Allocation Of Memory Resources Research Articles

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 ...

Fuzzy-logic optical optimization of mainframe CPU and memory

The allocation of CPU time and memory resources is a familiar problem in organizations with a large number of users and a single mainframe. Usually the amount of resources allocated to a single user is based on the user's own statistics not on the statistics of the entire organization, therefore patterns are not well identified and the allocation system is prodigal. A fuzzy-logic-based algorithm to optimize the CPU and memory distribution among users based on their history is suggested. The algorithm works on heavy and light users separately since they present different patterns to be observed. The result is a set of rules generated by the fuzzy-logic inference engine that will allow the system to use its computing ability in an optimized manner. Test results on data taken from the Faculty of Engineering of Tel Aviv University demonstrate the capabilities of the new algorithm.

Age-Related Changes in Event-Cued Visual and Auditory Prospective Memory Proper

ABSTRACT We rely upon prospective memory proper (ProMP) to bring back to awareness previously formed plans and intentions at the right place and time, and to enable us to act upon those plans and intentions. To examine age-related changes in ProMP, younger and older participants made decisions about simple stimuli (ongoing task) and at the same time were required to respond to a ProM cue, either a picture (visually cued ProM test) or a sound (auditorily cued ProM test), embedded in a simultaneously presented series of similar stimuli (either pictures or sounds). The cue display size or loudness increased across trials until a response was made. The cue size and cue loudness at the time of response indexed ProMP. The main results showed that both visual and auditory ProMP declined with age, and that such declines were mediated by age declines in sensory functions (visual acuity and hearing level), processing resources, working memory, intelligence, and ongoing task resource allocation.

Working Memory, Motivation, and Teacher-Initiated Learning

Working memory is where we “think” as we learn. A notion that emerges as a synthesis from several threads in the research literatures of cognition, motivation, and connectionism is that motivation in learning is the process whereby working memory resource allocation is instigated and sustained. This paper reviews much literature on motivation and working memory, and concludes that the apparent novelty of the proposal offered to describe motivation in terms of working memory results from the apparent lack of cross-channel exchange among these research traditions. The relation between working memory and motivation is explored in the context of the interactive compensatory model of learning (ICML) in which learning is considered to result from the interaction of ability, motivation, and prior learning. The ICML is recast in light of the revised definition of motivation offered here. This paper goes on to suggest ways in which a range of teaching and learning issues and activities may be reconceptualized in the context of a model emphasizing a learner's working memory that makes use of chunks of previously acquired knowledge.

Stream combination

Cycle-based IO schedulers use statically configured time-cycle durations. As a result, they are unable to avoid the formation of virtual bottlenecks. We term a bottleneck as virtual when it occurs within a single resource subsystem, and it is possible to use a secondary under-utilized resource to thwart the bottleneck. The primary reason for virtual bottlenecks in streaming servers is static allocation of memory and disk-bandwidth resources using fixed time-cycle durations. As a result, shifting request workload can cause a virtual bottleneck either in the memory or disk subsystem. We present stream combination, an adaptive IO scheduling technique that addresses this problem in a comprehensive fashion. Stream combination predicts the formation of virtual bottlenecks and proactively alters the IO schedule to avoid them. A simulation study suggests significant performance gains compared to the current state-of-the-art fixed time-cycle IO scheduler.

CAMERA

CAMERA, Cache and Memory Resource Allocation , is a collection of workbenches for cache mapping schemes (including direct, fully associative, and set associative) and virtual memory (including paging and TLBs). Its goals are to provide users with interactive tutorials and simulations to help them better understand the fundamental concepts of memory management. Implemented in Java Swing, these workbenches allow users to observe the processes of memory to cache mapping, and virtual memory using paging. The workbenches have proven useful in both introductory architecture classes and operating system classes. Students find CAMERA easy to use, even without the supplied user's guide. In the paper, we compare CAMERA to various existing systems and explore the software's features in detail in addition to discussing future enhancements.

A decoupled predictor-directed stream prefetching architecture

An effective method for reducing the effect of load latency in modern processors is data prefetching. One form of hardware-based data prefetching, stream buffers, has been shown to be particularly effective due to its ability to detect data streams and run ahead of them, prefetching as it goes. Unfortunately, in the past, the applicability of streaming was limited to stride intensive code. In this paper, we propose predictor-directed stream buffers (PSB), which allows the stream buffer to follow a general address prediction stream instead of a fixed stride. A general address prediction stream complicates the allocation of both stream buffer and memory resources because the predictions generated will not be as reliable as prior sequential next-line and stride-based stream buffer implementations. To address this, we examine using confidence-based techniques to guide the allocation and prioritization of stream buffers and their prefetch requests. Our results show, when using PSB on a benchmark suite heavy in pointer-based applications, that PSB provides a 23 percent speedup on average over the best previous stream buffer implementation, and an improvement of 75 percent over using no prefetching at all.

The allocation of memory resources during sentence comprehension: evidence from the elderly.

Two experiments were carried out to examine the ability of elderly subjects to establish syntactically governed dependency relations during the course of sentence comprehension. The findings reveal the manner in which memory constraints operate during syntactic processing.

On Static Memory Allocation in Computer Systems

An algorithm for allocation of memory resources is presented making use of a priori information about a computer program. This static allocation algorithm is based on a directed graph model of computations with particular attention to the memory allocation of programs assigned for execution on multiprocessor systems.