scholarly journals Statistical learning occurs during practice while high-order rule learning during rest period

2020 ◽  
Author(s):  
Romain Quentin ◽  
Lison Fanuel ◽  
Mariann Kiss ◽  
Marine Vernet ◽  
Teodóra Vékony ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Temporal Dynamics ◽  
Rule Learning ◽  
High Order ◽  
Procedural Learning ◽  
Skill Learning ◽  
Rest Periods ◽  
The Brain ◽  
General Skill ◽  
Order Rule

AbstractKnowing when the brain learns is crucial for both the comprehension of memory formation and consolidation, and for developing new training and neurorehabilitation strategies in healthy and patient populations. Recently, a rapid form of offline learning developing during short rest periods has been shown to account for most of procedural learning, leading to the hypothesis that the brain mainly learns during rest between practice periods. Nonetheless, procedural learning has several subcomponents not disentangled in previous studies investigating learning dynamics, such as acquiring the statistical regularities of the task, or else the high-order rules that regulate its organization. Here, we analyzed 506 behavioral sessions of implicit visuomotor deterministic and probabilistic sequence learning tasks, allowing the distinction between general skill learning, statistical learning and high-order rule learning. Our results show that the temporal dynamics of apparently simultaneous learning processes differ. While general skill and high-order rule learning are acquired offline, statistical learning is evidenced online. These findings open new avenues on the short-scale temporal dynamics of learning and memory consolidation and reveal a fundamental distinction between statistical and high-order rule learning, the former benefiting from online evidence accumulation and the latter requiring short rest periods for rapid consolidation.

scholarly journals Statistical learning occurs during practice while high-order rule learning during rest period

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Romain Quentin ◽  
Lison Fanuel ◽  
Mariann Kiss ◽  
Marine Vernet ◽  
Teodóra Vékony ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Temporal Dynamics ◽  
Rule Learning ◽  
Rest Period ◽  
High Order ◽  
Procedural Learning ◽  
Skill Learning ◽  
Rest Periods ◽  
The Brain ◽  
Order Rule

AbstractKnowing when the brain learns is crucial for both the comprehension of memory formation and consolidation and for developing new training and neurorehabilitation strategies in healthy and patient populations. Recently, a rapid form of offline learning developing during short rest periods has been shown to account for most of procedural learning, leading to the hypothesis that the brain mainly learns during rest between practice periods. Nonetheless, procedural learning has several subcomponents not disentangled in previous studies investigating learning dynamics, such as acquiring the statistical regularities of the task, or else the high-order rules that regulate its organization. Here we analyzed 506 behavioral sessions of implicit visuomotor deterministic and probabilistic sequence learning tasks, allowing the distinction between general skill learning, statistical learning, and high-order rule learning. Our results show that the temporal dynamics of apparently simultaneous learning processes differ. While high-order rule learning is acquired offline, statistical learning is evidenced online. These findings open new avenues on the short-scale temporal dynamics of learning and memory consolidation and reveal a fundamental distinction between statistical and high-order rule learning, the former benefiting from online evidence accumulation and the latter requiring short rest periods for rapid consolidation.

scholarly journals Is there more room to improve? The lifespan trajectory of procedural learning and its relationship to the between- and within-group differences in average response times

2019 ◽  
Author(s):  
Dora Juhasz ◽  
Dezso Nemeth ◽  
Karolina Janacsek
Keyword(s):  
Statistical Learning ◽  
Cognitive Functions ◽  
Response Times ◽  
Developmental Trajectories ◽  
Age Groups ◽  
Procedural Learning ◽  
Skill Learning ◽  
Group Differences ◽  
Average Speed ◽  
General Skill

AbstractCharacterizing the developmental trajectories of cognitive functions such as learning, memory and decision making across the lifespan faces fundamental challenges. Cognitive functions typically encompass several processes that can be differentially affected by age. Methodological issues also arise when comparisons are made across age groups that differ in basic performance measures, such as in average response times (RTs). Here we focus on procedural learning – a fundamental cognitive function that underlies the acquisition of cognitive, social, and motor skills – and demonstrate how disentangling subprocesses of learning and controlling for differences in average RTs can reveal different developmental trajectories across the human lifespan. Two hundred-seventy participants aged between 7 and 85 years performed a probabilistic sequence learning task that enabled us to separately measure two processes of procedural learning, namely general skill learning and statistical learning. Using raw RT measures, in between-group comparisons, we found a U-shaped trajectory with children and older adults exhibiting greater general skill learning compared to adolescents and younger adults. However, when we controlled for differences in average RTs (either by using ratio scores or focusing on a subsample of participants with similar average speed), only children (but not older adults) demonstrated superior general skill learning consistently across analyses. Testing the relationship between average RTs and general skill learning within age groups shed light on further age-related differences, suggesting that general skill learning measures are more affected by average speed in some age groups. Consistent with previous studies of learning probabilistic regularities, statistical learning showed a gradual decline across the lifespan, and learning performance seemed to be independent of average speed, regardless of the age group. Overall, our results suggest that children are superior learners in various aspects of procedural learning, including both general skill and statistical learning. Our study also highlights the importance to test, and control for, the effect of average speed on other RT measures of cognitive functions, which can fundamentally affect the interpretation of group differences in developmental, aging and clinical psychology and neuroscience studies.

