REPLY TO EARLIER COMMENTARY What Determines the Speed of Lexical Access: Homophone or Specific-Word Frequency? A Reply to Jescheniak et al. (2003)
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A. Caramazza, A. Costa, M. Miozzo, and Y. Bi (2001) reported a series of experiments showing that naming latencies for homophones are determined by specific-word frequency (e.g., frequency of nun) and not homophone frequency (frequency of nun none). J. D. Jescheniak, A. S. Meyer, and W. J. M. Levelt (2003) have challenged these studies on a variety of grounds. Here we argue that these criticisms are not well founded and try to clarify the theoretical issues that can be meaningfully addressed by considering the effects of frequency on homophone production. We conclude that the evidence from homophone production cannot be considered to provide support to 2-layer theories of the lexical system.Keywords:
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Cognates are defined as words similar in form and meaning across two languages. Similarity in form may range from full orthographic overlap, as in English film – German Film, to partial overlap, as in English chapel – German Kapelle. Some pairs of cognate words developed historically from a common ancestor word, whereas others emerge when languages come into contact and loan each other words. Language users are typically unaware of such diachronic pressures. When acquiring a second language (L2) they can only perceive shared elements between L1 and L2. Cognates help explain the nature of lexical processing and the manner in which elements from the two languages interact. Different measures have been used to explore cognate processing and representation, including ERP (Midgley et al., 2011; Peeters et al., 2013; Strijkers et al., 2009), latencies in single word (Dijkstra et al., 2010; Lemhofer and Dijkstra, 2004), and primed lexical decision (De Groot and Nas, 1991), eyemovements (Mulder et al., 2011; Rosselli et al., 2012), and scores on standardized tests (Kelley and Kohnert, 2012; Perez et al., 2010). Taken together, empirical findings support the claim that cognates are processed differently from noncognate words. Despite the fact that the aforementioned experimental measures and techniques diverge, the conclusion is similar both in language production and in language comprehension (Dijkstra et al., 2010, for an overview). Nevertheless, results do differ with respect to a range of details, including the direction as well as the magnitude of the cognate effect. Specifically, most studies find facilitation in the processing of cognates in L2 (Dijkstra et al., 1999; Lemhofer and Dijkstra, 2004; Van Hell and De Groot, 2008), but results are less clear when it comes to the effect of cognates in L1. For example, Van Hell and Dijkstra (2002) and Duyck (2005) reported cognates facilitation in the dominant language, while Kroll et al. (2002) reported small cognate inhibition in an L1 naming task, and Caramazza and Brones (1979) failed to find such an effect at all. In the present study we sought to examine the influence of cognates on lexical processing in a visual lexical decision task, using L1/L2 language pairs that belong to different subgroups of IndoEuropean languages: Slavic L1 and Germanic L2. The aim was to carefully replicate recent findings from a study by Radanovic, Feldman, and Milin (2014). Crucially, their study showed quite a complex pattern of effects that included a threeway interaction of language (Serbian L1 vs. English L2) by cognate status (cognate vs. noncognate) by word frequency (as a numerical predictor – covariate). Cognates were processed faster than noncognates in L2, but, surprisingly, significantly slower than noncognates in L1. Furthermore, the size of the effect was greater when word frequency was low. Because this pattern of effects differs from what is typically reported in the literature, we designed a replication of the Radanovic et al. study and followed their method and design, this time using another contrasting pair of languages: Croatian (L1) and German (L2).
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Although previous theories of past-tense verb inflection have considered phonological and grammatical information to be the only relevant factors in the inflection process (e.g. Bybee & Moder, 1983; Rumelhart & McClelland, 1986; Kim, Pinker, Prince & Prasada, 1991), Ramscar (in press) demonstrated the importance of semantics in processing inflectional morphology. This paper presents an experiment that demonstrates the on-line effects of semantics on inflection. These findings indicate that regular and irregular inflections are determined by semantic and phonological similarities in memory, and furthermore that people are not responsive to the kind of grammatical distinctions amongst verb roots that default rule theories of inflection (Pinker, 1999) presuppose.
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Shenxun is one of the manual styles of text-based semantics in Shiming(Annotations),further accounting for words which are phonetically and verbally interpreted as to explain the meaning of sound or homophone exegesis and to explicitly suggest the significance of the naming of the interpreted words.There are normally three forms of Shenxun: first,sentence pattern;second,word explanation;third,the combination of the above two forms.
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In this brief rejoinder, we respond to Farmer, Monaghan, Misyak, and Christiansen (2011).We argue that the data still do not support the claim that reading time is affected by the phonological typicality of a word for its part of speech.We also question Farmer et al.' s claim that interleaving syntactic structures in an experiment modifies grammatically-based syntactic expectations.
