Hannah Betts, Jenni Rodd, and other Word Lab members have a new pre-print published on the Open Science Framework, the details of which can be found below:
Title: “Retuning of lexical-semantic representations: Repetition and spacing effects in word-meaning priming”
Authors: Hannah N. Betts, Becky A. Gilbert, Zhenguang Garry Cai, Zainab B. Okedara, and Jennifer M. Rodd
Current models of word-meaning access typically assume that lexical-semantic representations of ambiguous words (e.g. ‘bark of the dog/tree’) reach a relatively stable state in adulthood, with only the relative frequencies of the meanings in the language and immediate sentence context determining meaning preference. However, recent experience also affects interpretation: recently-encountered word-meanings become more readily available (Rodd et al., 2016; 2013). Here, three experiments investigated how multiple encounters with word-meanings influence the subsequent interpretation of these words. Participants heard ambiguous words contextually-disambiguated towards a particular meaning and, after a 20-30 minute delay, interpretations of the words were tested in isolation. We replicate the finding that one encounter with an ambiguous word biased later interpretation of this word towards the primed meaning for both subordinate (Experiments 1, 2, 3) and dominant meanings (Experiment 1). In addition, for the first time, we show cumulative effects of multiple repetitions of both the same and different meanings. The effect of a single subordinate exposure persisted after a subsequent encounter with the dominant meaning, compared to a dominant exposure alone (Experiment 1). Furthermore, three subordinate word-meaning repetitions provided an additional boost to priming compared to one, although only when their presentation was spaced (Experiments 2, 3); massed repetitions provided no such boost (Experiments 1, 3). These findings indicate that comprehension is guided by the collective effect of multiple recently activated meanings and that the spacing of these activations is key to producing lasting updates to the lexical-semantic network.