Allocation Of Memory Resources Research Articles

Limitations on flexible allocation of visual short-term memory resources with multiple levels of goal-directed attentional prioritization.

Studies suggest that visual short-term memory (VSTM) is a continuous resource that can be flexibly allocated using probabilistic cues that indicate test likelihood (i.e., goal-directed attentional priority to those items). Previous studies using simultaneous cues have not examined this flexible allocation beyond two distinct levels of priority. Moreover, previous studies have not examined whether there are individual differences in the ability to flexibly allocate VSTM resources, as well as whether this ability benefits from practice. The current study used a continuous report procedure to examine whether participants can use up to three levels of attentional priority to allocate VSTM resources via simultaneous probabilistic spatial cues. Three experiments were performed with differing priority levels, cues, and cue presentation times. Group level analysis demonstrated flexible allocation of VSTM resources; however, there was limited evidence that participants could use three goal-directed priority levels. A temporal analysis suggested that task fatigue, rather than practice effects, may interact with item priority. A Bayesian individual-differences analysis revealed that a minority of participants were using three levels of attentional priority, demonstrating that, while possible, it is not the predominant pattern of behaviour. Thus, we provided evidence that flexible allocation to three attention levels is possible under simultaneous cuing conditions for a minority of participants. Flexible allocation to three categories may be interpreted as a skill of high-performing participants akin to high memory capacity.

Optimal FPGA memory allocation for image processing

In the field of computer vision, Field Programmable Gate Array (FGPA) limited de on-chip memory is difficult to meet the power, size and other requirements. To address this phenomenon, the study constructs a partitioning algorithm to achieve a balance between energy consumption and resource utilisation based on the analysis of memory resource allocation, overall power consumption and resource utilisation from the perspective of image processing technology. The power consumption of the balancing algorithm is lower compared to the optimised utilisation algorithm HLS tool, with both Block Ramdom Access Memory (BRAM) power consumption taking the value of 0.005; the dynamic power consumption takes the value range of 0.014–0.082. Compared to the High Level Synthesis (HLS) tool, the overall power consumption of the balancing algorithm and the optimised utilisation algorithm is significantly lower, with the values of 0.251 and 0.252 respectively, both with a reduction rate of approximately 30%. The accuracy rate of the proposed memory optimisation allocation algorithm is the highest among the four memory optimisation allocation algorithms and strategies on all three types of target scales. FPGA memory optimisation allocation strategy can guarantee to have lower power consumption while satisfying the same resource occupancy, and the model has in-depth application value in visual image vision technology.

Neural Network Compression for Noisy Storage Devices

Compression and efficient storage of neural network (NN) parameters is critical for applications that run on resource-constrained devices. Despite the significant progress in NN model compression, there has been considerably less investigation in the actual physical storage of NN parameters. Conventionally, model compression and physical storage are decoupled, as digital storage media with error-correcting codes (ECCs) provide robust error-free storage. However, this decoupled approach is inefficient as it ignores the overparameterization present in most NNs and forces the memory device to allocate the same amount of resources to every bit of information regardless of its importance. In this work, we investigate analog memory devices as an alternative to digital media – one that naturally provides a way to add more protection for significant bits unlike its counterpart, but is noisy and may compromise the stored model’s performance if used naively. We develop a variety of robust coding strategies for NN weight storage on analog devices, and propose an approach to jointly optimize model compression and memory resource allocation. We then demonstrate the efficacy of our approach on models trained on MNIST, CIFAR-10, and ImageNet datasets for existing compression techniques. Compared to conventional error-free digital storage, our method reduces the memory footprint by up to one order of magnitude, without significantly compromising the stored model’s accuracy.

A High-Throughput Full-Dataflow MobileNetv2 Accelerator on Edge FPGA

FPGA accelerators for lightweight neural networks such as MobileNetv2 are of great need in edge computing applications with high throughput requirements. Dataflow architecture has been considered a promising approach to optimize throughput since the intermediate feature map transfers can be significantly saved. However, previous MobileNetv2 accelerators only achieved a partial-dataflow architecture, and just one-third of the feature map transfers can be saved. To solve this issue, we propose a scheme to achieve a full-dataflow MobileNetv2 accelerator on FPGA. The scheme contains four techniques. First, we improve the full-integer quantization for easier deployment on hardware. Second, we propose tunable activation weight imbalance transfer for less quantization accuracy loss. Third, we present several highly optimized accelerator components whose parallelism can be flexibly adjusted, and implement residual connection with deeper FIFO so that the requirements of the full-dataflow architecture can be fully met. Finally, we present a computing resource allocation strategy to balance the latency of each layer, and a memory resource allocation strategy to effectively use the on-chip memory. Compared to the state-ofthe-art, experimental results show that the accelerator achieves 1910 FPS with 1.8× speedup when implemented on the Xilinx ZCU102 FPGA. In addition, it reaches 72.98% Top-1 accuracy with 8-bit integer quantization that outperforms all the other MobileNetv2 accelerators.

Increasing perceptual separateness affects working memory for depth - re-allocation of attention from boundaries to the fixated center.

For decades, working memory (WM) has been a heated research topic in the field of cognitive psychology. However, most studies on WM presented visual stimuli on a two-dimensional plane, rarely involving depth perception. Several previous studies have investigated how depth information is stored in WM, and found that WM for depth is even more limited in capacity and the memory performance is poor compared to visual WM. In the present study, we used a change detection task to investigate whether dissociating memory items by different visual features, thereby to increase their perceptual separateness, can improve WM performance for depth. Memory items presented at various depth planes were bound with different colors (Experiments 1 and 3) or sizes (Experiment 2). The memory performance for depth locations of visual stimuli with homogeneous and heterogeneous appearances were tested and compared. The results showed a consistent pattern that although separating items with various feature values did not affect the overall memory performance, the manipulation significantly improved memory performance for the middle depth locations but impaired the performance for the boundary locations when observers fixated at the center of the whole depth volume. The memory benefits of feature separation can be attributed to enhanced individuation of memory items, therefore facilitating a more balanced allocation of attention and memory resources.

QuickLD: An efficient software for linkage disequilibrium analyses.

