scholarly journals Statistical Properties in Jazz Improvisation Underline Individuality of Musical Representation

NeuroSci ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 24-43
Author(s):  
Tatsuya Daikoku
Keyword(s):  
Statistical Learning ◽  
Time Course ◽  
Statistical Properties ◽  
Transitional Probability ◽  
Jazz Improvisation ◽  
Statistical Knowledge ◽  
Future State ◽  
Structured Information ◽  
Transitional Probabilities ◽  
The Brain

Statistical learning is an innate function in the brain and considered to be essential for producing and comprehending structured information such as music. Within the framework of statistical learning the brain has an ability to calculate the transitional probabilities of sequences such as speech and music, and to predict a future state using learned statistics. This paper computationally examines whether and how statistical learning and knowledge partially contributes to musical representation in jazz improvisation. The results represent the time-course variations in a musician’s statistical knowledge. Furthermore, the findings show that improvisational musical representation might be susceptible to higher- but not lower-order statistical knowledge (i.e., knowledge of higher-order transitional probability). The evidence also demonstrates the individuality of improvisation for each improviser, which in part depends on statistical knowledge. Thus, this study suggests that statistical properties in jazz improvisation underline individuality of musical representation.

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 Neural Signatures of Spatial Statistical Learning: Characterizing the Extraction of Structure from Complex Visual Scenes

2017 ◽  
Vol 29 (12) ◽  
pp. 1963-1976 ◽  
Author(s):  
Elisabeth A. Karuza ◽  
Lauren L. Emberson ◽  
Matthew E. Roser ◽  
Daniel Cole ◽  
Richard N. Aslin ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Time Course ◽  
Functional Integration ◽  
Univariate Analysis ◽  
Brain Regions ◽  
Behavioral Performance ◽  
Posttest Performance ◽  
Behavioral Evidence ◽  
Performance Results ◽  
The Brain

Behavioral evidence has shown that humans automatically develop internal representations adapted to the temporal and spatial statistics of the environment. Building on prior fMRI studies that have focused on statistical learning of temporal sequences, we investigated the neural substrates and mechanisms underlying statistical learning from scenes with a structured spatial layout. Our goals were twofold: (1) to determine discrete brain regions in which degree of learning (i.e., behavioral performance) was a significant predictor of neural activity during acquisition of spatial regularities and (2) to examine how connectivity between this set of areas and the rest of the brain changed over the course of learning. Univariate activity analyses indicated a diffuse set of dorsal striatal and occipitoparietal activations correlated with individual differences in participants' ability to acquire the underlying spatial structure of the scenes. In addition, bilateral medial-temporal activation was linked to participants' behavioral performance, suggesting that spatial statistical learning recruits additional resources from the limbic system. Connectivity analyses examined, across the time course of learning, psychophysiological interactions with peak regions defined by the initial univariate analysis. Generally, we find that task-based connectivity with these regions was significantly greater in early relative to later periods of learning. Moreover, in certain cases, decreased task-based connectivity between time points was predicted by overall posttest performance. Results suggest a narrowing mechanism whereby the brain, confronted with a novel structured environment, initially boosts overall functional integration and then reduces interregional coupling over time.

scholarly journals Divided attention does not affect the acquisition and consolidation of transitional probabilities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kata Horváth ◽  
Csenge Török ◽  
Orsolya Pesthy ◽  
Dezso Nemeth ◽  
Karolina Janacsek
Keyword(s):  
Statistical Learning ◽  
Divided Attention ◽  
Reaction Time Task ◽  
Learning Task ◽  
Second Order ◽  
The Other ◽  
Attentional Processes ◽  
Statistical Knowledge ◽  
Environmental Events ◽  
Transitional Probabilities

AbstractStatistical learning facilitates the efficient processing and prediction of environmental events and contributes to the acquisition of automatic behaviors. Whereas a minimal level of attention seems to be required for learning to occur, it is still unclear how acquisition and consolidation of statistical knowledge are affected when attention is divided during learning. To test the effect of divided attention on statistical learning and consolidation, ninety-six healthy young adults performed the Alternating Serial Reaction Time task in which they incidentally acquired second-order transitional probabilities. Half of the participants completed the task with a concurrent secondary intentional sequence learning task that was applied to the same stimulus stream. The other half of the participants performed the task without any attention manipulation. Performance was retested after a 12-h post-learning offline period. Half of each group slept during the delay, while the other half had normal daily activity, enabling us to test the effect of delay activity (sleep vs. wake) on the consolidation of statistical knowledge. Divided attention had no effect on statistical learning: The acquisition of second-order transitional probabilities was comparable with and without the secondary task. Consolidation was neither affected by divided attention: Statistical knowledge was similarly retained over the 12-h delay, irrespective of the delay activity. Our findings can contribute to a better understanding of the role of attentional processes in and the robustness of visuomotor statistical learning and consolidation.

