The Experts below are selected from a list of 112395 Experts worldwide ranked by ideXlab platform
Rachael D Seidler - One of the best experts on this subject based on the ideXlab platform.
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Sequence Learning in parkinson s disease focusing on action dynamics and the role of dopaminergic medication
Neuropsychologia, 2016Co-Authors: Rachael D Seidler, Marit F L Ruitenberg, Wout Duthoo, Patrick Santens, Wim Notebaert, Elger L AbrahamseAbstract:Previous studies on movement Sequence Learning in Parkinson's disease (PD) have produced mixed results. A possible explanation for the inconsistent findings is that some studies have taken dopaminergic medication into account while others have not. Additionally, in previous studies the response modalities did not allow for an investigation of the action dynamics of sequential movements as they unfold over time. In the current study we investigated Sequence Learning in PD by specifically considering the role of medication status in a Sequence Learning task where mouse movements were performed. The focus on mouse movements allowed us to examine the action dynamics of sequential movement in terms of initiation time, movement time, movement accuracy, and velocity. PD patients performed the Sequence Learning task once on their regular medication, and once after overnight withdrawal from their medication. Results showed that Sequence Learning as reflected in initiation times was impaired when PD patients performed the task ON medication compared to OFF medication. In contrast, Sequence Learning as reflected in the accuracy of movement trajectories was enhanced when performing the task ON compared to OFF medication. Our findings suggest that while medication enhances execution processes of movement Sequence Learning, it may at the same time impair planning processes that precede actual execution. Overall, the current study extends earlier findings on movement Sequence Learning in PD by differentiating between various components of performance, and further refines previous dopamine overdose effects in Sequence Learning.
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The effect of haptic cues on motor and perceptual based implicit Sequence Learning
Frontiers in human neuroscience, 2014Co-Authors: Dong-won Kim, Brandon J. Johnson, R. Brent Gillespie, Rachael D SeidlerAbstract:We introduced haptic cues to the serial reaction time (SRT) Sequence Learning task alongside the standard visual cues to assess the relative contributions of haptic and visual stimuli to the formation of motor and perceptual memories. We used motorized keys to deliver brief pulse-like displacements to the resting fingers, expecting that the proximity and similarity of these cues to the subsequent response motor actions (finger activated key-presses) would strengthen the motor memory trace in particular. We adopted the experimental protocol developed by Willingham in 1999 to explore whether haptic cues contribute differently than visual cues to the balance of motor and perceptual Learning. We found that Sequence Learning occurs with haptic stimuli as with visual stimuli and we found that irrespective of the stimuli (visual or haptic) the serial reaction time task leads to a greater amount of motor Learning than perceptual Learning.
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age differences in symbolic representations of motor Sequence Learning
Neuroscience Letters, 2011Co-Authors: Scott Peltier, Douglas C Noll, Rachael D SeidlerAbstract:We recently reported that young adults (YA) preferentially recruit cerebellar lobule HVI for symbolic motor Sequence Learning [3]. Learning magnitude in the symbolic condition was correlated with activation level in lobule HVI. Here, we evaluated age differences in the symbolic representation of motor Sequence Learning. Fourteen YA and 14 older adults (OA) performed the alternating serial reaction time task (ASRT) under conditions in which the spatial processing component was selectively eliminated from stimulus presentation (spatial versus symbolic), response execution (manual versus vocal), or both. Results showed that OA had reduced Learning magnitudes relative to YA. Using the cerebellum lobule HVI as a region-of-interest, we found that OA had significantly lower activation in this region than YA during the symbolic Learning conditions (FWE, P<0.05). Similar to YA, OA also showed a significant correlation between Learning magnitude and cerebellar activation in the symbolic conditions. These results suggest that although YA and OA recruit similar neural networks during implicit Learning, OA under-recruit relevant brain areas which may partially explain their implicit Sequence Learning deficits.