Software tools for linkage disequilibrium (LD) analyses are designed to calculate LD among all genetic variants in a single region. Since compute and memory requirements grow quadratically with the distance between variants, using these tools for long‐range LD calculations leads to long execution times and increased allocation of memory resources. Furthermore, widely used tools do not fully utilize the computational resources of modern processors and/or graphics processing cards, limiting future large‐scale analyses on thousands of samples. We present quickLD, a stand‐alone and open‐source software that computes several LD‐related statistics, including the commonly used r 2. quickLD calculates pairwise LD between genetic variants in a single region or in arbitrarily distant regions with negligible memory requirements. Moreover, quickLD achieves up to 95% and 97% of the theoretical peak performance of a CPU and a GPU, respectively, enabling 21.5× faster processing than current state‐of‐the‐art software on a multicore processor and 49.5× faster processing when the aggregate processing power of a multicore CPU and a GPU is harnessed. quickLD can also be used in studies of selection, recombination, genetic drift, inbreeding and gene flow. The software is available at https://github.com/pephco/quickLD.

The Effects of Task Difficulty Predictability and Noise Reduction on Recall Performance and Pupil Dilation Responses.

Objectives:Communication requires cognitive processes which are not captured by traditional speech understanding tests. Under challenging listening situations, more working memory resources are needed to process speech, leaving fewer resources available for storage. The aim of the current study was to investigate the effect of task difficulty predictability, that is, knowing versus not knowing task difficulty in advance, and the effect of noise reduction on working memory resource allocation to processing and storage of speech heard in background noise. For this purpose, an “offline” behavioral measure, the Sentence-Final Word Identification and Recall (SWIR) test, and an “online” physiological measure, pupillometry, were combined. Moreover, the outcomes of the two measures were compared to investigate whether they reflect the same processes related to resource allocation.Design:Twenty-four experienced hearing aid users with moderate to moderately severe hearing loss participated in this study. The SWIR test and pupillometry were measured simultaneously with noise reduction in the test hearing aids activated and deactivated in a background noise composed of four-talker babble. The task of the SWIR test is to listen to lists of sentences, repeat the last word immediately after each sentence and recall the repeated words when the list is finished. The sentence baseline dilation, which is defined as the mean pupil dilation before each sentence, and task-evoked peak pupil dilation (PPD) were analyzed over the course of the lists. The task difficulty predictability was manipulated by including lists of three, five, and seven sentences. The test was conducted over two sessions, one during which the participants were informed about list length before each list (predictable task difficulty) and one during which they were not (unpredictable task difficulty).Results:The sentence baseline dilation was higher when task difficulty was unpredictable compared to predictable, except at the start of the list, where there was no difference. The PPD tended to be higher at the beginning of the list, this pattern being more prominent when task difficulty was unpredictable. Recall performance was better and sentence baseline dilation was higher when noise reduction was on, especially toward the end of longer lists. There was no effect of noise reduction on PPD.Conclusions:Task difficulty predictability did not have an effect on resource allocation, since recall performance was similar independently of whether task difficulty was predictable or unpredictable. The higher sentence baseline dilation when task difficulty was unpredictable likely reflected a difference in the recall strategy or higher degree of task engagement/alertness or arousal. Hence, pupillometry captured processes which the SWIR test does not capture. Noise reduction frees up resources to be used for storage of speech, which was reflected in the better recall performance and larger sentence baseline dilation toward the end of the list when noise reduction was on. Thus, both measures captured different temporal aspects of the same processes related to resource allocation with noise reduction on and off.

Electrophysiological correlates of the flexible allocation of visual working memory resources

Visual working memory is a brief, capacity-limited store of visual information that is involved in a large number of cognitive functions. To guide one’s behavior effectively, one must efficiently allocate these limited memory resources across memory items. Previous research has suggested that items are either stored in memory or completely blocked from memory access. However, recent behavioral work proposes that memory resources can be flexibly split across items based on their level of task importance. Here, we investigated the electrophysiological correlates of flexible resource allocation by manipulating the distribution of resources amongst systematically lateralized memory items. We examined the contralateral delay activity (CDA), a waveform typically associated with the number of items held in memory. Across three experiments, we found that, in addition to memory load, the CDA flexibly tracks memory resource allocation. This allocation occurred as early as attentional selection, as indicated by the N2pc. Additionally, CDA amplitude was better-described when fit with a continuous model predicted by load and resources together than when fit with either alone. Our findings show that electrophysiological markers of attentional selection and memory maintenance not only track memory load, but also the proportion of memory resources those items receive.

Improving resource allocation in software-defined networks using clustering

Software-defined networks (SDNs) are a huge evolution in simplifying implementation and network operation which have reduced costs and made the network programmable. Although SDNs are a suitable option for solving some of the previous problems, but they have some challenges as any other new technology. Resource allocation and balance control in network is one of the main challenges of this technology which is studied in this paper. In this study, a new approach is proposed for improving memory resource allocation in network using load distribution clusters. Since in the proposed method, K-mean++ algorithm is used for clustering, load balancing of clusters can be used to preserve load balance of the network. In the proposed method, data with higher recall is transmitted to high-quality clusters in terms of average number of hubs and lower average delay between server and user. In the proposed method, by increasing number of clusters, higher memory is created in the network.

A Dynamic Memory Allocation Approach Based on Balloon Technology on Virtualization Platform

In virtualization platforms, Virtual Machines(VMs) have different memory demand when running different applications. Applications with high memory demand often result in guest swapping and performance degradation of the VM. Because of the semantic gap between the Virtual Machine Monitor(VMM) and the guest Operating System(OS), it is impossible to achieve reasonable allocation and use of memory resources between the guests. Our goal is to monitor the memory information in real time and achieve memory recovery and replenish of each VM in case of memory fluctuation. This method builds up a reasonable swapping mechanism, which can achieve automatic balance of memory allocation among multiple VMs, optimize guest swapping and improve memory utilization. We display it by visualization module. Based on ballooning technology, this paper designs a dynamic memory allocation method in KVM virtualization platform, which consists of memory monitoring, memory dynamic allocation, memory resource visualization and other modules. The results of many tests and analysis demonstrate that our method has the ability to basically achieve our expected goals and requirements.

Optimized Memory Allocation and Power Minimization for FPGA-Based Image Processing.

