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Regular Articles

An electrophysiological analysis of the time course of phonological and orthographic encoding in written word production

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Pages 360-373 | Received 21 Nov 2018, Accepted 29 Jul 2019, Published online: 29 Aug 2019
 

ABSTRACT

Recent evidence suggests that individuals generate written words based on both spelling and sound. The present study used event-related potentials (ERPs) to examine the relative time course of orthographic and phonological activation. We adopted Chinese as a target language in which spelling and sound are largely dissociated. Native speakers of Chinese Mandarin were presented with coloured pictures and wrote down colour and picture names as adjective–noun phrases. Colour and picture names were either phonologically related, orthographically related, or unrelated. EEG revealed phonological effects in the 200–500 ms time window, starting at 206 ms after picture onset, and orthographic effects in the 300–400 ms time window, starting at 298 ms. The results of our study suggest that activation of phonological codes takes place approximately 100 ms earlier than access to orthographic codes, which provides evidence for phonological encoding as early sources of constraint in written word production.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC), No. 31771212, Youth Innovation Promotion Association CAS, and the German Research Foundation (DFG) and the NSFC in project Crossmodal Learning, DFG TRR-169/NSFC No. 61621136008 to the first author.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Stimuli in the phonological and orthographic conditions were matched on the following variables: number of strokes, word frequency, word length in number of character, image variability, image agreement, concept familiarity, visual complexity, subjective frequency, name agreement, concept agreement, and age-of-acquisition. Values were taken from Liu, Hao, Li, and Shu (Citation2011).

2 Laganaro and Perret (Citation2011) and Perret and Laganaro (Citation2012) introduced an analysis which combines stimulus- and response-aligned ERPs in production tasks. This form of analysis is undeniably valuable because almost the entire production process can be covered. However, in our study, EEG signals close to the average written latency were substantially contaminated from movement due to writing; therefore, we exclusively performed stimulus-aligned analysis.

3 The onset latency analysis suggests “breakpoints” around 200 ms (where phonological effects begin to appear) and 300 ms (where orthographic effects begin to appear), around 400 ms (where orthographic effects disappear) and around 500 ms (where phonological effects disappear).

4 For both latencies and errors, we conducted additional analyses in which the fixed factor repetition was included, and we obtained a main effect of repetition (p < .01, average latencies accelerated with repeated naming of the same trials). But critically, repetition did not statistically interact with any of the other factors, ps > .56.

5 According to the argument highlighted in Barr, Levy, Scheepers, and Tily (Citation2013) one should specify a “maximum model” by including not only by-participant and by-item adjustments, but also allow for adjustments to the slope of each critical within-participants/items variable. Because both relatedness and type of relatedness are manipulated within-participants and with-items, we specified slope adjustments for participants and items. However, this model returned a correlation of 1.00 between intercept and slope for the critical variable, which indicates that the model has been overparameterized (Baayen, Davidson, & Bates, Citation2008) and the simpler model without slope adjustments is preferable.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (NSFC), No. 31771212, Youth Innovation Promotion Association CAS, and the German Research Foundation (DFG) and the NSFC in project Crossmodal Learning, DFG TRR-169/NSFC No. 61621136008 to the first author.

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