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symbolic representations in motor Sequence Learning
NeuroImage, 2011Co-Authors: Scott Peltier, Douglas C Noll, Rachael D SeidlerAbstract:It has been shown that varying the spatial versus symbolic nature of stimulus presentation and response production, which affects stimulus-response (S-R) mapping requirements, influences the magnitude of implicit Sequence Learning (Koch & Hoffman, 2000). Here, we evaluated how spatial and symbolic stimuli and responses affect the neural bases of Sequence Learning. We selectively eliminated the spatial component of stimulus presentation (spatial versus symbolic), response execution (manual versus vocal), or both. Fourteen participants performed the alternating serial reaction time task under these conditions in an MRI scanner, with interleaved acquisition to allow for recording of vocal response reaction times. Nine regions of interest (ROI) were selected to test the hypothesis that the dorsolateral prefrontal cortex (DLPFC) was preferentially engaged for spatially cued conditions and cerebellum lobule HVI, crus I & II were associated with symbolically cued Learning. We found that left cerebellum lobule HVI was selectively recruited for symbolic Learning and the percent signal change in this region was correlated with Learning magnitude under the symbolic conditions. In contrast, the DLPFC did not exhibit selective activation for Learning under spatial conditions. The inferior parietal lobule exhibited increased activation during Learning regardless of the condition, supporting its role in forming an abstract representation of learned Sequences. These findings reveal different brain networks that are flexibly engaged depending on the conditions of Sequence Learning.
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spatial and symbolic implicit Sequence Learning in young and older adults
Experimental Brain Research, 2010Co-Authors: Rachael D SeidlerAbstract:In three experiments, we examined the effects of age and spatial processing on implicit Sequence Learning. In experiment 1, 48 older adults (OA) and 48 young adults (YA) performed the alternating serial reaction time task (ASRT) under one of four conditions in which spatial processing demands were either present or absent from stimulus presentation (spatial vs. symbolic cueing) and/or response execution (spatial manual vs. vocal responses). Surprisingly, OA exhibited more Learning than YA in the two vocal response conditions. In two follow-up experiments, we increased the response selection demands of the Sequence Learning task by asking new groups of YA and OA participants to make word categorization responses with 1:1 stimulus–response mapping (experiment 2) and 2:1 mapping (experiment 3) rather than letter reading (vocal response condition of experiment 1). The results showed that YA had increased Learning under the more challenging response selection conditions (experiments 2 and 3) while OA did not. We propose that (1) manipulating the spatial aspects of implicit Sequence Learning does not necessarily impact the amount of Learning for OA, (2) implicit Sequence Learning depends on both the structure of response execution and relative task difficulty, and (3) these factors affect implicit Sequence Learning for both YA and OA.
Jeff Hawkins - One of the best experts on this subject based on the ideXlab platform.
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continuous online Sequence Learning with an unsupervised neural network model
Neural Computation, 2016Co-Authors: Yuwei Cui, Subutai Ahmad, Jeff HawkinsAbstract:The ability to recognize and predict temporal Sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory HTM Sequence memory recently has been proposed as a theoretical framework for Sequence Learning in the cortex. In this letter, we analyze properties of HTM Sequence memory and apply it to Sequence Learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variable order temporal Sequences using an unsupervised Hebbian-like Learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal Sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM Sequence memory with other Sequence Learning algorithms, including statistical methods-autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme Learning machine; and recurrent neural networks-long short-term memory and echo-state networks on Sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for Sequence Learning, including continuous online Learning, the ability to handle multiple predictions and branching Sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM Sequence memory not only advances our understanding of how the brain may solve the Sequence Learning problem but is also applicable to real-world Sequence Learning problems from continuous data streams.