Direct Comparison of Neural Systems Mediating Conscious and Unconscious Skill Learning

2002 ◽  
Vol 88 (3) ◽  
pp. 1451-1460 ◽  
Author(s):  
Daniel B. Willingham ◽  
Joanna Salidis ◽  
John D.E. Gabrieli
Keyword(s):  
Sequence Learning ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Procedural Learning ◽  
Skill Learning ◽  
Adaptive Plasticity ◽  
Neural Systems ◽  
Motor Sequence ◽  
Learning Without Awareness ◽  
The Brain

Procedural learning, such as perceptual-motor sequence learning, has been suggested to be an obligatory consequence of practiced performance and to reflect adaptive plasticity in the neural systems mediating performance. Prior neuroimaging studies, however, have found that sequence learning accompanied with awareness (declarative learning) of the sequence activates entirely different brain regions than learning without awareness of the sequence (procedural learning). Functional neuroimaging was used to assess whether declarative sequence learning prevents procedural learning in the brain. Awareness of the sequence was controlled by changing the color of the stimuli to match or differ from the color used for random sequences. This allowed direct comparison of brain activation associated with procedural and declarative memory for an identical sequence. Activation occurred in a common neural network whether initial learning had occurred with or without awareness of the sequence, and whether subjects were aware or not aware of the sequence during performance. There was widespread additional activation associated with awareness of the sequence. This supports the view that some types of unconscious procedural learning occurs in the brain whether or not it is accompanied by conscious declarative knowledge.

scholarly journals Statistical Properties of Musical Creativity: Roles of Hierarchy and Uncertainty in Statistical Learning

2021 ◽  
Vol 15 ◽  
Author(s):  
Tatsuya Daikoku ◽  
Geraint A. Wiggins ◽  
Yukie Nagai
Keyword(s):  
Brain Development ◽  
Statistical Learning ◽  
Temporal Dynamics ◽  
Intention To Use ◽  
Statistical Knowledge ◽  
Structured Information ◽  
Musical Creativity ◽  
The Brain ◽  
Shed Light ◽  
Atypical Brain Development

Creativity is part of human nature and is commonly understood as a phenomenon whereby something original and worthwhile is formed. Owing to this ability, humans can produce innovative information that often facilitates growth in our society. Creativity also contributes to esthetic and artistic productions, such as music and art. However, the mechanism by which creativity emerges in the brain remains debatable. Recently, a growing body of evidence has suggested that statistical learning contributes to creativity. Statistical learning is an innate and implicit function of the human brain and is considered essential for brain development. Through statistical learning, humans can produce and comprehend structured information, such as music. It is thought that creativity is linked to acquired knowledge, but so-called “eureka” moments often occur unexpectedly under subconscious conditions, without the intention to use the acquired knowledge. Given that a creative moment is intrinsically implicit, we postulate that some types of creativity can be linked to implicit statistical knowledge in the brain. This article reviews neural and computational studies on how creativity emerges within the framework of statistical learning in the brain (i.e., statistical creativity). Here, we propose a hierarchical model of statistical learning: statistically chunking into a unit (hereafter and shallow statistical learning) and combining several units (hereafter and deep statistical learning). We suggest that deep statistical learning contributes dominantly to statistical creativity in music. Furthermore, the temporal dynamics of perceptual uncertainty can be another potential causal factor in statistical creativity. Considering that statistical learning is fundamental to brain development, we also discuss how typical versus atypical brain development modulates hierarchical statistical learning and statistical creativity. We believe that this review will shed light on the key roles of statistical learning in musical creativity and facilitate further investigation of how creativity emerges in the brain.

scholarly journals The longer the better? General skill but not probabilistic learning improves with the duration of short rest periods

2020 ◽  
Author(s):  
Lison Fanuel ◽  
Claire Plèche ◽  
Teodóra Vékony ◽  
Romain Quentin ◽  
Karolina Janacsek ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Sequence Learning ◽  
Memory Consolidation ◽  
Reaction Times ◽  
Learning Task ◽  
Skill Learning ◽  
Rest Periods ◽  
General Skills ◽  
Probabilistic Sequences ◽  
General Skill