scholarly journals Tracking transitional probabilities and segmenting auditory sequences are dissociable processes in adults and neonates

2021 ◽  
Author(s):  
Lucas Benjamin ◽  
Ana Fló ◽  
Marie Palu ◽  
Shruti Naik ◽  
Lucia Melloni ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Transition Probabilities ◽  
Statistical Analyses ◽  
Transitional Probability ◽  
Neural Responses ◽  
Prosodic Cues ◽  
Continuous Stream ◽  
Neural Marker ◽  
The Difference ◽  
Transitional Probabilities

Since speech is a continuous stream with no systematic boundaries between words, how do pre-verbal infants manage to discover words? A proposed solution is that they might use the transitional probability between adjacent syllables, which drops at word boundaries. Here, we tested the limits of this mechanism by increasing the size of the word-unit to 4 syllables, and its automaticity by testing asleep neonates. Using markers of statistical learning in neonates' EEG, compared to adult' behavioral performances in the same task, we confirmed that statistical learning is automatic enough to be efficient even in sleeping neonates. But we also revealed that : 1) Successfully tracking transition probabilities in a sequence is not sufficient to segment it 2) Prosodic cues, as subtle as subliminal pauses, enable to recover segmenting capacities 3) Adults' and neonates' capacities are remarkably similar despite the difference of maturation and expertise. Finally, we observed that learning increased the similarity of neural responses across infants, providing a new neural marker to monitor learning. Thus, from birth, infants are equipped with adult-like tools, allowing to extract small coherent word-like units within auditory streams, based on the combination of statistical analyses and prosodic cues.

scholarly journals Statistical learning of unbalanced exclusive-or temporal sequences in humans

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246826
Author(s):  
Laura Lazartigues ◽  
Fabien Mathy ◽  
Frédéric Lavigne
Keyword(s):  
Statistical Learning ◽  
Second Order ◽  
Transitional Probability ◽  
Learning Models ◽  
First Order ◽  
The Third ◽  
Temporal And Spatial ◽  
Exclusive Or ◽  
Transitional Probabilities ◽  
Temporal Sequences

A pervasive issue in statistical learning has been to determine the parameters of regularity extraction. Our hypothesis was that the extraction of transitional probabilities can prevail over frequency if the task involves prediction. Participants were exposed to four repeated sequences of three stimuli (XYZ) with each stimulus corresponding to the position of a red dot on a touch screen that participants were required to touch sequentially. The temporal and spatial structure of the positions corresponded to a serial version of the exclusive-or (XOR) that allowed testing of the respective effect of frequency and first- and second-order transitional probabilities. The XOR allowed the first-order transitional probability to vary while being not completely related to frequency and to vary while the second-order transitional probability was fixed (p(Z|X, Y) = 1). The findings show that first-order transitional probability prevails over frequency to predict the second stimulus from the first and that it also influences the prediction of the third item despite the presence of second-order transitional probability that could have offered a certain prediction of the third item. These results are particularly informative in light of statistical learning models.

Morphological Changes in the Brain of Acutely Ill and Weight-Recovered Patients with Anorexia Nervosa

2014 ◽  
Vol 42 (1) ◽  
pp. 7-18 ◽  
Author(s):  
Jochen Seitz ◽  
Katharina Bühren ◽  
Georg G. von Polier ◽  
Nicole Heussen ◽  
Beate Herpertz-Dahlmann ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Cognitive Deficits ◽  
Time Course ◽  
Morphological Changes ◽  
Meta Analysis ◽  
Short Term ◽  
Clinical Prognosis ◽  
Qualitative Review ◽  
Brain Changes ◽  
The Brain