Memory is the biggest limiting factor to the widespread use of FPGAs for high-level image processing, which require complete frame(s) to be stored in situ. Since FPGAs have limited on-chip memory capabilities, efficient use of such resources is essential to meet performance, size and power constraints. In this paper, we investigate allocation of on-chip memory resources in order to minimize resource usage and power consumption, contributing to the realization of power-efficient high-level image processing fully contained on FPGAs. We propose methods for generating memory architectures, from both Hardware Description Languages and High Level Synthesis designs, which minimize memory usage and power consumption. Based on a formalization of on-chip memory configuration options and a power model, we demonstrate how our partitioning algorithms can outperform traditional strategies. Compared to commercial FPGA synthesis and High Level Synthesis tools, our results show that the proposed algorithms can result in up to 60% higher utilization efficiency, increasing the sizes and/or number of frames that can be accommodated, and reduce frame buffers’ dynamic power consumption by up to approximately 70%. In our experiments using Optical Flow and MeanShift Tracking, representative high-level algorithms, data show that partitioning algorithms can reduce total power by up to 25% and 30%, respectively, without impacting performance.

Optimally Removing Synchronization Overhead for CNNs in Three-Dimensional Neuromorphic Architecture

Recent three-dimensional (3-D) neuromorphic processing-in-memory (PIM) architecture provides a promising hardware-based solution to speed up the processing of convolutional neural networks. However, the limited capacity of the global buffer in this architecture is unable to efficiently handle synchronization overhead. In this paper, we jointly optimize the allocation of computation and memory resources on the 3-D-stacked PIM architecture. The objective is to minimize schedule length by removing synchronization overhead. To guarantee the generation of a feasible task schedule, we theoretically obtain the upper bound to reschedule each computation task. The target problem is further formulated as a dynamic programming model to get an optimal solution. We evaluate our technique with a variety of realistic neural network applications running on deep learning frameworks Caffe and TensorFlow. The results show that the proposed technique can achieve a significant reduction in processing time and improve the utilization of processing cores compared to previous studies.

Hi-DMM: High-Performance Dynamic Memory Management in High-Level Synthesis

High-level synthesis (HLS) of field programmable gate array (FPGA)-based accelerators has been proposed in order to simplify accelerator design process with respect to design time and complexity. However, modern HLS tools do not consider dynamic memory allocation constructs in high-level programming languages like C and limit themselves to static memory allocation. This paper proposes a dynamic memory allocation and management scheme, called Hi-DMM, for inclusion in commercial HLS design flows. Hi-DMM performs source-to-source transformation of user C code with dynamic memory constructs into C-source code with the dynamic memory allocator and management scheme developed in this paper. The transformed C-source code is amenable to synthesis by commercial tools like Vivado HLS. Relying on buddy tree-based allocation schemes and efficient hardware implementation of the allocators, Hi-DMM achieves 4x speed-up in both fine-grained and coarse-grained memory allocation compared to previous works. Experimental results obtained by including Hi-DMM with Vivado-HLS show that dynamic memory allocation of FPGA memory resources can be achieved at a much lower latency with minimal resource overhead, paving the way for synthesis of dynamic memory constructs in commercial HLS flows.

A time division multiplexing algorithm based on FPGA buffer shift to realize high speed image processing

With the development of medical image technology, two-dimensional imaging technology bottlenecks highlighted, three-dimensional imaging technology needs more and more urgent. Cone beam projection computer reorganization tomography equipment principle, which is also called “CBCT”, is the X-ray generator with a lower ray around the projection body to do a circular DR digital projection. Then the data obtained in the “intersection” after projection of the projection body has reconstructed in the computer to obtain the three-dimensional image. Image processing has still faced with image processing problems, such as processing speed and stability. In this paper, the key technology of high-speed image processing system based on FPGA is studied, and a high-speed and high-definition cone-shaped beam projection computerized tomo-graphic image processing platform based on FPGA is realized. The design of this paper is based on the image processing algorithm module of the pipeline. According to the structural characteristics of FPGA, a time division-multiplexing algorithm based on buffer shift is proposed to solve the problem of time division multiplexing for shared resources in image processing process, which facilitates the allocation of buffer memory resources. In this paper, the parallel CORDIC algorithm has introduced to solve the problem of the transcendental function in the image algorithm. The iterative speed advantage is obvious, which provides a powerful guarantee for the later image registration.

Mindfulness-based training with transcranial direct current stimulation modulates neuronal resource allocation in working memory: A randomized pilot study with a nonequivalent control group

Mindfulness-based training (MBT) and transcranial electrical stimulation (TES) methods such as direct current stimulation (tDCS) have demonstrated promise for the augmentation of cognitive abilities. The current study investigated the potential compatibility of concurrent “electrical” MBT and tDCS (or eMBT) by testing its combined effects on behavioral and neurophysiological indices of working memory (WM) and attentional resource allocation. Thirty-four healthy participants were randomly assigned to either a MBT task with tDCS group (eMBT) or an active control training task with sham tDCS (Control) group. Training lasted 4-weeks, with up to twenty MBT sessions and with up to eight of those sessions that were eMBT sessions. Electroencephalography was acquired during varying WM load conditions using the n-back task (1-, 2-, 3-back), along with performance on complex WM span tasks (operation and symmetry span) and fluid intelligence measures (Ravens and Shipley) before and after training. Improved performance was observed only on the 3-back and spatial span tasks for eMBT but not the Control group. During 3-back performance in the eMBT group, an increase in P3 amplitude and theta power at electrode site Pz was also observed, along with a simultaneous decrease in frontal midline P3 amplitude and theta power compared to the Control group. These results are consistent with the neural efficiency hypothesis, where higher cognitive capacity was associated with more distributed brain activity (i.e., increase in parietal and decrease in frontal amplitudes). Future longitudinal studies are called upon to further examine the direct contributions of tDCS on MBT by assessing the differential effects of electrode montage, polarity, current strength and a direct contrast between the eMBT and MBT conditions on performance and neuroimaging outcome data. While preliminary, the current results provided evidence for the potential compatibility of using eMBT to modulate WM capacity through the allocation of attention and its neurophysiological correlates.

Attention mediates the flexible allocation of visual working memory resources.

