https://orcid.org/0000-0002-6520-1968
Figures
Abstract
Cochlear implantation is a well-established method for restoring hearing sensation in individuals with severe to profound hearing loss. It significantly improves verbal communication for many users, despite substantial variability in patients’ reports and performance on speech perception tests and quality-of-life outcome measures. Such variability in outcome measures remains several years after implantation and could reflect difficulties in attentional regulation. The current study assessed the ability to use a cue to guide attention internally toward visual or auditory working memory (i.e., reflective attention) in cochlear implant (CI) users. Participants completed a cognitive task called the delayed match-to-sample task in which a visual gradient was presented on a computer screen and a piano tone was presented through speakers simultaneously. A visual cue (i.e., letter A or V) instructed participants to focus attention on the item held in auditory or visual working memory. After a delay following the cue presentation, participants were presented with a probe item and indicated by pressing a button whether it matched the cued item in working memory. CI users and age-matched normal hearing adults showed comparable benefit from having an informative cue relative to an uninformative cue (i.e., letter X). Although CI users have had a history of severe deafness and experience coarse sound information, they were able to retrospectively orient their attention to an item in auditory or visual working memory. These findings suggest that CI users with at least one year of CI experience can successfully regulate attention to a level that is comparable to that of normal hearing individuals.
Citation: Ojha A, Dimitrijevic A, Alain C (2024) Orienting attention to auditory and visual working memory in older adults with cochlear implants. PLoS ONE 19(12): e0310082. https://doi.org/10.1371/journal.pone.0310082
Editor: Jyrki Ahveninen, Harvard Medical School, UNITED STATES OF AMERICA
Received: January 4, 2024; Accepted: August 23, 2024; Published: December 26, 2024
Copyright: © 2024 Ojha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Regrettably, some but not all data could be included in this submission as it contains Personal Health Information, as mandated by the Research Ethics Board at Baycrest Health Sciences. Researchers who meet the criteria for access to confidential data may do so by contacting the the Research Ethics Board at Baycrest Health Sciences through Noah Koblinsky at nkoblinsky@research.baycrest.org or 416-785-2500 x 2440. Minimum relevant data is available in the Supporting Information files.
Funding: This research was supported by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery and the William Demant Foundation to Claude Alain [RGPIN- 2021-02721] and NSERC-Create Training Program in Complex Dynamics grant to Amisha Ojha. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Severe-to-profound sensorineural hearing loss can be partially restored using cochlear implants (CI). In adults who acquired hearing loss due to delayed onset or progressive genetic hearing loss, accidents, noise exposure, or viral infections, cochlear implantation is becoming the standard of care. It significantly improves speech perception in older adults [1], though there is substantial variability in speech perception and quality-of-life outcome measures. For instance, some CI users report improvements in language development, communication with others, voice recognition, awareness of sounds in the environment and ability to control their own voice [2]. However, other users report negative physical, physiological and emotional experiences that eventually drive their decision to discontinue the use of their implant. A consensus among users is difficulty understanding speech in noisy environments [2, 3]. Such variability in outcome measures remains several years after implantation and could be related to complex interactions between hearing restoration and cognition.
The prevalent view is that hearing restoration through cochlear implantation improves cognition in older adults [4–6]. However, the evidence supporting this assumption is mixed. Some studies report no difference in performance on cognitive measures taken before and after implantation [7, 8]. Castiglione et al. [9] reported a slight improvement on the Montréal cognitive assessment (MoCA) after cochlear implantation. There was no control group in that study, and the same MoCA test was used twice, making it difficult to rule out the practice effect. Other studies showed some improvement in performance at various cognitive tests after cochlear implantation [4–6, 10–13]. Still, these benefits were often limited to those individuals who experienced mild cognitive deficits before cochlear implantation. Lastly, the cognitive domain positively impacted by cochlear implantation varies substantially among the studies, with some studies reporting benefits in short-term, long-term, and working memory tasks [5, 9, 14], while others report benefits in attention [4], processing speed and overall cognition [4, 15].
The sensory-deprivation theory [16, 17] posits that cognitive abilities begin to decline due to the degradation of auditory input associated with hearing impairment. One open question is whether hearing rehabilitation by the use of a CI could mitigate the effects of auditory deprivation on cognition. Studies have examined the effectiveness of CIs in minimizing this effect and provide evidence for CI and hearing aid use resulting in greater cognition than those with hearing loss with no hearing device [18, 19]. However, other studies have not found a positive effect of hearing aids [20] or CIs on cognition [10, 13]. A possible explanation for these inconsistencies in the literature could be related to the degraded sound input of the CI and the continued cross-modal neuroplasticity observed in CI users. The effects of severe to profound deafness on the auditory cortex may remain even after hearing interventions with a CI and perhaps this continued impoverished auditory system is not capable of reversing the negative effects of hearing loss on cognition [21, 22]. The sensory-deprivation theory predicts that the relationship between hearing loss and cognition can be alleviated once hearing is restored.
Age-related hearing loss (ARHL), also known as presbycusis, is a progressive sensorineural hearing loss. Presbycusis has direct effects on communication, quality-of-life and several studies suggest that hearing loss is associated with decline in several cognitive domains including memory, attention, and speed of information processing [23]. Garami et al. [24] adapted a visual and auditory delayed match-to-sample task to examine whether age and presbycusis are associated with deficits in reflective attention. In this paradigm, participants were presented with a stimulus array of four different digits, two of which were presented visually (one in each hemifield) and two aurally (one in each ear). An informative or uninformative (neutral) retro-cue was presented during the retention interval to orient attention towards either the auditory or visual working memory. Older adults with normal hearing and those with presbycusis demonstrated benefit from informative auditory and visual retro-cues. However, benefit of the informative auditory retro-cue varied as a function of hearing status. Specifically, greater hearing loss was associated with reduced response time benefit when attention was cued to the auditory items in working memory. This implies that there may be a link between hearing loss and reflective attention. Older adults with hearing loss may be allocating more attentional resources to external auditory stimuli, decreasing the cognitive resources available to orient attention towards internal auditory information in working memory. Hearing loss may be reducing the effectiveness of retro-cues in facilitating this internal focus of attention, shown by the slowing of response [24].
One open research question is how hearing restoration with a CI impacts the regulation of attention and memory processes for both visual and auditory material. For instance, severe to profound hearing loss may negatively impact the development or utilization of working memory processes, such as reflective attention. Specifically, the natural inclination to mentally replay auditory experiences, often referred to as "listening back in time," may be diminished in adults with severe to profound hearing loss. This could be attributed to the absence or degradation of sound object representations. Evidence suggests that mild to moderate hearing loss is associated with deficits in retrospectively orienting attention and selecting an item in working memory [24]. This finding implies that hearing impairment may predict deficits in shifting attention to internal auditory representations. Consequently, one would anticipate that CI users would experience difficulties in retrospectively allocating attention to sound object representations in working memory.