AbstractMemory consolidation has mainly been investigated for extended periods, from hours to days. Recent studies suggest that memory consolidation can also occur within shorter periods, from minutes to seconds. Our study aimed at determining (1) whether short rest periods lead to improvements in implicit probabilistic sequence learning and (2) whether length of rest duration influences such offline improvements. Participants performed an implicit probabilistic sequence learning task throughout 45 blocks. Between blocks, participants were allowed to rest and then to continue the task in their pace. The overall reaction times (general skill learning) shortened from pre- to post-rest periods, and this improvement was increased for longer rest durations. However, probabilistic sequences knowledge decreased in these periods, and this decrement was not related to the length of rest duration. These results suggest that (1) general skill learning but not probabilistic sequence knowledge benefits from short rest periods and, possibly, from memory consolidation, (2) ultra-fast offline improvements in general skills, but not forgetting in probabilistic sequence knowledge, are time-dependent. Overall, our findings highlight that ultra-fast consolidation differently affects distinct cognitive processes.

scholarly journals Stability and flexibility in multisensory sampling: insights from perceptual illusions

2019 ◽  
Vol 121 (5) ◽  
pp. 1588-1590 ◽  
Author(s):  
Luca Casartelli
Keyword(s):  
Temporal Dynamics ◽  
Multisensory Processing ◽  
General Idea ◽  
Perceptual Illusions ◽  
The Brain ◽  
Multisensory Processes

Neural, oscillatory, and computational counterparts of multisensory processing remain a crucial challenge for neuroscientists. Converging evidence underlines a certain efficiency in balancing stability and flexibility of sensory sampling, supporting the general idea that multiple parallel and hierarchically organized processing stages in the brain contribute to our understanding of the (sensory/perceptual) world. Intriguingly, how temporal dynamics impact and modulate multisensory processes in our brain can be investigated benefiting from studies on perceptual illusions.

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

scholarly journals The brain timewise: how timing shapes and supports brain function

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140170 ◽  
Author(s):  
Riitta Hari ◽  
Lauri Parkkonen
Keyword(s):  
Human Brain ◽  
Brain Imaging ◽  
Brain Function ◽  
Temporal Dynamics ◽  
Generative Models ◽  
Network Activity ◽  
Brain Diseases ◽  
Specific Information ◽  
Non Invasive ◽  
The Brain

We discuss the importance of timing in brain function: how temporal dynamics of the world has left its traces in the brain during evolution and how we can monitor the dynamics of the human brain with non-invasive measurements. Accurate timing is important for the interplay of neurons, neuronal circuitries, brain areas and human individuals. In the human brain, multiple temporal integration windows are hierarchically organized, with temporal scales ranging from microseconds to tens and hundreds of milliseconds for perceptual, motor and cognitive functions, and up to minutes, hours and even months for hormonal and mood changes. Accurate timing is impaired in several brain diseases. From the current repertoire of non-invasive brain imaging methods, only magnetoencephalography (MEG) and scalp electroencephalography (EEG) provide millisecond time-resolution; our focus in this paper is on MEG. Since the introduction of high-density whole-scalp MEG/EEG coverage in the 1990s, the instrumentation has not changed drastically; yet, novel data analyses are advancing the field rapidly by shifting the focus from the mere pinpointing of activity hotspots to seeking stimulus- or task-specific information and to characterizing functional networks. During the next decades, we can expect increased spatial resolution and accuracy of the time-resolved brain imaging and better understanding of brain function, especially its temporal constraints, with the development of novel instrumentation and finer-grained, physiologically inspired generative models of local and network activity. Merging both spatial and temporal information with increasing accuracy and carrying out recordings in naturalistic conditions, including social interaction, will bring much new information about human brain function.

scholarly journals Mimicry is associated with procedural learning, not social bonding, neural systems in autism

2020 ◽  
Author(s):  
Bahar Tunçgenç ◽  
Carolyn Koch ◽  
Amira Herstic ◽  
Inge-Marie Eigsti ◽  
Stewart Mostofsky
Keyword(s):  
Social Bonding ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Skill Learning ◽  
Autism Spectrum Conditions ◽  
Neural Systems ◽  
Procedural Skill ◽  
Communication Difficulties ◽  
Learning Functions ◽  
The Brain

AbstractMimicry facilitates social bonding throughout the lifespan. Mimicry impairments in autism spectrum conditions (ASC) are widely reported, including differentiation of the brain networks associated with its social bonding and learning functions. This study examined associations between volumes of brain regions associated with social bonding versus procedural skill learning, and mimicry of gestures during a naturalistic interaction in ASC and neurotypical (NT) children. Consistent with predictions, results revealed reduced mimicry in ASC relative to the NT children. Mimicry frequency was negatively associated with autism symptom severity. Mimicry was predicted predominantly by the volume of procedural skill learning regions in ASC, and by bonding regions in NT. Further, bonding regions contributed significantly less to mimicry in ASC than in NT, while the contribution of learning regions was not different across groups. These findings suggest that associating mimicry with skill learning, rather than social bonding, may partially explain observed communication difficulties in ASC.

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