Though it is clear that it is impossible to store an unlimited amount of information in visual working memory (VWM), the limiting mechanisms remain elusive. While several models of VWM limitations exist, these typically characterize changes in performance as a function of the number of to-be-remembered items. Here, we examine whether changes in spatial attention could better account for VWM performance, independent of load. Across 2 experiments, performance was better predicted by the prioritization of memory items (i.e., attention) than by the number of items to be remembered (i.e., memory load). This relationship followed a power law, and held regardless of whether performance was assessed based on overall precision or any of 3 measures in a mixture model. Moreover, at large set sizes, even minimally attended items could receive a small proportion of resources, without any evidence for a discrete-capacity on the number of items that could be maintained in VWM. Finally, the observed data were best fit by a variable-precision model in which response error was related to the proportion of resources allocated to each item, consistent with a model of VWM in which performance is determined by the continuous allocation of attentional resources during encoding. (PsycINFO Database Record

A Load-Balanced Call Admission Controller for IMS Cloud Computing

Network functions virtualization provides opportunities to design, deploy, and manage networking services. It utilizes cloud computing virtualization services that run on high-volume servers, switches, and storage hardware to virtualize network functions. Virtualization techniques can be used in IP multimedia subsystem (IMS) cloud computing to develop different networking functions (e.g., load balancing and call admission control). IMS network signaling happens through session initiation protocol (SIP). An open issue is the control of overload that occurs when an SIP server lacks sufficient CPU and memory resources to process all messages. This paper proposes a virtual load balanced call admission controller (VLB-CAC) for the cloud-hosted SIP servers. VLB-CAC determines the optimal call admission rates and signaling paths for admitted calls along with the optimal allocation of CPU and memory resources of the SIP servers. This optimal solution is derived through a new linear programming model. This model requires some critical information of SIP servers as input. Further, VLB-CAC is equipped with an autoscaler to overcome resource limitations. The proposed scheme is implemented in smart applications on virtual infrastructure (SAVI) which serves as a virtual testbed. An assessment of the numerical and experimental results demonstrates the efficiency of the proposed work.

PD19-11 SURGEON PERFORMANCE AND DISTRACTIONS IN THE OPERATING ROOM: A RANDOMIZED, CONTROLLED, CROSSOVER TRIAL

You have accessJournal of UrologyTechnology & Instruments: Surgical Education & Skills Assessment III1 Apr 2015PD19-11 SURGEON PERFORMANCE AND DISTRACTIONS IN THE OPERATING ROOM: A RANDOMIZED, CONTROLLED, CROSSOVER TRIAL Ryan Speir, Timothy Brand, and Richard Greene Ryan SpeirRyan Speir More articles by this author , Timothy BrandTimothy Brand More articles by this author , and Richard GreeneRichard Greene More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.710AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Surgery is a complex interaction of cognitive and psychomotor skills that requires significant concentration to perform safely. The ability to maintain focus amidst the many distractions in the operating room (O.R.) is largely influenced by effective allocation of limited working memory resources. Distractions can overload working memory resources resulting in diminished performance. We hypothesize that O.R. distractions are significant and negatively impact surgeon performance. METHODS We conducted a prospective randomized controlled trial with ten resident and ten attending surgeons. Participants were required to meet proficiency benchmarks on the previously validated high fidelity virtual reality daVinci desktop simulator (dV Trainer, Mimic Technologies, Seattle WA) to minimize performance variability. Participants performed four simulated surgical tasks with increasing complexity on the dV Trainer. Participants performed each task four times in a cross-over design; twice with distraction (intervention) and twice without distraction (control). Distraction was provided by 85 decibels of background operating room noise and board-style medical questions. Surgical performance was then determined by total task time, economy of motion (EOM) and master work space range (MWSR). Data were analyzed using SPSS. RESULTS Distraction had a detrimental effect on surgeon performance. During the simpler tasks, pick and place jacks (PAPJ) and ring walk (RW), attending surgeons performance declined with distraction in EOM (175.41 vs 189.29, p=0.014) and MWSR (8.61 vs 9.51, p=0.02), respectively. Resident performance was not affected by distraction in the PAPJ and RW tasks for each metric, but on more difficult tasks like pegboard level 1, resident surgeons showed significant decline due to distraction on both EOM (447.1 vs 485.65, p=0.03) and MWSR (9.48 vs 10.59, p=0.007). On the most advanced task, pegboard level 3, neither group significantly declined however experts EOM (787.33 vs 757.13) and MWSR (13.15 vs 11.99) was superior. CONCLUSIONS We have shown that distractions during VR robotic task performance can adversely affect surgeon performance. Resident surgeons seem to be more affected by distraction as the complexity of task increases. With increasing complexity of tasks, attending surgeons were less affected by distraction. The results indicate the need to further examine the detrimental effect distractions have on surgical performance. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e396 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ryan Speir More articles by this author Timothy Brand More articles by this author Richard Greene More articles by this author Expand All Advertisement Advertisement PDF DownloadLoading ...

