Evaluating Pediatric Cochlear Implant Users' Encoding, Storage, and Retrieval Strategies in Verbal Working Memory

Abstract

Purpose The current study adopts a systematic approach to the examination of working memory components in pediatric cochlear implant (CI) users by separately assessing contributions of encoding, storage, and retrieval. Method Forty-nine long-term CI users and 56 typically hearing controls completed forward and backward span tasks with 3 stimulus sets: visually presented digits, pictures of concrete nouns, and novel symbols. In addition, measures associated with each memory stage were collected: Rapid digit naming provided an estimate of phonological recoding speed, nonword repetition assessed the robustness of representations within phonological storage, and vocabulary knowledge (as measured by the Peabody Picture Vocabulary Test; Dunn & Dunn, 1997 ) estimated redintegration abilities during retrieval. Results Linear mixed modeling revealed that digit naming speed and vocabulary knowledge were consistently related to short-term and working memory span in both CI users and typically hearing controls. However, nonword repetition only contributed to the model for short-term memory. Conclusions Nonword repetition, an index of phonological storage, explained little of the individual variability inworking memory differences between CI users and typically hearing peers. On the other hand, individual differences in encoding and retrieval explained a significant amount of outcome variability in both short-term and working memory tasks. Differences between CI users and typically hearing peers in working memory therefore appear to reflect process components of encoding and retrieval and not simply differences in memory storage. Supplemental Material https://doi.org/10.23641/asha.7849394

Multilab Direct Replication of Flavell, Beach, and Chinsky (1966): Spontaneous Verbal Rehearsal in a Memory Task as a Function of Age

“…In addition to the benefits of robust phonological representations that would be supported by the phonological loop (Baddeley et al, 1998), robust long-term lexical networks contribute to memoryspan performance for both children and adults (Edwards et al, 2004;MacDonald & Christiansen, 2002). Moreover, individual differences in phonological robustness, longterm lexical knowledge, and verbalization speed all predict unique variance in adolescents' serial recall of verbalizable pictures similar to those used in the current study (AuBuchon et al, 2019).…”

Comparing the Speech Perception of Cochlear Implant Users with Three Different Finnish Speech Intelligibility Tests in Noise

“…These qualities likely contribute to the lower reference value for NH listeners, and also to the lower SRT estimate in our study population. The shortness and simplicity of the test is essential for testing children, since their speech perception in noise is still maturing [19,49], and children with hearing loss may have a shorter auditory memory span [50,51]. Likewise, these qualities might render the test the most reliable SIN measure in the elderly, since ageing and age-related cognitive decline affect the speech perception in noise independently of the hearing level [52][53][54].…”

Listening in Noise Remains a Significant Challenge for Cochlear Implant Users: Evidence from Early Deafened and Those with Progressive Hearing Loss Compared to Peers with Normal Hearing

“…At the same time, working memory is engaged [13] to store the speech signal long enough to solve possible mismatches between the degraded auditory input and previously encoded and stored information from the mental lexicon in long-term memory [22]. Some CI users with prelingual deafness (i.e., hearing loss that started at birth or at early age, before the acquisition of language), however, were shown to have poorer linguistic and/or cognitive skills compared to NH subjects [6,12,[23][24][25][26][27][28][29][30], presumably because their brain was deprived of auditory stimulation prior to implantation [5,6,15,23,[31][32][33], thus limiting their ability to exploit top-down predictive coding strategies for perception in noise [34,35]. Finally, personal background factors, including the age of hearing loss, use of residual hearing, mode of communication, and age at implantation, were also found to be contributing factors to the performance of speech-in-noise [18,22,24,[36][37][38][39][40][41][42].…”