2,396 research outputs found

    Verbal Learning and Memory After Cochlear Implantation in Postlingually Deaf Adults: Some New Findings with the CVLT-II

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    OBJECTIVES: Despite the importance of verbal learning and memory in speech and language processing, this domain of cognitive functioning has been virtually ignored in clinical studies of hearing loss and cochlear implants in both adults and children. In this article, we report the results of two studies that used a newly developed visually based version of the California Verbal Learning Test-Second Edition (CVLT-II), a well-known normed neuropsychological measure of verbal learning and memory. DESIGN: The first study established the validity and feasibility of a computer-controlled visual version of the CVLT-II, which eliminates the effects of audibility of spoken stimuli, in groups of young normal-hearing and older normal-hearing (ONH) adults. A second study was then carried out using the visual CVLT-II format with a group of older postlingually deaf experienced cochlear implant (ECI) users (N = 25) and a group of ONH controls (N = 25) who were matched to ECI users for age, socioeconomic status, and nonverbal IQ. In addition to the visual CVLT-II, subjects provided data on demographics, hearing history, nonverbal IQ, reading fluency, vocabulary, and short-term memory span for visually presented digits. ECI participants were also tested for speech recognition in quiet. RESULTS: The ECI and ONH groups did not differ on most measures of verbal learning and memory obtained with the visual CVLT-II, but deficits were identified in ECI participants that were related to recency recall, the buildup of proactive interference, and retrieval-induced forgetting. Within the ECI group, nonverbal fluid IQ, reading fluency, and resistance to the buildup of proactive interference from the CVLT-II consistently predicted better speech recognition outcomes. CONCLUSIONS: Results from this study suggest that several underlying foundational neurocognitive abilities are related to core speech perception outcomes after implantation in older adults. Implications of these findings for explaining individual differences and variability and predicting speech recognition outcomes after implantation are discussed

    Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction

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    British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225

    Catastrophe Models for Cognitive Workload and Fatigue

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    We reconceptualised several problems concerning the measurement of cognitive workload – fixed versus variable limits on channel capacity, work volume versus time pressure, adaptive strategies, resources demanded by tasks when performed simultaneously, and unclear distinctions between workload and fatigue effects – as two cusp catastrophe models: buckling stress resulting from acute workload, and fatigue resulting from extended engagement. Experimental participants completed a task that was intensive on non-verbal episodic memory and had an automatically speeded component. For buckling stress, the epoch of maximum (speeded) performance was the asymmetry parameter; however, anxiety did not contribute to bifurcation as expected. For fatigue, the bifurcation factor was the total work accomplished, and arithmetic, a compensatory ability, was the asymmetry parameter; R2 for the cusp models outperformed the linear comparison models in both cases. A research programme is outlined that revolves around the two models with different types of task and resource configurations

    A Neural Model of How the Brain Represents and Compares Multi-Digit Numbers: Spatial and Categorical Processes

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    Both animals and humans are capable of representing and comparing numerical quantities, but only humans seem to have evolved multi-digit place-value number systems. This article develops a neural model, called the Spatial Number Network, or SpaN model, which predicts how these shared numerical capabilities are computed using a spatial representation of number quantities in the Where cortical processing stream, notably the Inferior Parietal Cortex. Multi-digit numerical representations that obey a place-value principle are proposed to arise through learned interactions between categorical language representations in the What cortical processing stream and the Where spatial representation. It is proposed that learned semantic categories that symbolize separate digits, as well as place markers like "tens," "hundreds," "thousands," etc., are associated through learning with the corresponding spatial locations of the Where representation, leading to a place-value number system as an emergent property of What-Where information fusion. The model quantitatively simulates error rates in quantification and numerical comparison tasks, and reaction times for number priming and numerical assessment and comparison tasks. In the Where cortical process, it is proposed that transient responses to inputs are integrated before they activate an ordered spatial map that selectively responds to the number of events in a sequence. Neural mechanisms are defined which give rise to an ordered spatial numerical map ordering and Weber law characteristics as emergent properties. The dynamics of numerical comparison are encoded in activity pattern changes within this spatial map. Such changes cause a "directional comparison wave" whose properties mimic data about numerical comparison. These model mechanisms are variants of neural mechanisms that have elsewhere been used to explain data about motion perception, attention shifts, and target tracking. Thus, the present model suggests how numerical representations may have emerged as specializations of more primitive mechanisms in the cortical Where processing stream. The model's What-Where interactions can explain human psychophysical data, such as error rates and reaction times, about multi-digit (base 10) numerical stimuli, and describe how such a competence can develop through learning. The SpaN model and its explanatory range arc compared with other models of numerical representation.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333

    Submaximal Isometric Force Steadiness in People with Multiple Sclerosis Under Single and Dual Task Conditions