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Continuous online Sequence Learning with an unsupervised neural network model
Neural Computation, 2016Co-Authors: Yuwei Cui, Subutai Ahmad, Jeff HawkinsAbstract:The ability to recognize and predict temporal Sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) Sequence memory is recently proposed as a theoretical framework for Sequence Learning in the cortex. In this paper, we analyze properties of HTM Sequence memory and apply it to Sequence Learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variable-order temporal Sequences using an unsupervised Hebbian-like Learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal Sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM Sequence memory with other Sequence Learning algorithms, including statistical methods: autoregressive integrated moving average (ARIMA), feedforward neural networks: online sequential extreme Learning machine (ELM), and recurrent neural networks: long short-term memory (LSTM) and echo-state networks (ESN), on Sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for Sequence Learning, including continuous online Learning, the ability to handle multiple predictions and branching Sequences with high order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyper- parameters tuning. Therefore the HTM Sequence memory not only advances our understanding of how the brain may solve the Sequence Learning problem, but is also applicable to a wide range of real-world problems such as discrete and continuous Sequence prediction, anomaly detection, and Sequence classification.
James H Howard - One of the best experts on this subject based on the ideXlab platform.
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sleep has no critical role in implicit motor Sequence Learning in young and old adults
Experimental Brain Research, 2010Co-Authors: Michael T Ullman, Karolina Janacsek, Dezso Nemeth, James H Howard, Darlene V Howard, Zsuzsa LondeAbstract:The influence of sleep on motor skill consolidation has been a research topic of increasing interest. In this study, we distinguished general skill Learning from Sequence-specific Learning in a probabilistic implicit Sequence Learning task (alternating serial reaction time) in young and old adults before and after a 12-h offline interval which did or did not contain sleep (p.m.-a.m. and a.m.-p.m. groups, respectively). The results showed that general skill Learning, as assessed via overall reaction time, improved offline in both the young and older groups, with the young group improving more than the old. However, the improvement was not sleep-dependent, in that there was no difference between the a.m.-p.m. and p.m.-a.m. groups. We did not find Sequence-specific offline improvement in either age group for the a.m.-either p.m. or p.m.-a.m. groups, suggesting that consolidation of this kind of implicit motor Sequence Learning may not be influenced by sleep.
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perceptual Sequence Learning in a serial reaction time task
Experimental Brain Research, 2008Co-Authors: Sunbin Song, James H Howard, Darlene V HowardAbstract:In the serial reaction time task (SRTT), a Sequence of visuo-spatial cues instructs subjects to perform a Sequence of movements which follow a repeating pattern. Though motor responses are known to support implicit Sequence Learning in this task, the goal of the present experiments is to determine whether observation of the Sequence of cues alone can also yield evidence of implicit Sequence Learning. This question has been difficult to answer because in previous research, performance improvements which appeared to be due to implicit perceptual Sequence Learning could also be due to spontaneous increases in explicit knowledge of the Sequence. The present experiments use probabilistic Sequences to prevent the spontaneous development of explicit awareness. They include a training phase, during which half of the subjects observe and the other half respond, followed by a transfer phase in which everyone responds. Results show that observation alone can support Sequence Learning, which translates at transfer into equivalent performance as that of a group who made motor responses during training. However, perceptual Learning or its expression is sensitive to changes in target colors, and its expression is impaired by concurrent explicit search. Motor-response based Learning is not affected by these manipulations. Thus, observation alone can support implicit Sequence Learning, even of higher order probabilistic Sequences. However, perceptual Learning can be prevented or concealed by variations of stimuli or task demands.
Dezso Nemeth - One of the best experts on this subject based on the ideXlab platform.