Investigating auditory reflective attention in CI users could shed light on possible cognitive outcomes of hearing restoration, specifically attentional processes. Considering the sensory deprivation theory [16, 17], hearing restoration should reduce the draw on cognitive resources, which could be redistributed to other perceptual and cognitive domains. The current study examines whether older CI users could use a visual cue to orient their attention to a visual or an auditory item held in working memory. We measured response times and accuracy scores during a variant of the delayed match-to-sample paradigm that included a retro-cue during the retention interval, directing attention to visual or auditory working memory. We predicted that compared to age-matched controls, CI users will experience more difficulties in focusing and selecting items from auditory than visual working memory (i.e., reflective attention).
Methods
Participants
Thirty-two CI users and thirty-eight normal hearing controls were recruited between February 12, 2022, and May 4, 2023, from the patient population in the Department of Otolaryngology at Sunnybrook Health Science Centre and the participant database at the Rotman Research Institute, Baycrest Hospital. Five controls were excluded due to unrecorded responses, programming errors and a history of mild cognitive impairment. Four CI users were also excluded, of which were due to unrecorded responses, and study withdrawal. Regarding the unrecorded responses, it was observed that participants whose response exceeded the timed window for more than ten trials also performed at chance level. Therefore, participants who had ten or more missed trials were excluded from further analysis.
The final sample comprised of twenty-eight CI users aged between 60 and 86 years old (M (mean) = 72.1, SD (standard deviation) = 6.7; 21 males) (S1 Table) and thirty-three age-matched normal hearing controls between 60 and 86 years old (M = 71.6, SD = 7.7; 12 males) (S2 Table). The CI group consisted of eight bilateral CI users and twenty unilateral CI users, of which fourteen used a hearing aid on the contralateral ear. All CI users had at least one year of implantation. A priori power analysis was conducted using G*Power version 3.1.9.6 to estimate the required sample size based on a large effect size of .8 from Alain et al. [25], which compared auditory reflective attention between young and old adults. The minimum total sample size needed to achieve 80% power with a significance criterion of α = .05 for this large effect size was N = 52. We used a conservative approach and anticipated a smaller effect size in the present study and, therefore recruited 61 participants to test the study hypothesis. Informed written consent was obtained from participants in accordance with the guidelines established by the Research Ethics Board at Baycrest Health Sciences and Sunnybrook Health Sciences Centre. All participants received monetary compensation and parking passes at the respective hospital campus.
All participants completed hearing assessments and a behavioral task in a sound-attenuated booth. The hearing assessments included pure tone audiograms (PTA) for octave frequencies from 250–8000 Hz, and four-word lists in the Quick Speech-in-Noise (QSIN) test [26]. Only the NH group completed the PTA, whereas all participants were assessed with the QSIN. All but seven controls showed normal hearing thresholds in the lower frequencies (250–2000 Hz) with an upper cut-off of 20 decibel (dB) hearing level (HL) but had threshold shift to 50 dB HL at frequencies 4000–8000 Hz. These levels are consistent with normal age-related hearing loss at higher frequencies (4000–8000 Hz) as suggested by the American Speech-Language-Hearing Association [27]. All controls were administered the Montreal Cognitive Assessment (MoCA) [28], and CI users completed the Hearing-Impaired MoCA (HI-MoCA) [29], which were used to measure how general cognitive functioning is related to visual and auditory reflective attention. Age and assessment scores of all participants are shown in Table 1. The visual acuity of all participants was quantified as normal or corrected to normal using the Snellen Eye Chart [30].
Stimuli
The stimuli used in the study comprised of black and white 9 cm Gaussian gradients within a gray rectangle with a height of 16.5 cm and width of 22 cm, auditory piano tones presented at 75 dB sound pressure level (SPL), and white visual letters A, V, or X (Arial font) with the height of 2.5 cm. Gaussian gradients and the letters were presented at the center of a computer screen, while piano tones were presented binaurally through two speakers placed at a 45-degree angle from the participant. All piano tones were generated by Audacity Software, and Gaussian gradients ranging from an angle orientation of 5 to 360 degrees were created from the Visual Stimulus Generation Toolkit on Neurobehavioral Presentation Software (Version 16.3, www.neuro-bs.com). All stimuli were presented on a computer screen with a black background.
Experimental procedure
Familiarization phase.
Prior to beginning the experiment, all participants completed two simplified unimodal tasks. These tasks were used to familiarize the participants with the main task and to ensure a level of accuracy above chance and between 85–90% by adjusting the level of discriminability between the different stimuli.
This familiarization phase aimed to minimize the expected difference in pitch acuity between CI users and age-matched controls, so CI users and controls would be presented with stimuli that were closely matched in terms of their discriminability.
In the auditory delayed match-to-sample task, participants were first shown a white fixation cross for 1000 ms, followed by a 500 ms piano tone, a 1000 ms blank screen as a delay, followed by a second presentation of a 500 ms piano tone immediately following the first. Participants indicated whether the tones were identical or different by pressing the right or left arrow key, respectively, on a computer keyboard. The visual delayed match-to-sample task followed the same procedure with Gaussian gradients rather than piano tones. Each unimodal task consisted of 14 trials.
The physical difference between the gradients (i.e., visual angle rotation) and piano tone (i.e., frequency) varied between versions of the tasks. The difference in visual angle between gradients ranged from a random rotation of 25 to 60 degrees for the visual delayed match-to-sample task. For the auditory delayed match-to-sample, the difference in frequency varied from one-quarter tone to seven semitones.
All participants began the familiarization phase with a difference of a 30-degree visual angle rotation and two semitones for the visual and auditory delayed match-to-sample, respectively. They completed a block of 14 trials. If performance was lower than 85%, the physical difference was increased. If performance was at the ceiling (i.e., 100% accuracy), then the difference was reduced. This process was repeated until performance was approximately 85% accuracy in both auditory and visual working memory tasks. For each participant, the auditory and visual stimuli that yielded approximately 85% accuracy in the unimodal task was then incorporated into the audio-visual delayed match-to-sample task.
