Effects of primary and secondary morphological family size in monolingual and bilingual word processing

Mulder, Kimberley; Dijkstra, Ton; Schreuder, Robert; Baayen, R. Harald

doi:10.1016/j.jml.2013.12.004

Cited by 87 publications

(48 citation statements)

References 45 publications

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“…They emerge not only in the present data set, but have been observed for visual lexical decision (Mulder et al, 2014) as well as for word naming and for eeg data (Baayen et al, 2016b). If this interpretation is correct, penalized factor smooths are the appropriate statistical tool to use.…”

Section: The Kkl Datasetsupporting

confidence: 62%

The cave of shadows: Addressing the human factor with generalized additive mixed models

Baayen

Vasishth

Kliegl

et al. 2017

Journal of Memory and Language

198

201

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Generalized additive mixed models are introduced as an extension of the generalized linear mixed model which makes it possible to deal with temporal autocorrelational structure in experimental data. This autocorrelational structure is likely to be a consequence of learning, fatigue, or the ebb and flow of attention within an experiment (the 'human factor'). Unlike molecules or plots of barley, subjects in psycholinguistic experiments are intelligent beings that depend for their survival on constant adaptation to their environment, including the environment of an experiment. Three data sets illustrate that the human factor may interact with predictors of interest, both factorial and metric. We also show that, especially within the framework of the generalized additive model, in the nonlinear world, fitting maximally complex models that take every possible contingency into account is ill-advised as a modeling strategy. Alternative modeling strategies are discussed for both confirmatory and exploratory data analysis.

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Section: The Kkl Datasetsupporting

confidence: 62%

The cave of shadows: Addressing the human factor with generalized additive mixed models

Baayen

Vasishth

Kliegl

et al. 2017

Journal of Memory and Language

198

201

View full text Add to dashboard Cite

show abstract

“…Alternatively, it is possible that for some reason specific to the bilingual language architecture, bilinguals would use cognate words overall more often than monolinguals do (Sadat, Pureza, & Alario, unpublished data). Whatever the origin for a confounded bilingual frequency count could be, in this context it is important to mention that in the present data there was no significant interaction between lexical frequency and translation similarity (e.g., see also Costa et al, 2000;Mulder, Dijkstra, Schreuder, & Baayen, 2014). Therefore, the effects of phonological similarity and frequency are assumed to be additive and considered separately in this discussion.…”

Section: Discussionmentioning

confidence: 61%

Breaking Down the Bilingual Cost in Speech Production

et al. 2015

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Bilinguals have been shown to perform worse than monolinguals in a variety of verbal tasks. The current study investigated this bilingual verbal cost in a large-scale picture naming study conducted in Spanish. We explored how individual characteristics of the participants and the linguistic properties of the words being spoken influence this performance cost. In particular, we focused on the contributions of lexical frequency and phonological similarity across translations. The naming performance of Spanish-Catalan bilinguals speaking in their dominant and non-dominant language was compared to that of Spanish monolinguals. Single trial naming latencies were analyzed by means of linear mixed models accounting for individual effects at the participant and item level. While decreasing lexical frequency was shown to increase naming latencies in all groups, this variable by itself did not account for the bilingual cost. In turn, our results showed that the bilingual cost disappeared when naming words with high phonological similarity across translations. In short, our results show that frequency of use can play a role in the emergence of the bilingual cost, but that phonological similarity across translations should be regarded as one of the most important variables that determine the bilingual cost in speech production. Low phonological similarity across translations yields worse performance in bilinguals and promotes the bilingual cost in naming performance. The implications of our results for the effect of phonological similarity across translations within the bilingual speech production system are discussed.

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“…Baayen et al (2011) take the 20 most activated competitors into account as competitors for the target meaning, and Mulder, Dijkstra, Schreuder, and Baayen (2013) show that within the NDR approach, the summed activation of non-targeted meanings above a certain threshold can be taken into account, in line with the multiple readout framework of Grainger and Jacobs (1996). The experience of these studies is that the target's activation is the main determinant of response latencies, with additional mechanisms for conflict resolution providing minor improvements in goodness of fit.…”

Section: Discrimination Learning and The Naive Discriminative Readermentioning

confidence: 88%

Sidestepping the Combinatorial Explosion: An Explanation of n-gram Frequency Effects Based on Naive Discriminative Learning

2013

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Arnon and Snider ((2010). More than words: Frequency effects for multi-word phrases. Journal of memory and language, 62, 67-82) documented frequency effects for compositional four-grams independently of the frequencies of lower-order n-grams. They argue that comprehenders apparently store frequency information about multi-word units. We show that n-gram frequency effects can emerge in a parameter-free computational model driven by naive discriminative learning, trained on a sample of 300,000 four-word phrases from the British National Corpus. The discriminative learning model is a full decomposition model, associating orthographic input features straightforwardly with meanings. The model does not make use of separate representations for derived or inflected words, nor for compounds, nor for phrases. Nevertheless, frequency effects are correctly predicted for all these linguistic units. Naive discriminative learning provides the simplest and most economical explanation for frequency effects in language processing, obviating the need to posit counters in the head for, and the existence of, hundreds of millions of n-gram representations.

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Effects of primary and secondary morphological family size in monolingual and bilingual word processing

Cited by 87 publications

References 45 publications

The cave of shadows: Addressing the human factor with generalized additive mixed models

The cave of shadows: Addressing the human factor with generalized additive mixed models

Breaking Down the Bilingual Cost in Speech Production

Sidestepping the Combinatorial Explosion: An Explanation of n-gram Frequency Effects Based on Naive Discriminative Learning

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