.2020.00017/fullmorphological processingnaive discriminative learningprimingsemantic transparencystem-based lexical accesscomplex verbs |
| spellingShingle | R. Harald Baayen Eva Smolka Modeling Morphological Priming in German With Naive Discriminative Learning Frontiers in Communication morphological processing naive discriminative learning priming semantic transparency stem-based lexical access complex verbs |
| title | Modeling Morphological Priming in German With Naive Discriminative Learning |
| title_full | Modeling Morphological Priming in German With Naive Discriminative Learning |
| title_fullStr | Modeling Morphological Priming in German With Naive Discriminative Learning |
| title_full_unstemmed | Modeling Morphological Priming in German With Naive Discriminative Learning |
| title_short | Modeling Morphological Priming in German With Naive Discriminative Learning |
| title_sort | modeling morphological priming in german with naive discriminative learning |
| topic | morphological processing naive discriminative learning priming semantic transparency stem-based lexical access complex verbs |
| url | https://www.frontiersin.org/article/10.3389/fcomm.2020.00017/full |
| work_keys_str_mv | AT rharaldbaayen modelingmorphologicalprimingingermanwithnaivediscriminativelearning AT evasmolka modelingmorphologicalprimingingermanwithnaivediscriminativelearning |