Audiovisual delayed match-to-sample
In the audio-visual delayed match-to-sample paradigm, participants were first shown a white fixation cross on a black background for 1000 ms, followed by a brief presentation of a piano tone and visual gradient simultaneously for 500 ms. Once the stimuli disappeared, a black screen was shown for 1000 ms, followed by another 500 ms presentation of a letter retro-cue that was either neutral (X) or informative (A or V to indicate whether to orient to auditory or visual modality, respectively). In trials with the informative A retro-cue, participants were tasked to remember the piano tone, whereas in trials with the informative V retro-cue, participants were instructed to remember the visual gradient. After another brief 2000 ms black screen, a unimodal test item was presented for 500 ms, and participants indicated whether the item matched with the auditory or visual stimuli held in their working memory (Fig 1). For instance, a trial with an informative A retro-cue would be followed by a piano tone as the test object, and a trial with an informative V retro-cue would be followed by a visual gradient as the test item. In the neutral X retro-cue condition, participants were required to retain both auditory and visual items in memory, as no indication was provided regarding which modality would be tested. The test item could originate from either modality, necessitating participants to match it with the corresponding item in their working memory. This condition accounted for 50% of the trials and required participants to be prepared for either test item.
On each trial, participants were first presented an image of a Gaussian gradient simultaneously with a sound of piano tone presented through two loudspeakers (placed at about an angle of 45 degrees relative to participants’ head) at a 65 dB SPL. After a delay, one of three retro-cues was presented visually to either orient attention to auditory, visual or neither working memory modalities. After another delay, a probe item was presented, and participants were tasked to indicate whether or not the probe was identical to the stimulus presented earlier.
The outcome measures were response time (RT) and accuracy. To retain as much data as possible for analysis while limiting bias of differing sample size, we used the non-recursive moving criterion method to remove outliers in RT [31]. This method removes RT data that falls outside a cut-off value determined by the standard deviation of a given sample size. The standard deviation used for the cut-off is thus altered based on the sample size.
The task was composed of 144 trials, divided into three blocks of 48 trials. Each block took approximately seven minutes to complete. Within a block of 48 trials, 50% were neutral (X) retro-cue. The remaining informative retro-cue trials were equally divided into auditory (A) and visual (V) retro-cues. The trial type (X, A, or V) occurred in random order.
Retro-cue benefit was calculated using RT data by comparing the benefit or cost of having an informative retro-cue relative to the neutral retro-cue. This was done for each participant using the following formula:
Performance in the audiovisual task was represented by the calculation of the d-prime (d’) value, a measure of discriminability derived by the signal detection theory [32]. The responses were labeled as either a hit, miss, false alarm or a correct rejection, in identifying whether the probe was identical or different than the item in the memory array. D-prime value was the difference between the z-transformed hit and false alarm rates.
Statistical analysis
RT and accuracy measures were subjected to mixed model analysis of variance (ANOVA), with condition (informative vs. neutral) and modality (visual vs. auditory) as within-subject factors and group (CI vs. NH) as a between-subject factor. For all follow-up pairwise comparisons, the p-value was corrected using the Bonferroni adjustment for multiple comparisons. All ANOVAs and correlations were two-tailed and the alpha criterion for Type I error was set at 0.05. All statistical analyses were run using IBM SPSS Statistics Version 28 and R Version 4.2.2.
Results
Cognitive and hearing assessments
Fig 2 shows the group mean performance on the MoCA and QSIN tests. There was a significant difference between groups on MoCA scores, t(59) = -4.43, p < .001, Cohen’s d = -1.138, with controls (M = 27.39, SD = 1.53) scoring higher than CI users (M = 24.96, SD = 2.67). Controls (M = 2.31, SD = 1.86) also performed significantly better (lower threshold) than CI users (M = 14.92, SD = 3.81) in the QSIN task, t(58) = 16.75, p < .001, Cohen’s d = 4.346.
MoCA scores between CI users and controls were significantly different. QSIN threshold of CI users and controls were also significantly different. Single dots represent the data points of each score. White circle represents the mean score of each group. **p < .001.
Familiarization phase
On average, CI users needed a larger difference in pitch to reach the performance criteria compared to NH participants, t(59) = 9.054, p < .001 (Fig 3). The difference in visual angle needed to reach the performance criteria was comparable in NH and CI users, t(59) = 1.827, p = .073.
Group mean difference in pitch and visual rotation to attain the accuracy criteria. White circle represents the mean score of each group. CI users needed a larger semitone difference to acquire a high level of accuracy. **p < .001, n.s. = non-significant.
We also examined whether the two groups differed in accuracy, which was defined as the proportion of correct responses from the last sequence used in the familiarization phase and included both hits and correct rejections. The analyses revealed a significant interaction between group and modality, F(1, 57) = 6.899, p = .011, η2 = .108 (Fig 4). Despite our attempt to adjust for perceptual difference in auditory acuity and equate performance between groups, CI users were less accurate (M = .862) than controls (M = .94), p < .001. For the visual modality, there was no significant difference in accuracy between CI users and age-matched controls.
All participants performed above chance level. Box represents the interquartile interval where 50% of the data lies. Lines in boxplot represent median accuracy score in each modality. **p < .001, n.s. = non-significant.
Performance during the audiovisual delayed-match-to sample task
The percent correct data did not have a normal distribution. We used D-prime values to assess the effect of group and retro-cue on accuracy. RT data were also not normally distributed and therefore were log-transformed before statistical analyses.
The mixed model ANOVA with a Greenhouse-Geisser correction was conducted with group as the between subject factor, stimulus modality and retro-cue type as the within subject factors and D-prime as the dependent variable. This analysis revealed a main effect of retro-cue type on D-prime, F(1, 58) = 17.79, p < .001, η2p = .235. Overall, participants were more accurate for informative (M = 2.695) than neutral (M = 2.337) cues. Participants also showed higher accuracy for auditory (M = 2.693) than visual (M = 2.339) stimuli, F(1, 58) = 7.509, p = .008, η2p = .115. The main effect of group on D-prime was also significant, F(1, 58) = 4.649, p = .035, η2p = .074, where controls (M = 2.698) were overall more accurate than CI users (M = 2.334).
Notably, the interaction between stimulus modality and group was significant, F(1, 58) = 12.273, p < .001, η2p = .175 (Fig 5A). Pairwise comparisons showed that controls (M = 3.121) were more accurate than CI users (M = 2.285) for auditory stimuli, p < .001 (Fig 5A). There was no significant difference between the two groups in accuracy for visual stimuli, p = .601. In the controls, accuracy was also higher for auditory than visual stimuli (p < .001), whereas no such difference was observed in the CI users (p = .603). Finally, the three-way interaction between group, stimulus modality and retro-cue type on accuracy was not significant, F(1, 58) = 1.728, p = .194, η2p = .029.