Suboptimal allocation of visual short term memory resources

Suboptimal allocation of visual short term memory resources

Audition and Cognition: Where Lab Meets Clinic

You have accessThe ASHA LeaderFeature1 Aug 2008Audition and Cognition: Where Lab Meets Clinic M. Kathleen Pichora-Fuller M. Kathleen Pichora-Fuller Google Scholar More articles by this author https://doi.org/10.1044/leader.FTR2.13102008.14 SectionsAbout ToolsAdd to favorites ShareFacebookTwitterLinked In As time-travelers turning the clock back about a half century, we might be surprised to discover that the importance of the link between auditory and cognitive processing had already been anticipated in the post-WWII era of hearing research and in the early days of audiology education and practice. A diagram in one of the earliest audiology textbooks, Hearing and Deafness, showed the physical, anatomical, physiological, and psychological aspects of speech communication, including connections between information processing by the peripheral and central auditory systems and information processing at higher cognitive levels, including language, memory, and attention (Davis, 1964, in Davis & Silverman, 1970). Even earlier, seminal research on auditory attention by Collin Cherry (1953) and his famous writings about the “cocktail party” effect foreshadowed the current interests of hearing-aid manufacturers for whom a rebirth of interest in cognition has been inspiring the design of new hearing aids (Edwards, 2007). The relationship between auditory and cognitive processing has been recognized as important for decades, but new technologies now enable us to understand it in a new way. Advances in cognitive neuroscience now provide answers to questions about those relationships, which earlier researchers did not have the tools to tackle. At the same time, advances in digital signal processing offer us rehabilitative options that were not possible with older technologies. These advances now compel audiologists to shift from a narrower focus on the ear and hearing to a broader consideration of the brain and listening (Pichora-Fuller, 2003; Pichora-Fuller & Singh, 2006). The Audition-Cognition Link Four main factors are motivating audiologists to explore the connection between audition and cognition (for discussions see Pichora-Fuller, 2006, 2007): The everyday challenges encountered by people living with hearing loss in the complex acoustic ecologies of the real world cannot be understood only in terms of hearing impairment (Kiessling et al., 2003) In multi-tasking situations, difficulty hearing may exacerbate or masquerade as reductions in cognitive performance, including problems with remembering and/or comprehending spoken language (Pichora-Fuller, Schneider, & Daneman, 1995; Schneider, Daneman, Murphy, & Kwong See, 2000) The ability of listeners, especially older adults, to use preserved cognitive abilities and supportive context to compensate for declines in the rapid processing of reduced sensory information offers hope for new rehabilitative interventions (e.g., Kricos, 2006; Pichora-Fuller, in press) Cognitive factors have recently been recognized to be important predictors of success with hearing aids, especially technologies with fast-acting, complex signal-processing (e.g., Edwards, 2008; Lunner, & Sundewall-Thorén, 2007) An additional reason to link auditory and cognitive processing as we shape new best practices is the mounting evidence that auditory processing disorders may predict and accelerate the onset of symptoms of cognitive disorders such as dementia (e.g., Gates, Beiser, Rees, Agostino, & Wolf, 2002; Golding, Taylor, Cupples, & Mitchell, 2006). The first four factors indicate why the link between cognition and audition should be important to audiologists working with adults of any age; the additional reason is especially important to audiologists working with older adults. Research Evidence In daily life listeners constantly take in new “bottom-up” information using their senses, and they combine these inputs with “top-down” knowledge they have learned and stored in various brain regions (Craik, 2007). Listeners use the auditory system to accomplish many everyday functions including hearing, listening, comprehending, and communicating (see Kiessling et al., 2003); however, these functions also require more general cognitive operations such as attention, memory, and language. Simply put, our ears enable us to hear passively, but our brains enable us to use actively what we have heard for specific purposes. As Davis and Silverman (1970) anticipated, despite the obvious importance of discovering how brain systems coordinate information when a person is engaged in complex behaviors such as communication, considerable research would be needed to develop the methods for studying the relationship between brain hardware and cognitive software. Fortunately, audiologists are now poised to apply exciting new knowledge about the interactions between auditory and cognitive processing that has been discovered using behavioral, physiological, and epidemiologic research approaches. Behavioral Research Because it is well-known that the prevalence of both auditory and cognitive deficits increases with age, it is not surprising that the behavioral research linking audition and cognition arose from research on adult aging. Both types of declines provide possible explanations for older adults’ frequent reports of difficulty understanding speech in noise (CHABA, 1988). Initially, hearing researchers tried to distinguish whether the speech understanding problems of older adults were due primarily to auditory (peripheral or central) or cognitive deficits (Humes, 2008). However, recognizing a strong correlation between sensory and cognitive aging, leading cognitive researchers by the mid-1990s called for new approaches to investigate various hypotheses regarding the reasons underlying the strong correlations between auditory and cognitive aging (Baltes & Lindenberger, 1997; Lindenberger & Baltes, 1994). One of the hypotheses was information degradation, which suggests that reduced quality of the sensory input will result in less efficient cognitive functioning. We have demonstrated that memory and comprehension suffer when the quality of the sound input is reduced by masking or by temporal distortion (Pichora-Fuller, 2007). In almost all studies this pattern of results is equivalent for younger and older adults (Schneider, Pichora-Fuller, & Daneman, in press). It seems that, regardless of age, a listener is more able to use the information that has been heard if the quality of the input is better. Thus, cognitive performance is optimal when listening is effortless and is reduced when listening is effortful. In challenging conditions requiring effortful listening, such as the presence of multiple competing talkers, cognitive resources are presumed to be diverted to listening. As listening effort is increased, fewer cognitive resources remain available for remembering and/or comprehending what was heard. In such conditions, listeners may be able to repeat what was heard, but they do not retain or understand it as well as they would in ideal listening conditions. The primary difference between younger and older listeners with relatively good audiograms (normal pure-tone thresholds below 4 kHz) is simply that older adults need a signal-to-noise ratio (SNR) that is about 3 dB more favorable than that required by younger adults to achieve the same score on a speech in noise test. Older adults’ need for a more favorable SNR is due to auditory aging, including age-related differences in auditory temporal processing that are not explained simply in terms of the audiogram (Pichora-Fuller & Souza, 2003; Pichora-Fuller & MacDonald, 2008). Because SNR conditions that are challenging for an older listener may not be challenging for a younger listener, older listeners may demonstrate the negative effect of effortful listening on cognitive performance in SNR conditions in which younger adults are unaffected. Despite differences among individuals or groups in the specific SNR at which listening becomes effortful, cognitive performance on measures such as memory and comprehension will be reduced for any listener when listening becomes effortful (Rönnberg, Rudner, Foo, & Lunner, in press). Apparent age-related differences in cognitive performance are largely eliminated when younger and older adults are tested in individualized SNR conditions that are equated so that each listener experiences the same degree of listening challenge (Schneider & Pichora-Fuller, 2000). Physiological Research In the present era of neuroscience, technological advances have enabled researchers to use tools such as functional magnetic resonance imaging (fMRI) to study patterns of brain activation. These patterns involve connections between different regions of the brain when humans perform complex behaviors, including perception, memory, attention, and language tasks (e.g., Goodglass & Wingfield, 1998). An appreciation of these patterns of activation involving multiple brain regions has superseded the simpler idea that our senses operate in a “bottom-up” modality-specific fashion to relay information from our ears to dedicated regions of auditory cortex (e.g., Scott, 2005; Zatorre, 2007). In cognitive neuroscience, two common strategies look for differences by comparing patterns of brain activation on two or more tasks or by comparing activation in two or more brain regions to determine the network of connections mediating a task (Horwitz, MacIntosh, Haxby, & Grady, 1995). The first strategy investigates the role of an anatomical site in a variety of tasks; the second strategy investigates how different anatomical sites combine when a task is performed. Building on these two strategies, “cross-function” and “within-function” approaches have been described in which brain organization is studied to understand how the neural correlates of different cognitive functions overlap and interact (Cabeza & Nyberg, 2002). The goal of the cross-function approach is to determine all of the functions (or tasks) that involve a given brain region; the goal of the within-function approach is to determine all of the brain regions that participate in a given function (or task). These two approaches have been applied to research on adult aging; the research is expected to help explain complex auditory behaviors that rely on a combination of auditory and cognitive processes. One brain-imaging study showed that the pattern of brain activation shifts when a driver (in a driving simulator) must also listen to speech. This finding is consistent with the idea that cognitive resources are shared among tasks in demanding multi-tasking situations, and that listening consumes cognitive resources that would otherwise be allocated to other tasks (Just, Keller, & Cnyker, 2008). It has been suggested that more widespread brain activation is an indication of “thinking harder” and that it may reflect the allocation of more working memory resources during language processing (e.g., Just, Carpenter, Keller, Eddy, & Thulborn, 1996). In another brain-imaging study in which young adults with normal hearing listened to sentences in degraded conditions, brain activation was more widespread in more adverse listening conditions (Obleser, Wise, Dresner, & Scott, 2007). Specifically, when sentence context was available, the investigators observed increased activation in the left dorsolateral prefrontal cortical areas that are thought to be involved in semantic processing and working memory (e.g., Petrides, Alivisatos, Meyer, & Evans, 1993). It seems that when listeners engage in more “top-down,” context-driven processing of auditory information by involving greater activation of prefrontal cortex, perceptual learning of degraded speech by young adults with normal hearing is accelerated (Davis, Johnsrude, Hervais-Adelman, Taylor, & McGettigan, 2005). Interestingly, when younger and older adults perform equivalently on various perceptual and cognitive tasks, there is more widespread activation in older brains than in younger brains, with one interpretation being that this difference reflects compensatory processing (e.g., Cabeza, 2002; Cabeza, Anderson, Locantore, & McIntosh, 2002). Such compensatory brain activation would be consistent with the finding that older adults are better than younger adults at using context to compensate for difficulty hearing in challenging listening conditions (Pichora-Fuller & Singh, 2006; Pichora-Fuller, in press). Individual differences in the ability to use context to support the understanding and learning of a degraded or novel signal may be critical to explaining why individuals with higher cognitive function do better with fast-acting, complex-signal-processing hearing aids. Epidemiological Research Epidemiological research has uncovered links between age-related changes in audition and cognition that raise serious issues for audiologists to consider as they formulate best practices incorporating cognitive factors. The prevalence of both hearing impairment and cognitive impairment increases with age. Hearing loss is associated with cognitive function, even when the participants are deemed to be clinically normal on cognitive screening tests (Golding et al., 2006). In addition, as many as 90% of people with dementia have hearing loss (Gold, Lightfoot, & Hnath-Chisolm, 1996), and as many as a third of older adults with dementia living in residential care were recategorized as having less severe dementia when they were re-tested with amplification (Weinstein & Amsel, 1986). There is an obvious need to take sensory function into account when assessing cognitive function (Uhlmann et al., 1989b). Hearing loss may even contribute to or accelerate clinically significant cognitive decline, and it has been suggested that sensory intervention can reduce problem behaviors in dementia (Palmer, Adams, Bourgeois, Durrant, & Ross, 1999), or even slow cognitive decline (Peters, Potter, & Scholer, 1988; Wahl & Heyl, 2003). In one study the relationship between hearing loss and cognitive decline was examined in two groups of 100 people—one group with and the other without Alzheimer’s-type dementia—matched with respect to age, sex, and education (Uhlmann et al., 1989a). The investigators found that cognitive function was significantly correlated with hearing loss in both groups, but more so for the group with dementia, suggesting that hearing loss is a risk factor for dementia. In another large-scale, multi-center longitudinal study of older women who were participating in research on osteoporotic fractures, combined vision and hearing loss was associated with the greatest odds for cognitive decline and for functional decline on five everyday activities over a period of four years (Lin et al., 2004). In a smaller longitudinal study, individuals with Alzheimer’s who had hearing loss experienced a more rapid cognitive decline than individuals with Alzheimer’s who had relatively good hearing (Peters et al., 1988). Research has also shown a reduced rate of decline in scores on a cognitive screening test over a six-month period following intervention with hearing aids (Allen et al., 2003), suggesting that audiologic rehabilitation may slow the progression of dementia. Most studies concerning the association between age-related hearing impairment and cognitive impairment have used pure-tone thresholds to measure degree of hearing