Examining the Electrophysiology of Long-term Priming: Repetition And Talker Specificity Effects on Spoken Word Recognition

Our knowledge of how spoken words are represented in the brain is currently limited. In this study, we aimed to probe the representation of spoken words to determine if details related to an episode of exposure to a spoken word are included in those representations. We hypothesized that episodic details of a spoken word are included in mental representations of spoken words, but that these details are not accessed until a relatively late stage of processing. Participants were presented with disyllabic high and low frequency real words in American English, as well as nonwords. Participants were initially exposed to stimuli in block 1, completed a distractor math test, and then were reexposed to the same stimuli in block 2 (long-term repetition priming), in the context of a lexical decision task. Half of the re-exposed stimuli were spoken by the same talkers in blocks 1 and 2, while half were spoken by different talkers. Block 2 also included a control condition with new, unprimed words. Reaction times, accuracy, and event-related potentials were measured during block 2. The results were as follows: There was no evidence for repetition effects (advantages for words repeated by the same talker, compared to unprimed words) or talker effects (advantages for words repeated by the same talker compared to words repeated by different talkers) in accuracy for either high or low frequency words. Significant repetition effects in RT were found for both high and low frequency words, such that participants were quicker to respond to words repeated by the same speaker compared to unprimed words. A trend toward talker effects in high frequency words was observed, but not in low frequency words, as we had predicted1. 1Due to the COVID-19 pandemic, we were unable to complete data collection or access much of our ERP data. Consequently, the behavioral analyses are tentative, until we are able to complete data collection and analyze the complete dataset. Moreover, we plan to complete the ERP analyses as soon as possible