A) Group mean D-prime from cochlear implant (CI) users and normal hearing (NH) controls for both auditory and visual modality conditions regardless of cue type. There was a difference between CI and NH in the auditory domain. B) Mean D-prime in the auditory domain was not different between CI and NH when semitone differences were controlled. *p < .05, **p < .001, n.s. = non- significant. C) Group mean response time (RT) (log-transformed) for both auditory and visual modality conditions (informative and neutral). Lines in the box plot represent mean RT for each modality. There was a difference between CI and NH in the auditory domain. D) RT-log in the auditory domain was comparable between CI and NH when semitone differences were controlled. *p < .05, n.s. = non- significant.
Given that CI users required greater pitch difference to reach performance criteria for the audiovisual delayed match-to-sample task, we conducted an analysis of covariance (ANCOVA) to determine if the difference in sensitivity between groups in the auditory modality was influenced by participants’ perceptual discrimination ability. The difference between the two groups was not significant, F(1, 58) = 1.97, p = .166, η2p = .033 (Fig 5B) once semitone difference between groups was taken into account.
For the RT data, a similar mixed model ANOVA showed a main effect of retro-cue type, F(1, 59) = 157.299, p < .001, η2p = .727 (Fig 5C), with faster RT for informative than neutral cues. There was also a main effect of group, F(1, 59) = 6.485, p = .014, η2p = .099 (Fig 5D), where controls responded faster than CI users. The main effect of modality was not significant, F(1, 59) = .05, p = .823, η2p = .001(Fig 5C). The main effect of group was no longer significant, F(1, 58) = .038, p = .856, η2p = .001, once the semitone difference between groups was controlled (Fig 5D).
There was a significant interaction between group and modality, F(1, 59) = 8.738, p = .004, η2p = .129. Pairwise comparisons showed that controls were quicker to respond to auditory conditions than CI users (p = .008), whereas no difference between the groups was observed for visual stimuli (p = .233). Normal hearing controls were also faster for auditory than visual stimuli, p = .022 (Fig 5C). CI users showed no significant difference in RT between the two modalities, p = .068. The group by retro-cue type interaction was not significant, F(1, 59) = .576, p = .451, η2p = .01, nor was the interaction between retro-cue types and stimulus modality, F(1, 59) = 1.151, p = .288, η2p = .019. The three-way interaction between group, retro-cue type, and stimulus modality was not significant, F(1, 59) = .251, p = .6181, η2p = .004.
RT benefit of retro-cue
We also examined the effects of retro-cueing on RT benefit using a mixed model ANOVA with group (CI and controls) as the between-subjects factor and modality (auditory and visual) as the within-subjects factor. RT benefit was obtained by comparing the benefit or cost of having an informative relative to the neutral retro-cue, for both auditory and visual modalities. It minimized group and modality differences in absolute RTs. The mixed model ANOVA with a Greenhouse-Geisser correction revealed no main effect of modality on RT benefit, F(1, 59) = 1.253, p = .268, η2p = .021. There was no main effect of group on RT benefit, F(1, 59) = .531, p = .469, η2p = .009, nor was there a significant interaction between group and modality, F(1, 59) = .246, p = .622, η2p = .004. Altogether, the retro-cue benefit on RTs was comparable between CI users and age-matched controls, and this benefit did not differ between the two modalities.
The effect of biological sex on RT benefit was also examined because there were differences in the number of males and females between the two groups. There was no effect of sex on the overall RT benefit of both visual (t(59) = -1.614, p = .112, Cohen’s d = -.415) and auditory (t(59) = -.439, p = .662, Cohen’s d = -.113) modalities. Moreover, there was no significant difference in retro-cue benefit on RT between the two types of CI (bilateral or unilateral) for either the visual (t(26) = -1.03, p = .313, Cohen’s d = -.413) or auditory (t(26) = -.397, p = .695, Cohen’s d = -.166). There was also no significant difference between hearing aid use and no hearing aid for both retro-cue benefit in RT for visual (t(26) = 1.00, p = .326, Cohen’s d = .367) and auditory (t(26) = .911, p = .371, Cohen’s d = .344) modalities.
Correlations
Bivariate Spearman’s correlations were calculated between CI duration and retro-cue benefit of RT for both auditory and visual domains. All p-values were adjusted using False Discovery Rate (FDR) corrections for multiple comparisons. The correlation between CI duration and retro-cue benefit in RT for either modality was not significant. As an exploratory analysis, further bivariate Spearman’s and Pearson’s correlations were calculated between age, MoCA scores, QSIN threshold, PTA threshold and retro-cue benefit of RT for both auditory and visual domains. Spearman’s correlations were performed for data that were not normally distributed, whereas Pearson’s correlations were conducted for normally distributed data. All p-values were adjusted using FDR corrections for multiple comparisons. In CI users, there were no significant correlations between the outcome measures. As for the control group, there was a significant positive correlation between age and PTA threshold (rho = .659, p = .004), a positive correlation between age and QuickSIN scores, (r(24) = .407, p = .041), and a positive correlation between age and retro-cue benefit of RT in the visual domain, (r(24) = .634, p < .001).
Linear regressions
Linear regressions of the significant Pearson’s correlations were conducted to further determine if age was a predictor of speech perception and visual retro-cue benefit (Table 2). The analysis indicated that age was a significant predictor of speech-in-noise performance for the NH group (F(1, 24) = .226, p = .008, R2 = .226). Age was also a significant predictor of RT retro-cue benefit in the visual domain for NH (F(1, 24) = 16.12, p < .001, R2 = .377). No significant predictors were observed for the CI group.
Discussion
The role of age and hearing loss on attention to memory has been previously studied, suggesting that hearing loss, rather than age, may be a predictor of poor auditory reflective attention [24]. The aim of this study was to further explore the association between hearing status and reflective attention by examining reflective attention in CI users. Given the findings of Garami et al. [24], it was anticipated that CI users, who experience poor spectral and temporal sound information, would have greater difficulties orienting attention towards an auditory item in working memory compared to NH listeners.
Surprisingly, the results of the current study showed that CI users, like normal hearing age-matched controls, are able to orient attention towards an item held in auditory and visual working memory. There was no relationship between the number of years of implantation and the ability to orient attention towards either auditory or visual working memory. Although NH listeners responded faster and more accurately when recalling auditory items, the results suggest that CI users take advantage of the informative retro-cues at the same level as those who have normal hearing. Hearing restoration of severe to profound hearing loss by at least one year of CI use yields an ability to attend to items in memory at the normal hearing level.