loss and few have included measures of central auditory processing. In one study, people with probable Alzheimer’s disease performed worse on central auditory processing tests, even though they were matched to the control group with respect to age, gender, and pure-tone average (Strouse, Hall, & Burger, 1995). When speech tests were employed to measure central auditory dysfunction, the results were predictive of the likelihood of developing Alzheimer’s disease, even after the contribution of audiometric sensitivity had been taken into account (Gates et al., 2002). The epidemiologic research suggests a bidirectional link between auditory and cognitive impairment. Therefore, health professionals assessing cognitive impairment need to know the hearing status of the individuals being assessed; conversely, audiologists need to be aware of individuals’ cognitive status when conducting assessments or rehabilitation. Cognitive Measures Audiologists will need to find effective ways to participate in interprofessional geriatric teams for older adults who have or are at risk for clinically significant cognitive impairment, so that information about auditory and cognitive function can be shared. For healthy adults, regardless of age, individual differences in cognitive function have emerged as a factor that may help audiologists to understand the candidacy for and/or the outcomes of interventions that aim to improve ease of listening in challenging situations. Two daunting questions remain: What kind of cognitive test would be the best suited for use by audiologists? How would audiologists use cognitive measures? Much work remains to be done to develop a test of cognitive function that could be used in audiologic practice. However, a working-memory test seems likely to be the most promising for audiologists to use to measure cognition (Pichora-Fuller, 2007). Over the last three decades, the role of working memory in language comprehension has been investigated extensively, so it is reasonable for audiologists to use this existing knowledge and apply it to test development. Indeed, preliminary studies examining alternative tests of cognition have favored working memory over other cognitive measures (Vaughan, Storzbach, & Furukawa, 2006; Foo, Rudner, Rönnberg, & Lunner, 2007). Any test developed will need to be clinically feasible and acceptable to both audiologists and their clients. Adapting existing tests of speech understanding to incorporate working memory may overcome some of the obstacles related to the feasibility and acceptability of the test. Ultimately, cognitive tests might be used to differentiate individuals with high or low cognitive function and to select interventions taking cognition into account. For example, a person who performs well on a cognitive test might be fitted with a hearing aid that uses fast-acting, complex signal processing; another person who does not perform as well might be fitted with a slower-acting or simpler device or be given training to facilitate acclimatization to a more complex hearing aid. Another possible application of cognitive testing in audiology would be to evaluate whether easier listening is the result of hearing aid fitting or other types of rehabilitation. For example, for many individuals it is not possible to demonstrate significant differences in benefit between hearing aids using measures such as the scores on standard word discrimination tests. It may be possible, however, to demonstrate differences due to particular hearing aid fittings by measuring how much of what the individual heard was remembered (Edwards, 2007). With recent advances in neuroscience and signal processing technology, we are now poised to finally tackle the issue of how best to incorporate cognitive measures into audiologic practice. Acknowledgements: This research has been supported by the Canadian Institutes for Health Research and the Natural Sciences and Engineering Research Council of Canada. References Allen N. H., Burns A., Newton V., Hickson F., Ramsden R., Rogers J. et al. (2003). The effects of improving hearing in dementia.Age Ageing, 32, 189–193. CrossrefGoogle Scholar Baltes P. B., & Lindenberger U. (1997). Emergence of a powerful connection between sensory and cognitive functions across the adult life span: A new window to the study of cognitive aging?.Psychology and Aging, 12, 12–21. CrossrefGoogle Scholar Cabeza R. (2002). Hemispheric asymmetry reduction in older adults: The HAROLD model.Psychology and Aging, 17, 85–100. CrossrefGoogle Scholar Cabeza R., Anderson N., Locantore J. K., & McIntosh A. R. (2002). Aging gracefully: Compensatory brain activity in high-performing older adults.NeuroImage, 17, 1394–1402. CrossrefGoogle Scholar Cabeza R., & Nyberg L. (2002). Seeing the forest through the trees: The cross-function approach to imaging cognition.In Zani A., & Proverbio A. M. (Eds), The cognitive electrophysiology of mind and brain. San Diego: Academic Press, pp. 41–68. Google Scholar CHABA (Working Group on Speech Understanding and Aging of the Committee on Hearing, Bioacoustics, and Biomechanics, Commission on Behavioral and Social Sciences and Education, National Research Council, Washington). (1988). Speech understanding and aging.Journal of the Acoustical Society of America, 83, 859–895. CrossrefGoogle Scholar Cherry E. C. (1953). Some experiments on the recognition of speech, with one and two ears.Journal of the Acoustical Society of America, 25, 975–979. CrossrefGoogle Scholar Craik F. I. M. (2007). Commentary: The role of cognition in age-related hearing loss.Journal of the American Academy of Audiology, 18, 539–547. CrossrefGoogle Scholar Davis H.1964. International Audiology, 3, 209–215 (in H. Davis H, & R. Silverman (1970). Hearing and deafness, 3rd ed. New York: Holt, Rinehart, Winston, 76.) CrossrefGoogle Scholar Davis H., & Silverman R., Eds. (1970). Hearing and deafness 3rd ed. New York: Holt, Rinehart, Winston. Google Scholar Davis M. H., Johnsrude I. S., Hervais-Adelman A., Taylor K., & McGettigan C. (2005). Lexical information drives perceptual learning of distorted speech: Evidence from the comprehension of noise-vocoded sentences.Journal of Experimental Psychology, 134, 222–241. CrossrefGoogle Scholar Edwards B. (2007). The future of hearing aid technology.Trends in Amplification, 11, 31–45. CrossrefGoogle Scholar Edwards B. (2008). The effect of hearing loss and hearing aids on cognition. American Academy of Audiology Convention, Charlotte, NC, April 2–4. Google Scholar Foo C., Rudner M., Rönnberg J., & Lunner T. (2007). Recognition of speech in noise with new hearing instrument compression release settings requires explicit cognitive storage and processing capacity.Journal of the American Academy of Audiology, 18, 618–631. CrossrefGoogle Scholar Gates G. A., Beiser A., Rees T. S., Agostino R. B., & Wolf P. A. (2002). Central auditory dysfunction may precede the onset of clinical dementia in people with probably Alzheimer’s disease.Journal of the American Geriatric Society, 50, 482–488. CrossrefGoogle Scholar Gold M., Lightfoot L. A., & Hnath-Chisolm T. (1996). Hearing loss in a memory disorders clinic: A specially vulnerable population.Archives of Neurology, 53, 922–928. CrossrefGoogle Scholar Golding M., Taylor A., Cupples L., & Mitchell P. (2006). Odds of demonstrating auditory processing abnormality in the average older adult: The Blue Mountains hearing study.Ear and Hearing, 27, 129–138. CrossrefGoogle Scholar Goodglass H., & Wingfield A. (1998). The changing relationship between anatomic and cognitive explanation in the neuropsychology of language.Journal of Psycholinguistic Research, 27(2), 147–165. CrossrefGoogle Scholar Horwitz B., McIntosh A. R., Haxby J. V., & Grady C. L. (1995). Network