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intention to learn differentially affects subprocesses of procedural Learning and consolidation evidence from a probabilistic Sequence Learning task
bioRxiv, 2019Co-Authors: Karolina Janacsek, Dezso Nemeth, Kata Horvath, Csenge Torok, Orsolya PesthyAbstract:Procedural memory facilitates the efficient processing of complex environmental stimuli and contributes to the acquisition of automatic behaviours and habits. Learning can occur intentionally or incidentally, yet, how the mode of Learning affects procedural memory is still poorly understood. Importantly, procedural memory is a complex cognitive function composed of different subprocesses, including the acquisition and consolidation of statistical, frequency-based and sequential, order-based knowledge. Therefore, we tested how statistical and Sequence knowledge develops during incidental versus intentional procedural memory formation and during consolidation. Seventy-four young adults performed either the uncued, incidental (N = 37) or the cued, intentional (N = 37) version of a probabilistic Sequence Learning task. Performance was retested after a 12-hour offline period, enabling us to test the effect of sleep on consolidation; therefore, half of the participants slept during the delay, while the other half had normal daily activity (PM-AM versus AM-PM design). The mode of Learning (incidental versus intentional) had no effect on the acquisition of statistical knowledge, while intention to learn increased Sequence Learning performance. Consolidation was not affected by intention to learn: Both statistical and Sequence knowledge was retained over the 12-hour delay, irrespective of the mode of Learning and whether the delay included sleep or wake activity. These results suggest a time-dependent instead of sleep-dependent consolidation of both statistical and Sequence knowledge. Our findings could contribute to a better understanding of how the mode of Learning (intentional or incidental) affects procedural memory formation and consolidation.
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is procedural memory enhanced in tourette syndrome evidence from a Sequence Learning task
Cortex, 2017Co-Authors: Michael T Ullman, Adam Takacs, Andrea Kobor, Julia Chezan, Noemi Eltető, Zsanett Tarnok, Dezso Nemeth, Karolina JanacsekAbstract:Procedural memory, which is rooted in the basal ganglia, underlies the Learning and processing of numerous automatized motor and cognitive skills, including in language. Not surprisingly, disorders with basal ganglia abnormalities have been found to show impairments of procedural memory. However, brain abnormalities could also lead to atypically enhanced function. Tourette syndrome (TS) is a candidate for enhanced procedural memory, given previous findings of enhanced TS processing of grammar, which likely depends on procedural memory. We comprehensively examined procedural Learning, from memory formation to retention, in children with TS and typically developing (TD) children, who performed an implicit Sequence Learning task over two days. The children with TS showed Sequence Learning advantages on both days, despite a regression of Sequence knowledge overnight to the level of the TD children. This is the first demonstration of procedural Learning advantages in any disorder. The findings may further our understanding of procedural memory and its enhancement. The evidence presented here, together with previous findings suggesting enhanced grammar processing in TS, underscore the dependence of language on a system that also subserves visuomotor sequencing.
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implicit Sequence Learning and working memory correlated or complicated
Cortex, 2013Co-Authors: Karolina Janacsek, Dezso NemethAbstract:The relationship between implicit/incidental Sequence Learning and working memory motivated a series of research because it is plausible that higher working memory capacity opens a “larger window” to a Sequence, allowing thereby the Sequence Learning process to be easier. Although the majority of studies found no relationship between implicit Sequence Learning and working memory capacity, in the past few years several studies have tried to demonstrate the shared or partly shared brain networks underlying these two systems. In order to help the interpretation of these and future results, in this mini-review we suggest the following factors to be taken into consideration before testing the relationship between Sequence Learning and working memory: 1) the explicitness of the Sequence; 2) the method of measuring working memory capacity; 3) online and offline stages of Sequence Learning; and 4) general skill- and Sequence-specific Learning.
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sleep has no critical role in implicit motor Sequence Learning in young and old adults
Experimental Brain Research, 2010Co-Authors: Michael T Ullman, Karolina Janacsek, Dezso Nemeth, James H Howard, Darlene V Howard, Zsuzsa LondeAbstract:The influence of sleep on motor skill consolidation has been a research topic of increasing interest. In this study, we distinguished general skill Learning from Sequence-specific Learning in a probabilistic implicit Sequence Learning task (alternating serial reaction time) in young and old adults before and after a 12-h offline interval which did or did not contain sleep (p.m.-a.m. and a.m.-p.m. groups, respectively). The results showed that general skill Learning, as assessed via overall reaction time, improved offline in both the young and older groups, with the young group improving more than the old. However, the improvement was not sleep-dependent, in that there was no difference between the a.m.-p.m. and p.m.-a.m. groups. We did not find Sequence-specific offline improvement in either age group for the a.m.-either p.m. or p.m.-a.m. groups, suggesting that consolidation of this kind of implicit motor Sequence Learning may not be influenced by sleep.