We found that age was an important factor in predicting speech-in-noise perception performance and the ability to benefit from an informative visually orienting retro-cue. This suggests that ageing results in poor speech perception but greater benefit of a retro-cue facilitating retrospective attention towards visual items in working memory. Additionally, ageing was related to poor hearing threshold in controls, highlighting the relationship between ageing and hearing loss.
Altogether, the results of this study suggest that CI users, after one year of implantation, can retrospectively orient attention toward auditory working memory to the same extent as NH age-matched adults. This is surprising given that CI users’ overall performance was lower than controls. The latter is consistent with a more coarse acoustic input despite our attempt to match the perceptual discrimination of auditory stimuli in CI users and controls. Our findings suggest that auditory reflective attention may not be affected by hearing loss, as predicted by previous findings of Garami et al. [24]. However, this discrepancy between the two studies may be related to the difference in the number of items held in working memory (see below).
Reflective attention to auditory and visual working memory
Considering prior research showing a relationship between hearing acuity and reflective attention towards auditory items in working memory [24], it was predicted that the coarse auditory analysis experienced by CI users would induce greater difficulties in auditory reflective attention. Contrary to this prediction, the current study demonstrated similar reflective attention abilities in both visual and auditory modalities for CI users and NH controls. The overall advantage of informative retro-cues compared to neutral cues is in line with previous research suggesting the facilitative action of retro-cues in diminishing the costs or decay of retrieving items held in working memory [33–39]. The current study findings also add to the existing evidence suggesting that CIs may be able to restore auditory perception and thereby improve some cognitive functions to the level of normal hearing [40]. Considering the degraded sound resolution of CIs, older adults with CI who already experience age-related cognitive decline would further require more cognitive resources for top-down information processing. However, in the case of the present study, older CI listeners were able to rely on contextual cues to recover internal information in the same way as NH older adults. An explanation for this observation could be due to the design of the delayed match-to-sample task. Unlike recent studies examining reflective attention with set sizes of three to four items [24, 41, 42], the current task only consisted of two items, eliciting a lower level of task difficulty. Furthermore, the retro-cues in the present study reflected attention toward a single item in one of two sensory modalities. In Garami et al. [24], the retro-cues oriented attention towards auditory or visual working memory, which comprised two items. Given that in the present study, there was only a single item in visual or auditory working memory, our paradigm did not require searching through and selecting an item within modality-specific working memory systems. This difference could explain why there was no difference in reflective attention between the two groups. Our findings show that CI users can orient attention towards a relevant modality, but further research is needed to determine whether CI users would also show comparable performance when the task requires searching and selecting an item within modality-specific working memory systems.
There was, however, a general slowing of responses and poor performance pattern in orienting to an auditory item from working memory in CI users compared to normal hearing when differences in discriminability were not controlled. The difference in RT and performance in attending to auditory items could be related to CI users’ poor discriminability of auditory stimuli used in this study. Although we attempted to equate the discriminability of the stimuli between CI and NH listeners with the unimodal task, a fully equal ability to discriminate between two auditory items could not be confirmed. However, once the effects of auditory discriminability on performance and RT were controlled statistically, CI users performed as accurately and as fast as NH listeners. This suggests that although CI users may require a greater perceptual difference between two auditory items to discriminate the two, they can redirect attention to auditory working memory with similar processing speed and accuracy as NH.
Consistent with the present study findings, Luo et al. [43] found similar cuing effects in older CI users and older adults with mild to moderate hearing loss. There was, however, a difference arising from age rather than CI-related declines in working memory and attention when comparing cuing effects with young normal hearing listeners [43]. The lack of group difference in reflective attention observed in the current study may be explained by the prediction that attention to memory deficits are more age-related than hearing-related. Luo et al. [43] also demonstrated greater recall for auditory than visual items, regardless of the cueing condition. Similarly, the present study also showed greater performance and RT for auditory items than visual, regardless of the cueing condition, but only for NH adults. Auditory and visual items were not perceptually equated in the present study, and therefore it is possible that the auditory stimuli were more salient than the visual items, resulting in greater accuracy and RT.
Visual performance in CI users
Surprisingly, unlike NH controls, CI users demonstrated no significant difference between auditory and visual conditions in variability of response time or accuracy; however, the results trended towards faster and more accurate recall of visual items. Interestingly, CI users show faster responses for visual than auditory items. These findings could be a result of neuroplastic changes related to hearing loss. Given its lack of use, the cortical representation of a deprived sensory system undergoes shrinkage, along with a takeover by the remaining sensory modalities [44]. Research has shown that an absence of auditory input over time can lead to cross-modal plasticity in reorganizing the auditory brain areas for visual processing [45, 46]. The brain tends to perform at supranormal levels in one or more of the intact sensory systems, particularly in the visual system, resulting in compensatory visual function [45]. Prior research on congenitally deaf individuals has shown that the auditory cortex is more activated for visual processing than NH listeners [47–53]. Although limited, current work on post lingually deaf individuals has also reported evidence for visual take-over of the auditory cortex [54]. Moreover, this cross-modal plasticity is experienced not only by deaf individuals but also by blind individuals who compensate for their sensory loss through the remaining intact senses [55]. The brain can rewire its components due to challenges such as sensory deprivation, aiming to adapt and achieve optimal performance using the information from the intact sensory system.
CI users undergo prolonged periods of severe to profound deafness and can exhibit compensatory visual behaviors, but the ability of CIs to reverse cross-modal plasticity is still being explored. CIs produce coarse sound representations, and thus partial hearing recovery may result in an incomplete reversal of cortical changes induced by hearing loss. Although studies have indicated greater responses in the auditory cortex during auditory processing post-implantation, cortical responses differ among CI users [56, 57]. Regarding cross-modal reorganization, studies have found greater activation in the auditory cortex of CI users than NH when processing visual stimuli [58–62]. Cross-modal reorganization of the auditory cortex for visual processing may not be reversed after CI use. This activation pattern has been found to be positively related to lip-reading skills, providing possible functionality of the incomplete reversal of cross-modal reorganization of the auditory cortex [61]. This continued cortical reorganization is beneficial for communication in CI users as it enhances the visual component of speech [63, 64].