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In Vieth et al . (2014a), we conducted three experiments to examine semantic relatedness effects in the picture-word interference (PWI) paradigm. According to the lexical selection by competition account of spoken word production, feature overlap between the target picture and related distractor word induces semantic interference. However, this account has been challenged by studies demonstrating semantic facilitation in the PWI paradigm (e.g., Costa et al., 2005; Mahon et al., 2007; but see Hutson and Damian, 2014; Sailor and Brooks, 2014; Vieth et al., 2014b). In Vieth et al. (2014a), we investigated whether some reports of semantic facilitation in PWI might be due to the influence of distinctive features, i.e., features that quickly distinguish a concept from other similar concepts, as previous studies had not controlled for this variable (e.g., Costa et al., 2005; Mahon et al., 2007; Sailor and Brooks, 2014). In Experiment 3, we observed semantic interference for distractor words denoting a non-distinctive feature (e.g., knee) visible in the target picture (e.g., CAMEL), but no interference for distractor words denoting a distinctive feature (e.g., hump) compared to matched sets of distractors denoting unrelated features. We argued this finding is consistent with lexical selection by competition accounts, and might entail additional spread of activation to related concepts that share the non-distinctive feature via the appropriate category node (e.g., Animals). In their commentary, Montefinese and Vinson (2015) arrive at the opposite conclusion, arguing that feature distinctiveness does not affect the degree of interference in PWI. Here, we respond to each of their objections...
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Abstract The present research examined the influence of polysemy on word naming. Naming was faster to polysemous than to nonpolysemous words. Moreover, polysemy interacted with word frequency such that the facilitative effects of polysemy were isolated to naming of low-frequency words. These findings are discussed with reference to the distributed models of Masson (1991, 1995; Borowsky & Masson, 1996; Masson & Borowsky, 1995) and Kawamoto, Ferrar, and Kello (1994). Resume La presente recherche examinait l'influence de la polysemie sur la designation des noms. Pour la designation, on choisissait plus rapidement des mots polysemiques plutot que des mots non polysemiques. En outre, la polysemie interagissait avec la frequence des mots de telle sorte que les effets facilitateurs de la polysemie etaient isoles a la designation de mots a basse frequence. Ces conclusions sont discutees en reference aux modeles repartis de Masson (1991, 1995; Borowsky & Masson, 1996; Masson & Borowsky, 1995) ainsi que Kawamoto, Ferrar et Kello (1994). The goal of reading is to extract meaning from printed text. Hence, it is reasonable to assume that semantics plays a role in the processing of single words. One semantic variable that has been used to investigate this assumption is polysemy. Polysemy (or ambiguity) refers to the number of meanings associated with a word. For example, the polysemous word chest can mean a body part or a piece of furniture, whereas the nonpolysemous word lake has a single meaning referring to a body of water. Kawamoto, Ferrar, and Kello (1994) and Masson and his colleagues (Borowsky & Masson, 1996; Masson, 1991, 1995; Masson & Borowsky, 1995) have implemented distributed models that provide explicit accounts of how polysemy should affect processing of single words. In both of these models, polysemy is predicted to facilitate lexical decisions, a prediction that is generally supported by the empirical evidence. For example, in recent papers, both Hino and Lupker (1996) and Borowsky and Masson (1996) reported that lexical decisions are faster to polysemous than to nonpolysemous words. For naming, however, the models diverge. The Kawamoto et al. model leads to the prediction that polysemy should facilitate naming, a prediction that has been supported with evidence from Hino and Lupker who found that polysemous words were named faster than nonpolysemous words. In contrast, Masson's model predicts that polysemy does not affect naming. This prediction has been supported by evidence from Borowsky and Masson showing that polysemy did not affect naming performance. Given the divergence of the models concerning polysemy effects in naming, and given the contradictory evidence of Hino and Lupker versus Borowsky and Masson, the goal of the present research was to provide a further empirical investigation of polysemy effects in naming. KAWAMOTO ET AL. (1994) MODEL The Kawamoto et al. (1994) model is built on the notion that for a polysemous word there is one orthographic pattern (or entry) but more than one semantic pattern (i.e., more than one meaning). The existence of more than one meaning is such that when a polysemous word is presented, activation at the semantic level may be unstable and inconsistent. To compensate for this inconsistency and uncertainty at the semantic level, the model's learning algorithm was tailored to give a relatively larger influence to weights at the orthographic layer than to those at the semantic layer for polysemous words. In contrast, nonpolysemous words activate only single patterns at both the orthographic and semantic layers and thus the learning algorithm generates weights that are similar at the orthographic and semantic layers. Kawamoto et al. assume that lexical decisions are based primarily on orthography. Accordingly, the model generates polysemy effects in lexical decisions because of the greater influence of the orthographic weights for polysemous words as compared to those for nonpolysemous words. …
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