Objective: Acute anorexia nervosa (AN) leads to reduced gray (GM) and white matter (WM) volume in the brain, which however improves again upon restoration of weight. Yet little is known about the extent and clinical correlates of these brain changes, nor do we know much about the time-course and completeness of their recovery. Methods: We conducted a meta-analysis and a qualitative review of all magnetic resonance imaging studies involving volume analyses of the brain in both acute and recovered AN. Results: We identified structural neuroimaging studies with a total of 214 acute AN patients and 177 weight-recovered AN patients. In acute AN, GM was reduced by 5.6% and WM by 3.8% compared to healthy controls (HC). Short-term weight recovery 2–5 months after admission resulted in restitution of about half of the GM aberrations and almost full WM recovery. After 2–8 years of remission GM and WM were nearly normalized, and differences to HC (GM: –1.0%, WM: –0.7%) were no longer significant, although small residual changes could not be ruled out. In the qualitative review some studies found GM volume loss to be associated with cognitive deficits and clinical prognosis. Conclusions: GM and WM were strongly reduced in acute AN. The completeness of brain volume rehabilitation remained equivocal.

Verbal statistical learning: Prior knowledge impacts word identification but not metacognition

2020 ◽  
Author(s):  
Laetitia Zmuda ◽  
Charlotte Baey ◽  
Paolo Mairano ◽  
Anahita Basirat
Keyword(s):  
Task Performance ◽  
Prior Knowledge ◽  
Statistical Learning ◽  
Cognitive Processes ◽  
Bayesian Approach ◽  
Word Identification ◽  
Artificial Language ◽  
Artificial Languages ◽  
Transitional Probabilities ◽  
Fully Bayesian

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.

scholarly journals Conceptual and Practical Issues in the Pharmacological Treatment of Brain Injury

1993 ◽  
Vol 4 (3) ◽  
pp. 227-237 ◽  
Author(s):  
Donald G. Stein ◽  
Marylou M. Glasier ◽  
Stuart W. Hoffman
Keyword(s):  
Central Nervous System ◽  
Brain Injury ◽  
Time Course ◽  
Chemical Activity ◽  
Behavioral Testing ◽  
Naturally Occurring ◽  
New Information ◽  
Definition Of ◽  
Injury And Repair ◽  
The Brain

It is only within the last ten years that research on treatment for central nervous system (CNS) recovery after injury has become more focused on the complexities involved in promoting recovery from brain injury when the CNS is viewed as an integrated and dynamic system. There have been major advances in research in recovery over the last decade, including new information on the mechanics and genetics of metabolism and chemical activity, the definition of excitotoxic effects and the discovery that the brain itself secretes complex proteins, peptides and hormones which are capable of directly stimulating the repair of damaged neurons or blocking some of the degenerative processes caused by the injury cascade. Many of these agents, plus other nontoxic naturally occurring substances, are being tested as treatment for brain injury. Further work is needed to determine appropriate combinations of treatments and optimum times of administration with respect to the time course of the CNS disorder. In order to understand the mechanisms that mediate traumatic brain injury and repair, there must be a merging of findings from neurochemical studies with data from intensive behavioral testing.

scholarly journals Ceramide and Related-Sphingolipid Levels Are Not Altered in Disease-Associated Brain Regions of APPSLand APPSL/PS1M146LMouse Models of Alzheimer's Disease: Relationship with the Lack of Neurodegeneration?

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Laurence Barrier ◽  
Bernard Fauconneau ◽  
Anastasia Noël ◽  
Sabrina Ingrand
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Animal Models ◽  
Neuronal Death ◽  
Time Course ◽  
Neuronal Loss ◽  
Brain Regions ◽  
App Processing ◽  
Ceramide Accumulation ◽  
The Brain

There is evidence linking sphingolipid abnormalities, APP processing, and neuronal death in Alzheimer's disease (AD). We previously reported a strong elevation of ceramide levels in the brain of the APPSL/PS1Ki mouse model of AD, preceding the neuronal death. To extend these findings, we analyzed ceramide and related-sphingolipid contents in brain from two other mouse models (i.e., APPSLand APPSL/PS1M146L) in which the time-course of pathology is closer to that seen in most currently available models. Conversely to our previous work, ceramides did not accumulate in disease-associated brain regions (cortex and hippocampus) from both models. However, the APPSL/PS1Ki model is unique for its drastic neuronal loss coinciding with strong accumulation of neurotoxic Aβisoforms, not observed in other animal models of AD. Since there are neither neuronal loss nor toxic Aβspecies accumulation in APPSLmice, we hypothesized that it might explain the lack of ceramide accumulation, at least in this model.

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