Interestingly, some studies have revealed possible visual cortex activation during auditory processing in CI users, even greater visual activation compared to NH listeners [57, 58]. Song et al. [21] found greater visual activation during the audiovisual speech, and weaker activation of auditory brain areas, such as the posterior superior temporal sulcus, during auditory only conditions in CI users. These findings suggest cross-modal visual cortex activation, shedding light on the new patterns of cortical reorganization emerging in CI users. CIs may be unable to reverse cross-modal plasticity once hearing is restored, and perhaps auditory processing exhibits continued reliance on the visual system [21]. This cortical organization has also been positively related to lip-reading abilities, suggesting possible benefits in facilitating communication in CI users [61].
The current study findings indicate that hearing restoration enables CI users to attend to visual items more efficiently, potentially due to continued cross-modal plasticity. This reliance on visual cues may extend to tasks involving retrospective attention, where CI users may benefit from visual representations in memory. Although not significant in this study, the trend suggests a potential role of visual contextual cues in facilitating attentional processes among CI users. The findings of this study suggest that CI users may rely more on visual processing due to continued cross-modal plasticity, highlighting the need for further investigation into the mechanisms underlying sensory reorganization in individuals with hearing loss.
Limitations
The results suggest that CI users can effectively allocate attention to a representation in auditory and visual working memory. This is unexpected in light of prior research showing that older adults with hearing loss struggle to allocate attention to sound object representation [24]. However, the experimental paradigm used in our study with CI users differed markedly from other paradigms used to investigate reflective attention. One significant difference relates to the number of items participants must hold in working memory. In our CI study, participants held one item in visual and auditory working memory. Prior research used at least two items per modality [24, 25, 41, 42, 65]. The selection of our specific paradigm was influenced by practical considerations inherent to the cochlear implant (CI) user population. Unlike individuals with normal hearing, CI users may face limitations in simultaneously holding multiple auditory information in working memory. Our simplified paradigm, focusing on a single auditory and visual item in working memory, allowed us to effectively assess the basic level of attentional allocation in CI users. Therefore, the discrepancy between our findings in CI users and those from our previous aging study [24] could be related to the experimental design. It is still being determined whether CI users would also show comparable benefits from the retro-cue if the task involved more than one item in auditory working memory. Increasing the number of items in working memory likely engages a “search” through memory and a selection process, which were unnecessary to perform our simplified version of the delayed match-to-sample task.
In addition, it is important to acknowledge the potential limitations inherent to the design and methodology of this study. Firstly, the sample size of our study may have been insufficient to detect subtle differences or effects, particularly given the heterogeneity within CI users. A larger sample size would enhance the statistical power of our analysis and provide more robust conclusions. Additionally, the absence of equivalence testing in our study design is noteworthy. Equivalence testing would have allowed for a more comprehensive examination of the similarities or differences between CI users and NH peers. Future research should aim to address these methodological limitations to ensure more conclusive and generalizable results.
Conclusions
The ability to reflect attention to a visual or auditory item in memory, or in other words, refresh memory of a just heard or just seen item, is not affected by the low spectral-temporal resolution of CIs. CI users can refresh their memory in a way similar to that of typical hearing age-matched adults. This ability is at the normal hearing level after one year of implantation and does not differ with greater experience of CI. This was evident as CI users could benefit from a retro-cue that prioritizes a just-remembered item by attending to the cued sensory modality, either visual or auditory. More importantly, the degraded sound information processed by CI users does not limit their ability to attend to an auditory item in their working memory, provided that the auditory stimulus is clearly distinguishable from the item in memory. Future studies should expand this finding by exploring the ability to search and select items in memory using larger set sizes of items. Electrophysiological methods should be employed in these studies to determine if neural processing during reflective attention is affected by hearing acuity when behavioral measures demonstrate comparable performance between groups, as seen in previous findings [66]. Studies should continue to investigate the association of hearing and cognition to help identify processes that would benefit from auditory rehabilitation with the overall aim of improving communication in those who are hard of hearing.
Supporting information
S1 Table. Demographic of CI participants including implanted side, CI duration, MoCA and QSIN scores.
https://doi.org/10.1371/journal.pone.0310082.s001
(TIFF)
S2 Table. Demographic of NH participants including MoCA, QSIN and PTA scores.
https://doi.org/10.1371/journal.pone.0310082.s002
(TIFF)
Acknowledgments
We would like to express our gratitude to Dr. Karen Gordon and Dr. Jay Pratt for their invaluable suggestions on experimental design and data interpretation, which greatly enriched the quality of this manuscript. We also thank Madison Grassi and Mary O’Neil for their diligent efforts in participant recruitment. Additionally, we acknowledge Madison Grassi for her assistance in data collection.