Paul J Reber - One of the best experts on this subject based on the ideXlab platform.
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operating characteristics of the implicit Learning system supporting serial interception Sequence Learning
Journal of Experimental Psychology: Human Perception and Performance, 2012Co-Authors: Daniel J Sanchez, Paul J ReberAbstract:The memory system that supports implicit perceptual-motor Sequence Learning relies on brain regions that operate separately from the explicit, medial temporal lobe memory system. The implicit Learning system therefore likely has distinct operating characteristics and information processing constraints. To attempt to identify the limits of the implicit Sequence Learning mechanism, participants performed the serial interception Sequence Learning (SISL) task with covertly embedded repeating Sequences that were much longer than most previous studies: ranging from 30 to 60 (Experiment 1) and 60 to 90 (Experiment 2) items in length. Robust Sequence-specific Learning was observed for Sequences up to 80 items in length, extending the known capacity of implicit Sequence Learning. In Experiment 3, 12-item repeating Sequences were embedded among increasing amounts of irrelevant nonrepeating Sequences (from 20 to 80% of training trials). Despite high levels of irrelevant trials, Learning occurred across conditions. A comparison of Learning rates across all three experiments found a surprising degree of constancy in the rate of Learning regardless of Sequence length or embedded noise. Sequence Learning appears to be constant with the logarithm of the number of Sequence repetitions practiced during training. The consistency in Learning rate across experiments and conditions implies that the mechanisms supporting implicit Sequence Learning are not capacity-constrained by very long Sequences nor adversely affected by high rates of irrelevant Sequences during training.
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integration of temporal and ordinal information during serial interception Sequence Learning
Journal of Experimental Psychology: Learning Memory and Cognition, 2011Co-Authors: Eric W Gobel, Daniel J Sanchez, Paul J ReberAbstract:The expression of expert motor skills typically involves Learning to perform a precisely timed Sequence of movements. Research examining incidental Sequence Learning has relied on a perceptually cued task that gives participants exposure to repeating motor Sequences but does not require timing of responses for accuracy. In the 1st experiment, a novel perceptual-motor Sequence Learning task was used, and Learning a precisely timed cued Sequence of motor actions was shown to occur without explicit instruction. Participants learned a repeating Sequence through practice and showed Sequence-specific knowledge via a performance decrement when switched to an unfamiliar Sequence. In the 2nd experiment, the integration of representation of action order and timing Sequence knowledge was examined. When either action order or timing Sequence information was selectively disrupted, performance was reduced to levels similar to completely novel Sequences. Unlike prior Sequence-Learning research that has found timing information to be secondary to Learning action Sequences, when the task demands require accurate action and timing information, an integrated representation of these types of information is acquired. These results provide the first evidence for incidental Learning of fully integrated action and timing Sequence information in the absence of an independent representation of action order and suggest that this integrative mechanism may play a material role in the acquisition of complex motor skills.
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performing the unexplainable implicit task performance reveals individually reliable Sequence Learning without explicit knowledge
Psychonomic Bulletin & Review, 2010Co-Authors: Daniel J Sanchez, Eric W Gobel, Paul J ReberAbstract:Memory-impaired patients express intact implicit perceptual-motor Sequence Learning, but it has been difficult to obtain a similarly clear dissociation in healthy participants. When explicit memory is intact, participants acquire some explicit knowledge and performance improvements from implicit Learning may be subtle. Therefore, it is difficult to determine whether performance exceeds what could be expected on the basis of the concomitant explicit knowledge. Using a challenging new Sequence-Learning task, robust implicit Learning was found in healthy participants with virtually no associated explicit knowledge. Participants trained on a repeating Sequence that was selected randomly from a set of five. On a performance test of all five Sequences, performance was best on the trained Sequence, and two-thirds of the participants exhibited individually reliable improvement (by chi-square analysis). Participants could not reliably indicate which Sequence had been trained by either recognition or recall. Only by expressing their knowledge via performance were participants able to indicate which Sequence they had learned.
