Interactions Between Item Set and Vocoding in Serial Recall

Abstract

Objectives: Serial recall of digits is frequently used to measure short-term memory span in various listening conditions. However, the use of digits may mask the effect of low quality auditory input. Digits have high frequency and are phonologically distinct relative to one another, so they should be easy to identify even with low quality auditory input. In contrast, larger item sets reduce listener ability to strategically constrain their expectations, which should reduce identification accuracy and increase the time and/or cognitive resources needed for identification when auditory quality is low. This diminished accuracy and increased cognitive load should interfere with memory for sequences of items drawn from large sets. The goal of this work was to determine whether this predicted interaction between auditory quality and stimulus set in short-term memory exists, and if so, whether this interaction is associated with processing speed, vocabulary, or attention. Design: We compared immediate serial recall within young adults with normal hearing across unprocessed and vocoded listening conditions for multiple stimulus sets. Stimulus sets were lists of digits (1 to 9), consonant-vowel-consonant (CVC) words (chosen from a list of 60 words), and CVC nonwords (chosen from a list of 50 nonwords). Stimuli were unprocessed or vocoded with an eight-channel noise vocoder. To support interpretation of responses, words and nonwords were selected to minimize inclusion of multiple phonemes from within a confusion cluster. We also measured receptive vocabulary (Peabody Picture Vocabulary Test [PPVT-4]), sustained attention (test of variables of attention [TOVA]), and repetition speed for individual items from each stimulus set under both listening conditions. Results: Vocoding stimuli had no impact on serial recall of digits, but reduced memory span for words and nonwords. This reduction in memory span was attributed to an increase in phonological confusions for nonwords. However, memory span for vocoded word lists remained reduced even after accounting for common phonetic confusions, indicating that lexical status played an additional role across listening conditions. Principal components analysis found two components that explained 84% of the variance in memory span across conditions. Component one had similar load across all conditions, indicating that participants had an underlying memory capacity, which was common to all conditions. Component two was loaded by performance in the vocoded word and nonword conditions, representing the sensitivity of memory span to vocoding of these stimuli. The order in which participants completed listening conditions had a small effect on memory span that could not account for the effect of listening condition. Repetition speed was fastest for digits, slower for words, and slowest for nonwords. On average, vocoding slowed repetition speed for all stimuli, but repetition speed was not predictive of individual memory span. Vocabulary and attention showed no correlation with memory span. Conclusions: Our results replicated previous findings that low quality auditory input can impair short-term memory, and demonstrated that this impairment is sensitive to stimulus set. Using multiple stimulus sets in degraded listening conditions can isolate memory capacity (in digit span) from impaired item identification (in word and nonword span), which may help characterize the relationship between memory and speech recognition in difficult listening conditions.

Serial Recall Predicts Vocoded Sentence Recognition Across Spectral Resolutions

“…Table 2 shows the results of principal components analysis for serial recall performance across stimuli and spectral resolutions. This analysis produced similar results as in the study of Bosen and Luckasen (2019). Component 1 is loaded roughly equally across all combinations of stimulus and spectral resolution and therefore reflects overall serial recall ability.…”

“…For serial recall, the proportion of correctly recalled digits and phonemes in each listening condition was used as predictors in the first stage of analysis. In addition, we replicated an analysis previously reported by Bosen and Luckasen (2019), in which principal components analysis was used to reduce dimensionality of serial recall task performance across stimuli and listening conditions. In that study, serial recall performance across sets of digits, words, and nonwords under clear and vocoded listening conditions could be explained by two principal components.…”

“…One practical consideration in using auditory serial recall to test individuals in adverse listening conditions is to distinguish between errors that arise from speech recognition difficulties and errors that arise from the limitations of memory. In our previous work (Bosen & Luckasen, 2019), we demonstrated that listener ability to remember and repeat lists of digits was unimpaired by limiting spectral resolution with an eight-channel vocoder, whereas performance for lists of words was impaired by vocoding. We found that two principal components accounted for most of the variance in serial recall performance across stimuli and listening conditions.…”

“…Sustained attention was assessed with the Test of Variables of Attention (TOVA; Greenberg, 2011). Although performance on the TOVA is not strongly correlated with serial recall performance (Bosen & Luckasen, 2019;Kronenberger et al, 2014), performance on the TOVA differs between listeners with cochlear implants and listeners with typical hearing (Kronenberger et al, 2013). Therefore, the TOVA is included not because we expect it to strongly correlate with vocoded speech recognition or other tests of cognition but rather to rule out sustained attention as an explanation for individual differences in speech recognition.…”

“…Session 2 comprised digit recall, PRESTO sentence recognition, and word recall. In our previous study, there was a trend toward increasing item identification accuracy when participants started with the vocoded listening condition, but not when they started with the unprocessed listening condition (Bosen & Luckasen, 2019). To avoid confounding effects of item learning affecting the results, spectral resolutions were presented in blocks from easiest to hardest conditions (16-, eight-, and four-channel vocoders, in that order).…”