References
- 1. Budenz CL, Cosetti MK, Coelho DH, Birenbaum B, Babb J, Waltzman SB, et al. The Effects of Cochlear Implantation on Speech Perception in Older Adults. J Am Geriatr Soc. 2011 Mar;59(3):446–53; pmid:21361884
- 2. Salehomoum M. Cochlear Implant Nonuse: Insight from Deaf Adults. J Deaf Stud Deaf Educ. 2020;25(4):411–422;
- 3. Rich S, Levinger M, Werner S, Adelman C. Being an adolescent with a cochlear implant in the world of hearing people: Coping in school, in society, and with self-identity. Int J Pediatr Otorhinolaryngol. 2013;77:1337–1344; pmid:23800472
- 4. Mertens G, Andries E, Claes AJ, Topsakal V, Van de Heyning P, Van Rompaey V, et al. Cognitive Improvement After Cochlear Implantation in Older Adults With Severe or Profound Hearing Impairment: A Prospective, Longitudinal, Controlled, Multicenter Study. Ear Hear. 2021 May;42(3):606–14; pmid:33055579
- 5. Völter C, Götze L, Haubitz I, Müther J, Dazert S, Thomas JP. Impact of Cochlear Implantation on Neurocognitive Subdomains in Adult Cochlear Implant Recipients. Audiology and Neurotology. 2021; pmid:33440376
- 6. Völter C, Götze L, Kamin ST, Haubitz I, Dazert S, Thomas JP. Can cochlear implantation prevent cognitive decline in the long-term follow-up? Front Neurol. 2022 Oct 20;13; pmid:36341108
- 7. Crary WG, Wexler M, Berliner KI, Miller LW. Psychometric studies and clinical interviews with cochlear implant patients. Ann Otol Rhinol Laryngol Suppl. 1982;91(2 Pt 3):55–8; pmid:6805398
- 8. Mosnier I, Vanier A, Bonnard D, Lina-Granade G, Truy E, Bordure P, et al. Long-Term Cognitive Prognosis of Profoundly Deaf Older Adults After Hearing Rehabilitation Using Cochlear Implants. J Am Geriatr Soc. 2018; pmid:30091185
- 9. Castiglione A, Benatti A, Velardita C, Favaro D, Padoan E, Severi D, et al. Aging, Cognitive Decline and Hearing Loss: Effects of Auditory Rehabilitation and Training with Hearing Aids and Cochlear Implants on Cognitive Function and Depression among Older Adults. In: Audiology and Neurotology. 2016;
- 10. Ambert-Dahan E, Routier S, Marot L, Bouccara D, Sterkers O, Ferrary E, et al. Cognitive Evaluation of Cochlear Implanted Adults Using CODEX and MoCA Screening Tests. Otology and Neurotology. 2017; pmid:28806339
- 11. Gurgel RK, Duff K, Foster NL, Urano KA, deTorres A. Evaluating the Impact of Cochlear Implantation on Cognitive Function in Older Adults. Laryngoscope. 2022 Apr;132 Suppl 7(Suppl 7):S1–15; pmid:34738240
- 12. Issing C, Baumann U, Pantel J, Stöver T. Impact of Hearing Rehabilitation Using Cochlear Implants on Cognitive Function in Older Patients. Otology and Neurotology. 2021; pmid:33782260
- 13. Mosnier I, Bebear JP, Marx M, Fraysse B, Truy E, Lina-Granade G, et al. Improvement of cognitive function after cochlear implantation in elderly patients. JAMA Otolaryngol Head Neck Surg. 2015; pmid:25763680
- 14. Cosetti MK, Pinkston JB, Flores JM, Friedmann DR, Jones CB, Roland JT, et al. Neurocognitive testing and cochlear implantation: Insights into performance in older adults. Clin Interv Aging. 2016; pmid:27274210
- 15. Huber M, Roesch S, Pletzer B, Lukaschyk J, Lesinski-Schiedat A, Illg A. Can cochlear implantation in older adults reverse cognitive decline due to hearing loss? Ear Hear. 2021; pmid:34028233
- 16. Pichora-Fuller MK, Schneider BA, Daneman M. How young and old adults listen to and remember speech in noise. J Acoust Soc Am. 1995 Jan 1;97(1):593–608. pmid:7860836
- 17. Rabbitt P. Mild hearing loss can cause apparent memory failures which increase with age and reduce with IQ. Acta Otolaryngol. 1991 Jan 8;111(sup476):167–76;
- 18. Amieva H, Ouvrard C, Giulioli C, Meillon C, Rullier L, Dartigues JF. Self-reported hearing loss, hearing aids, and cognitive decline in elderly adults: A 25-year study. J Am Geriatr Soc. 2015; pmid:26480972
- 19. Dawes P, Emsley R, Cruickshanks KJ, Moore DR, Fortnum H, Edmondson-Jones M, et al. Hearing loss and cognition: the role of hearing aids, social isolation and depression. PLoS One. 2015;10(3); pmid:25760329
- 20. Van Hooren SAH, Anteunis LJC, Valentijn SAM, Bosma H, Ponds RWHM, Jolles J, et al. Does cognitive function in older adults with hearing impairment improve by hearing aid use? Int J Audiol. 2005; pmid:16028789
- 21. Song JJ, Lee HJ, Kang H, Lee DS, Chang SO, Oh SH. Effects of congruent and incongruent visual cues on speech perception and brain activity in cochlear implant users. Brain Struct Funct. 2015; pmid:24402676
- 22. Doucet ME, Bergeron F, Lassonde M, Ferron P, Lepore F. Cross-modal reorganization and speech perception in cochlear implant users. Brain. 2006;129(12):3376–3383; pmid:17003067
- 23. Fortunato S, Forli F, Guglielmi V, De Corso E, Paludetti G, Berrettini S, et al. Ipoacusia e declino cognitivo: revisione della letteratura. Acta Otorhinolaryngol Ital. 2016;36(3):155–166;
- 24. Garami L, Chow R, Fakuade A, Swaminathan S, Alain C. Orienting Attention to Auditory and Visual Short-term Memory: The Roles of Age, Hearing Loss, and Cognitive Status. Exp Aging Res. 2020; pmid:31750789
- 25. Alain C, Cusimano M, Garami L, Backer KC, Habelt B, Chan V, et al. Age-related differences in orienting attention to sound object representations. Neurobiol Aging. 2018; pmid:29501965
- 26. Killion MC, Niquette PA, Gudmundsen GI, Revit LJ, Banerjee S. Development of a quick speech-in-noise test for measuring signal-to-noise ratio loss in normal-hearing and hearing-impaired listeners. J Acoust Soc Am. 2004; pmid:15532670
- 27.
Clark JG. Uses and abuses of hearing loss classification. ASHA. 1981;
- 28. Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;
- 29. Lin VYW, Chung J, Callahan BL, Smith L, Gritters N, Chen JM, et al. Development of cognitive screening test for the severely hearing impaired: Hearing-impaired MoCA. Laryngoscope. 2017; pmid:28409842
- 30. Tsui E, Patel P. Calculated Decisions: Visual acuity testing (Snellen chart). Emerg Med Pract. 2020; pmid:32259420
- 31. Van Selst M, Jolicoeur P. The Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology A solution to the effect of sample size on outlier elimination. Q J Exp Psychol. 1994;
- 32.
