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ABSTRACT
Research has shown that adults are skilled at learning new words and meanings. We examined whether learning new meanings for familiar words affects the processing of their existing meanings. Adults learnt fictitious meanings for previously unambiguous words over four consecutive days. We tested comprehension of existing meanings using a semantic relatedness decision task in which the probe word was related to the existing but not the new meaning. Following the training, responses were slower to the trained, but not to the untrained, words, indicating competition between newly-acquired and well-established meanings. This effect was smaller for meanings that were semantically related to existing meanings than for the unrelated counterparts, demonstrating that meaning relatedness modulates the degree of competition. Overall, the findings confirm that new meanings can be integrated into the mental lexicon after just a few days’ exposure, and provide support for current models of ambiguity processing.
Acknowledgements
The data for Experiment 1 was collected when the first author was a Master’s student at University College London. Experiment 2 is part of the first author’s PhD at the University of Leeds. This work was funded by an Economic and Social Research Council (ESRC) doctoral studentship (ES/J500215/1) and a University College London postgraduate research grant awarded to the first author. We would like to thank two anonymous reviewers for their helpful comments on an earlier draft of the manuscript.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 The word “slim” in Rodd et al.’s (Citation2012) stimulus list was changed to “hamster” (Experiment 1) or “mouse” (Experiment 2) so that all trained words had noun/noun-verb interpretations. The word “hamster” was replaced with “mouse” so that lexical and semantic properties of the trained and untrained targets in Experiment 2 were matched more rigorously.
2 As the experiment was not explicitly designed to explore the type of the relationship between the new and the existing meaning (e.g. physical properties vs. function), future studies will need to establish whether there could be an impact on learning performance based on the way new meanings are related.
3 We first attempted to analyse the ratings using a linear mixed-effects model. However, the residuals of the model showed an inverse normal distribution that was insensitive to data transformation, violating the assumption of linear but not generalised mixed-effects modelling.
4 We began analysis with a model that included significant random intercepts and tested all possible slopes for inclusion separately. Out of significant slopes, we first added the most influential one (based on the value of χ2 from model-comparison tests) to the base model and then tested whether the second most influential slope further improves the model. We continued to test and include the remaining slopes until the model failed to converge.
5 Target Type and Block were coded using Helmert contrasts. For Target Type, Contrast 1 compared both trained targets to the untrained counterparts (Untrained = −2/3, Related = 1/3, Unrelated = 1/3), and Contrast 2 compared the two types of trained targets (Untrained = 0, Related = −1/2, Unrelated = 1/2). For Block, Contrast 1 compared Block 1 to Blocks 2 and 3 (1 = 2/3, 2 = −1/3, 3 = −1/3), and Contrast 2 compared Blocks 2 and 3 (1 = 0, 2 = 1/3, 3 = −1/3). Deviation coding was used for both Session (Pre = −1/2, Post = 1/2) and Trial Type (Yes = −1/2, No = 1/2).
6 Throughout this report, any results that reached the significance threshold before but not after the correction for multiple comparisons should be viewed as trends only.
7 There were not any effects of Block in Experiment 2, neither in the latency nor the accuracy data.