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    Activities of daily living require steady, non-fatiguing, isometric muscular contractions to maintain postural control and stabilize body segments to facilitate interaction with the environment. Furthermore, typical activities often require simultaneous performance of cognitive and motor tasks. This may challenge people with multiple sclerosis, a chronic neurodegenerative disease of the central nervous system associated with motor and cognitive impairments. Despite functional relevance, isometric force steadiness in both the upper and lower extremities has not been explored in this population. Additionally, dual task experiments in multiple sclerosis have primarily used gait, a dynamic activity, as the motor task. Thus, the purpose of this dissertation was to examine isometric force steadiness performed under single and dual task conditions in people with multiple sclerosis. It was hypothesized that people with multiple sclerosis would be less steady and have greater dual task costs of cognitive-motor tasks.Study one measured steadiness of the ankle dorsiflexors and elbow flexors across a range of low to moderate force targets during a single task condition. Absolute force fluctuation at each target was measured and relative fluctuation was calculated using the coefficient of variation. In the elbow flexors, people with multiple sclerosis were less steady than controls only at very low forces and were less steady at nearly all force targets in the ankle. However, magnitudes of upper and lower extremity force fluctuation did not correlate within either sample.Study two determined dual task effects of simultaneous performance of a steady ankle dorsiflexion contraction and a cognitive task involving working memory and processing speed. Both controls and people with multiple sclerosis experienced negative dual task effects on motor and cognitive performances. Although those with multiple sclerosis did not perform as well as controls for all tasks, there was no difference in motor effects.This dissertation shows that 1.) isometric steadiness is impaired in the upper and lower extremities of people with multiple sclerosis at very low forces under single task conditions, 2.) people with multiple sclerosis experience cognitive-motor interference when dual tasking, and 3.) the relative dual task motor effects are nonetheless comparable to what is experienced by healthy controls

    Multiplierless CSD techniques for high performance FPGA implementation of digital filters.

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    I leverage FastCSD to develop a new, high performance iterative multiplierless structure based on a novel real-time CSD recoding, so that more zero partial products are introduced. Up to 66.7% zero partial products occur compared to 50% in the traditional modified Booth's recoding. Also, this structure reduces the non-zero partial products to a minimum. As a result, the number of arithmetic operations in the carry-save structure is reduced. Thus, an overall speed-up, as well as low-power consumption can be achieved. Furthermore, because the proposed structure involves real time CSD recoding and does not require a fixed value for the multiplier input to be known a priori, the proposed multiplier can be applied to implement digital filters with non-fixed filter coefficients, such as adaptive filters.My work is based on a dramatic new technique for converting between 2's complement and CSD number systems, and results in high-performance structures that are particularly effective for implementing adaptive systems in reconfigurable logic.My research focus is on two key ideas for improving DSP performance: (1) Develop new high performance, efficient shift-add techniques ("multiplierless") to implement the multiply-add operations without the need for a traditional multiplier structure. (2) There is a growing trend toward design prototyping and even production in FPGAs as opposed to dedicated DSP processors or ASICs; leverage this trend synergistically with the new multiplierless structures to improve performance.Implementation of digital signal processing (DSP) algorithms in hardware, such as field programmable gate arrays (FPGAs), requires a large number of multipliers. Fast, low area multiply-adds have become critical in modern commercial and military DSP applications. In many contemporary real-time DSP and multimedia applications, system performance is severely impacted by the limitations of currently available speed, energy efficiency, and area requirement of an onboard silicon multiplier.I also introduce a new multi-input Canonical Signed Digit (CSD) multiplier unit, which requires fewer shift/add/subtract operations and reduced CSD number conversion overhead compared to existing techniques. This results in reduced power consumption and area requirements in the hardware implementation of DSP algorithms. Furthermore, because all the products are produced simultaneously, the multiplication speed and thus the throughput are improved. The multi-input multiplier unit is applied to implement digital filters with non-fixed filter coefficients, such as adaptive filters. The implementation cost of these digital filters can be further reduced by limiting the wordlength of the input signal with little or no sacrifice to the filter performance, which is confirmed by my simulation results. The proposed multiplier unit can also be applied to other DSP algorithms, such as digital filter banks or matrix and vector multiplications.Finally, the tradeoff between filter order and coefficient length in the design and implementation of high-performance filters in Field Programmable Gate Arrays (FPGAs) is discussed. Non-minimum order FIR filters are designed for implementation using Canonical Signed Digit (CSD) multiplierless implementation techniques. By increasing the filter order, the length of the coefficients can be decreased without reducing the filter performance. Thus, an overall hardware savings can be achieved.Adaptive system implementations require real-time conversion of coefficients to Canonical Signed Digit (CSD) or similar representations to benefit from multiplierless techniques for implementing filters. Multiplierless approaches are used to reduce the hardware and increase the throughput. This dissertation introduces the first non-iterative hardware algorithm to convert 2's complement numbers to their CSD representations (FastCSD) using a fixed number of shift and logic operations. As a result, the power consumption and area requirements required for hardware implementation of DSP algorithms in which the coefficients are not known a priori can be greatly reduced. Because all CSD digits are produced simultaneously, the conversion speed and thus the throughput are improved when compared to overlap-and-scan techniques such as Booth's recoding

    Elements of attention in HIV-infected adults: Evaluation of an existing model

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    Because of the multifactorial nature of neuropsychological tests, attention remains poorly defined from a neuropsychological perspective, and conclusions made regarding attention across studies may be limited due to the different nature of the measures used. Thus, a more definitive schema for this neurocognitive domain is needed. We assessed the applicability of Mirsky and Duncan\u27s (2001) neuropsychological model of attention to a cohort of 104 HIV+ adults. Our analysis resulted in a five-factor structure similar to that of previous studies, which explained 74.5% of the variance. However, based on the psychometric characteristics of the measures comprising each factor, we offer an alternative interpretation of the factors. Findings also indicate that one factor, which is generally not assessed in clinical neuropsychology settings, may be more predictive of real-world behaviors (such as medication adherence) than those composed of traditional measures. Suggestions for further research in this important area are discussed
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