Macmillan NA, Creelman CD. Detection Theory [Internet]. Detection Theory: A User’s Guide: 2nd edition. Psychology Press. 2004;
- 33. Griffin IC, Nobre AC. Orienting Attention to Locations in Internal Representations. J Cogn Neurosci. 2003; pmid:14709235
- 34. Kuo BC, Stokes MG, Nobre AC. Attention modulates maintenance of representations in visual short-term memory. J Cogn Neurosci. 2012; pmid:21736457
- 35. Landman R, Spekreijse H, Lamme VAF. Large capacity storage of integrated objects before change blindness. Vision Res. 2003; pmid:12536137
- 36. Lepsien J, Griffin IC, Devlin JT, Nobre AC. Directing spatial attention in mental representations: Interactions between attentional orienting and working-memory load. Neuroimage. 2005 Jul; pmid:15955482
- 37. Lepsien J, Thornton I, Nobre AC. Modulation of working-memory maintenance by directed attention. Neuropsychologia. 2011; pmid:21420421
- 38. Lepsien J, Nobre AC. Cognitive control of attention in the human brain: Insights from orienting attention to mental representations. Brain Res. 2006; pmid:16729979
- 39. Backer KC, Alain C. Attention to memory: Orienting attention to sound object representations. Psychological Research. 2014. pmid:24352689
- 40. Sorrentino T, Donati G, Nassif N, Pasini S, Redaelli de Zinis LO. Cognitive function and quality of life in older adult patients with cochlear implants. Int J Audiol. 2020; pmid:31793801
- 41. Backer KC, Alain C. Orienting attention to sound object representations attenuates change deafness. J Exp Psychol Hum Percept Perform. 2012; pmid:22506788
- 42. Backer KC, Binns MA, Alain C. Neural Dynamics Underlying Attentional Orienting to Auditory Representations in Short-Term Memory. The Journal of Neuroscience. 2015 Jan 21;35(3):1307–18. pmid:25609643
- 43. Luo X, Azuma T, Kolberg C, Pulling KR. The effects of stimulus modality, task complexity, and cuing on working memory and the relationship with speech recognition in older cochlear implant users. J Commun Disord. 2022; pmid:34839068
- 44. Stropahl M, Chen LC, Debener S. Cortical reorganization in postlingually deaf cochlear implant users: Intra-modal and cross-modal considerations. Hear Res. 2017; pmid:27473503
- 45. Lomber SG, Meredith MA, Kral A. Cross-modal plasticity in specific auditory cortices underlies visual compensations in the deaf. Nat Neurosci. 2010; pmid:20935644
- 46. Merabet LB, Pascual-Leone A. Neural reorganization following sensory loss: the opportunity of change. Nat Rev Neurosci [Internet]. 2010 Jan 25;11(1):44–52. Available from: http://www.nature.com/articles/nrn2758 pmid:19935836
- 47. Bottari D, Heimler B, Caclin A, Dalmolin A, Giard MH, Pavani F. Visual change detection recruits auditory cortices in early deafness. Neuroimage. 2014 Jul;94:172–84. pmid:24636881
- 48. Fine I, Finney EM, Boynton GM, Dobkins KR. Comparing the Effects of Auditory Deprivation and Sign Language within the Auditory and Visual Cortex. J Cogn Neurosci. 2005 Oct 1;17(10):1621–37. pmid:16269101
- 49. Finney EM, Clementz BA, Hickok G, Dobkins KR. Visual stimuli activate auditory cortex in deaf subjects: Evidence from MEG. Neuroreport. 2003; pmid:12960757
- 50. Finney EM, Fine I, Dobkins KR. Visual stimuli activate auditory cortex in the deaf. Nat Neurosci. 2001; pmid:11704763
- 51. Karns CM, Dow MW, Neville HJ. Altered Cross-Modal Processing in the Primary Auditory Cortex of Congenitally Deaf Adults: A Visual-Somatosensory fMRI Study with a Double-Flash Illusion. Journal of Neuroscience. 2012 Jul 11;32(28):9626–38. pmid:22787048
- 52. Shiell MM, Champoux F, Zatorre RJ. Reorganization of Auditory Cortex in Early-deaf People: Functional Connectivity and Relationship to Hearing Aid Use. J Cogn Neurosci. 2015 Jan 1;27(1):150–63. pmid:25000527
- 53. Vachon P, Voss P, Lassonde M, Leroux JM, Mensour B, Beaudoin G, et al. Reorganization of the auditory, visual and multimodal areas in early deaf individuals. Neuroscience. 2013 Aug;245:50–60. pmid:23590908
- 54. Lee HJ, Truy E, Mamou G, Sappey-Marinier D, Giraud AL. Visual speech circuits in profound acquired deafness: a possible role for latent multimodal connectivity. Brain. 2007 Apr 5;130(11):2929–41. pmid:17906328
- 55. Collignon O, Voss P, Lassonde M, Lepore F. Cross-modal plasticity for the spatial processing of sounds in visually deprived subjects. In: Experimental Brain Research. 2009. pmid:18762928
- 56. Sandmann P, Plotz K, Hauthal N, de Vos M, Schönfeld R, Debener S. Rapid bilateral improvement in auditory cortex activity in postlingually deafened adults following cochlear implantation. Clinical Neurophysiology. 2015 Mar;126(3):594–607. pmid:25065298
- 57. Giraud AL. Functional plasticity of language-related brain areas after cochlear implantation. Brain. 2001 Jul 1;124(7):1307–16. pmid:11408326
- 58. Chen LC, Sandmann P, Thorne JD, Bleichner MG, Debener S. Cross-Modal Functional Reorganization of Visual and Auditory Cortex in Adult Cochlear Implant Users Identified with fNIRS. Neural Plast. 2016;2016:1–13. pmid:26819766
- 59. Rouger J, Lagleyre S, Démonet J, Fraysse B, Deguine O, Barone P. Evolution of crossmodal reorganization of the voice area in cochlear‐implanted deaf patients. Hum Brain Mapp. 2012 Aug 6;33(8):1929–40. pmid:21557388
- 60. Sandmann P, Dillier N, Eichele T, Meyer M, Kegel A, Pascual-Marqui RD, et al. Visual activation of auditory cortex reflects maladaptive plasticity in cochlear implant users. Brain. 2012 Feb 1;135(2):555–68. pmid:22232592
- 61. Stropahl M, Plotz K, Schönfeld R, Lenarz T, Sandmann P, Yovel G, et al. Cross-modal reorganization in cochlear implant users: Auditory cortex contributes to visual face processing. Neuroimage. 2015 Nov;121:159–70. pmid:26220741
- 62. Paul BT, Bajin MD, Uzelac M, Chen J, Le T, Lin V, et al. Evidence of visual crossmodal reorganization positively relates to speech outcomes in cochlear implant users. Sci Rep. 2022 Oct 22;12(1):17749. pmid:36273017
- 63. Desai S, Stickney G, Zeng FG. Auditory-visual speech perception in normal-hearing and cochlear-implant listeners. J Acoust Soc Am. 2008 Jan 1;123(1):428–40. pmid:18177171
- 64.
Kawase T, Hori Y, Ogawa T, Sakamoto S, Suzuki Y, Katori Y. Importance of Visual Cues in Hearing Restoration by Auditory Prosthesis. In: Interface Oral Health Science 2014. Tokyo: Springer Japan; 2015. p. 119–27.
- 65. Backer KC, Buchsbaum BR, Alain C. Orienting Attention to Short-Term Memory Representations via Sensory Modality and Semantic Category Retro-Cues. eNeuro. 2020 Nov;7(6):ENEURO.0018-20.2020. pmid:33139321
- 66. Prince P, Paul BT, Chen J, Le T, Lin V, Dimitrijevic A. Neural correlates of visual stimulus encoding and verbal working memory differ between cochlear implant users and normal-hearing controls. European Journal of Neuroscience. 2021; pmid:34146363