73 research outputs found

    Parameter Estimation of Linear Sensorimotor Synchronization Models: Phase Correction, Period Correction, and Ensemble Synchronization

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    <p>Linear models have been used in several contexts to study the mechanisms that underpin sensorimotor synchronization. Given that their parameters are often linked to psychological processes such as phase correction and period correction, the fit of the parameters to experimental data is an important practical question. We present a unified method for parameter estimation of linear sensorimotor synchronization models that extends available techniques and enhances their usability. This method enables reliable and efficient analysis of experimental data for single subject and multi-person synchronization. In a previous paper (Jacoby et al., 2015), we showed how to significantly reduce the estimation error and eliminate the bias of parameter estimation methods by adding a simple and empirically justified constraint on the parameter space. By applying this constraint in conjunction with the tools of matrix algebra, we here develop a novel method for estimating the parameters of most linear models described in the literature. Through extensive simulations, we demonstrate that our method reliably and efficiently recovers the parameters of two influential linear models: Vorberg and Wing (1996), and Schulze et al. (2005), together with their multi-person generalization to ensemble synchronization. We discuss how our method can be applied to include the study of individual differences in sensorimotor synchronization ability, for example, in clinical populations and ensemble musicians.</p> <p><strong>Implementation of the bGLS Method</strong><br>For more information see in <em>Timing & Time Perception</em>, Special issue on Rhythm Production and Perception (RPPW):<br>Jacoby, N., Tishby, N., Repp, B. H., Ahissar, M., & Keller, P. E. (2015). Parameter estimation of linear<br>sensorimotor synchronization models: Phase correction, period correction and ensemble synchronization. <em>Timing Time Percept.</em>, 3, 52-87.<br>and<br>Jacoby, N., Keller, P. E., Repp, B. H., Ahissar, M., & Tishby, N. (2015). Lower bound on the accuracy<br>of parameter estimation methods for linear sensorimotor synchronization models. <em>Timing Time</em><br><em>Percept.</em>, 3, 32-51.</p> <p><br>[email protected]<br>Please cite this paper if you are using this package:<br>Nori Jacoby, Naftali Tishby, Bruno H. Repp, Merav Ahissar and Peter E. Keller (2015)</p> <p><br>IMPORTANT: This code is currently (12/14) in beta.<br>Critical updates will be forthcoming.</p> <p><br>ALL CODE BY: Nori Jacoby ([email protected])</p> <p> </p

    Widespread shorter cortical adaptation in dyslexia

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    AbstractStudies of dyslexics’ performance on perceptual tasks suggest that their implicit inference of sound statistics is impaired. In a previous paper (Jaffe-Dax, Frenkel, &amp; Ahissar, 2017), using 2-tone frequency discrimination, we found that the effect of previous trial frequencies on dyslexics’ judgments decayed faster than the effect on controls’ judgments, and that the adaptation of their ERP responses to tones recovered faster. Here, we show the cortical distribution of this abnormal dynamics of adaptation using fast acquisition fMRI. We find that dyslexics’ faster decay of adaptation is widespread, though the most significant effects are found in the left superior temporal lobe, including the auditory cortex. This broad distribution suggests that dyslexics’ faster decay of implicit memory is a general characteristic of their cortical dynamics, which also encompasses the sensory cortices.</jats:p

    Reduced Benefit from Long-term Item Frequency Contributes to Short-term Memory Deficits in Dyslexia

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    Dyslexia, a specific difficulty in acquiring proficient reading, is also characterized by reduced short-term memory (STM) capacity, which is often attributed to poor phonological memory. However, while it is well established that performance in STM tasks is greatly influenced by the frequency of the comprising items, the effect of item frequency in a span task on the performance of Individuals with Developmental Dyslexia (IDDs) has not been tested until recently. Kimel and colleagues (Kimel et al. 2020; Kimel, Lieder, and Ahissar 2022) asked this question using syllables with high vs. low frequency, and found that the benefit of syllable frequency to STM is reduced among IDDs. We now test the effect of item frequency on the performance in a standard, widely used, STM assessment - the Digit Span subtest from the Wechsler Adult Intelligence Scale. The task was conducted twice: in native language and in second language. As the exposure to native language is greater than to second language, we predicted that IDDs’ performance in the task administered in native language will reveal a larger group difference as compared to second language, due to IDDs’ reduced benefit of item frequency. The prediction was confirmed, in line with the hypothesis that reduced STM in dyslexia to a large extent reflects reduced benefits from long-term item frequency and not a reduced STM per-se

    Perceptual Learning 2012

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    Learning Pop-out Detection: Specificities to Stimulus Characteristics

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    AbstractTraining induces dramatic improvement in the performance of pop-out detection. In this study, we examined the specificities of this improvement to stimulus characteristics. We found that learning is specific within basic visual dimensions: orientation, size and position. Accordingly, following training with one set of orientations, rotating target and distractors by 30 deg or more substantially hampers performance. Furthermore, rotation of either target or distractors alone greatly increases threshold. Learning is not transferred to reduced-size stimuli. Position specificity near fixation may be finer than 0.7 deg. On the other hand, learning transfers to the untrained eye, to expanded images, to mirror image transformations and to homologous positions across the midline (near fixation). Thus, learning must occur at a processing level which is early enough to maintain fine separability along basic stimulus dimensions, yet sufficiently high to manifest the described generalizations. We suggest that the site of early perceptual learning is one of the cortical areas which receive input from primary visual cortex, V1, and where top-down attentional control is present. Copyright © 1996 Elsevier Science Ltd
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