analysis of brain cognitive function using metabolic and blood flow data.Behavioural Brain Research, 66(1–2), 187–193. CrossrefGoogle Scholar Humes L. E. (2008, April 15). Aging and speech communication.The ASHA Leader, 13(5), 10–13, 33. LinkGoogle Scholar Just M. A., Carpenter P. A., Keller T. A., Eddy W. F., & Thulborn K. R. (1996). Brain activation modulated by sentence comprehension.Science, 274(5284), 114–116. CrossrefGoogle Scholar Just M. A., Keller T. A., & Cynkar J. (2008). A decrease in brain activation associated with driving when listening to someone speak.Brain Research, 1205, 70–80. CrossrefGoogle Scholar Kiessling J., Pichora-Fuller M. K., Gatehouse S., Stephens D., Arlinger S., Chisolm T., Davis A.C., Erber N. P., Hickson L., Holmes A., Rosenhall U., & von Wedel H. (2003). Candidature for and delivery of audiological services: Special needs of older people.International Journal of Audiology, 42(Supp 2), S92–S101. CrossrefGoogle Scholar Kricos P. B. (2006). Audiologic management of older adults with hearing loss and compromised cognitive/psychoacoustic auditory processing capabilities.Trends in Amplification, 10, 1–28. CrossrefGoogle Scholar Lin M. Y., Gutierrez P. R., Stone K. L., Yaffe K., Ensrud K. E., Fink H.A., et al. (2004). Vision impairment and combined vision and hearing impairment predict cognitive and functional decline in older women.Journal of the American Geriatric Society, 52, 1996–2002. CrossrefGoogle Scholar Lindenberger U. & Baltes P. B. (1994). Sensory functioning and intelligence in old age: A sensory connection.Psychology and Aging, 9, 339–355. CrossrefGoogle Scholar Lunner T., & Sundewall-Thorén E. (2007). Interactions between cognition, compression, and listening conditions: Effects on speech-in-noise performance in a two-channel hearing aid.Journal of the American Academy of Audiology, 18, 604–617. CrossrefGoogle Scholar Obleser J., Wise R. I. S., Dresner M. A., & Scott S. K. (2007). Functional integration across brain regions improves speech perception under adverse listening conditions.Journal of Neuroscience, 27, 2283–289. CrossrefGoogle Scholar Palmer C. V., Adams S. W., Bourgeois M., Durrant J., & Ross M. (1999). Reduction in caregiver-identified problem behaviors in patients with Alzheimer disease post-hearing-aid fitting.Journal of Speech Language Hearing Research, 42, 312–328. LinkGoogle Scholar Peters C. A., Potter J. F., & Scholer S. G. (1988). Hearing impairment as a predictor of cognitive decline in dementia.Journal of the American Geriatric Society, 36, 981–986. CrossrefGoogle Scholar Petrides M., Alivisatos B., Meyer E., & Evans A. C. (1993). Functional activation of the human frontal cortex during the performance of verbal working memory tasks.Proceedings of the National Academy of Science of the United States of America, 90, 878–882. CrossrefGoogle Scholar Pichora-Fuller M. K. (2007). Audition and cognition: What audiologists need to know about listening.In Palmer C., & Seewald R. (Eds.). Hearing care for adults. Phonak: Stäfa, Switzerland, pp. 71–85. Google Scholar Pichora-Fuller M. K. (2003). Cognitive aging and auditory information processing.International Journal of Audiology, 42 (Supp 2), S26–S32. CrossrefGoogle Scholar Pichora-Fuller M. K. (2006). Perceptual effort and apparent cognitive decline: Implications for audiologic rehabilitation.Seminars in Hearing, 27, 284–293. CrossrefGoogle Scholar Pichora-Fuller M. K. (in press). Use of supportive context by younger and older adult listeners: Balancing bottom-up and top-down information processing.International Journal of Audiology. Google Scholar Pichora-Fuller M. K., & MacDonald E. (2008). Auditory temporal processing deficits in older listeners: A review and overview.In Dau T., Buchholz J., Harte J., & Christiansen T. (Eds), Auditory signal processing in hearing-impaired listeners: Proceedings of the First International Symposium on Audiological and Auditory Research (ISAAR 2007), Centertryk A/S: Denmark, pp. 297–306. Google Scholar Pichora-Fuller M. K., Schneider B. A., & Daneman M. (1995). How young and old adults listen to and remember speech in noise.Journal of the Acoustical Society of America, 97(1), 593–608. CrossrefGoogle Scholar Pichora-Fuller M. K., & Singh G. (2006). Effects of age on auditory and cognitive processing: Implications for hearing aid fitting and audiological rehabilitation.Trends in Amplification, 10, 29–59. CrossrefGoogle Scholar Pichora-Fuller M. K., & Souza P. E. (2003). Effects of aging on auditory processing of speech.International Journal of Audiology, 42(Suppl 2), 2S11–6. Google Scholar Rönnberg J., Rudner M., Foo C., & Lunner T. (in press). Cognition counts: A working memory system for ease of language understanding (ELU).International Journal of Audiology. Google Scholar Schneider B.A., Pichora-Fuller M.K., & Daneman M. (in press). The effects of senescent changes in audition and cognition on spoken language comprehension.In Gordon-Salant S., Frisina R. D., Popper A., & Fay D. (Eds), The aging auditory system: Perceptual characterization and neural bases of presbycusis, Springer Handbook of Auditory Research. Springer: Berlin. Google Scholar Schneider B. A., Daneman M., Murphy D. R., & Kwong See S. (2000). Listening to discourse in distracting settings: The effects of aging.Psychology and Aging, 15, 110–125. CrossrefGoogle Scholar Schneider B. A, & Pichora-Fuller M. K. (2000). Implications of perceptual deterioration for cognitive aging research.In Craik F. I. M. & Salthouse T. A. (Eds), The handbook of aging and cognition (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates, pp. 155–219. Google Scholar Scott S. (2005). Auditory processing – speech, space and auditory objects.Current Opinion in Neurobiology, 15, 197–201. CrossrefGoogle Scholar Strouse A. L., Hall J. W., & Burger M. C. (1995). Central auditory processing in Alzheimber’s disease.Ear and Hearing, 16, 230–238. CrossrefGoogle Scholar Uhlmann R. F., Larson E. B., Rees T. S., Koepsell T. D., & Duckert L. G. (1989a). Relationship of hearing impairment to dementia and cognitive dysfunction in older adults.Journal of the American Medical Association, 261, 1916–1919. CrossrefGoogle Scholar Uhlmann R. F., Teri L., Rees T. S., Mozlowski K. J., & Larson E. B. (1989). Impact of mild to moderate hearing loss on mental status testing.Journal of the American Geriatric Society, 37, 223–228. CrossrefGoogle Scholar Vaughan N., Storzbach D., & Furukawa I. (2006). Sequencing versus nonsequencing working memory in understanding of rapid speech by older listeners.Journal of the American Academy of Audiology, 17, 506–518. CrossrefGoogle Scholar Wahl H.-W., & Heyl V. (2003). Connections between vision, hearing, and cognitive function in old age.Generations, 27, 39–45. Google Scholar Weinstein B., & Amsel L. (1986). Hearing loss and senile dementia in the institutionalized elderly.Clinical Gerontologist, 4, 3–15. CrossrefGoogle Scholar Zatorre R. J. (2007). There’s more to auditory cortex than meets the ear.Hearing Research, 229, 24–30. CrossrefGoogle Scholar Author Notes M. Kathleen Pichora-Fuller, is a professor in the Department of Psychology, University of Toronto, and an adjunct scientist at Toronto Rehabilitation Institute. Her research combines clinical experience in rehabilitative audiology with experimental psychology. Contact her at [email protected]. Advertising Disclaimer | Advertise With Us Advertising Disclaimer | Advertise With Us Additional Resources FiguresSourcesRelatedDetails Volume 13Issue 10August 2008 Get Permissions Add to your Mendeley library History Published in print: Aug 1, 2008 Metrics Downloaded 451 times Topicsasha-topicsleader_do_tagasha-article-typesCopyright & Permissions© 2008 American Speech-Language-Hearing AssociationLoading ...