How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning

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.

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

10.1101/2020.10.25.353375 ◽

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.

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

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01182 ◽

2017 ◽

Vol 29 (12) ◽

pp. 1963-1976 ◽

Cited By ~ 6

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.

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The neurobiology of uncertainty: implications for statistical learning

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2016.0048 ◽

2017 ◽

Vol 372 (1711) ◽

pp. 20160048 ◽

Cited By ~ 30

Author(s):

Uri Hasson

Keyword(s):

Statistical Learning ◽

Historical Development ◽

Visual Stimuli ◽

Learning Mechanisms ◽

Cognitive Sciences ◽

Input Uncertainty ◽

Environmental Features ◽

Online Prediction ◽

Neurobiological Research ◽

The Brain

The capacity for assessing the degree of uncertainty in the environment relies on estimating statistics of temporally unfolding inputs. This, in turn, allows calibration of predictive and bottom-up processing, and signalling changes in temporally unfolding environmental features. In the last decade, several studies have examined how the brain codes for and responds to input uncertainty. Initial neurobiological experiments implicated frontoparietal and hippocampal systems, based largely on paradigms that manipulated distributional features of visual stimuli. However, later work in the auditory domain pointed to different systems, whose activation profiles have interesting implications for computational and neurobiological models of statistical learning (SL). This review begins by briefly recapping the historical development of ideas pertaining to the sensitivity to uncertainty in temporally unfolding inputs. It then discusses several issues at the interface of studies of uncertainty and SL. Following, it presents several current treatments of the neurobiology of uncertainty and reviews recent findings that point to principles that serve as important constraints on future neurobiological theories of uncertainty, and relatedly, SL. This review suggests it may be useful to establish closer links between neurobiological research on uncertainty and SL, considering particularly mechanisms sensitive to local and global structure in inputs, the degree of input uncertainty, the complexity of the system generating the input, learning mechanisms that operate on different temporal scales and the use of learnt information for online prediction. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’.

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Statistical prediction of the future impairs episodic encoding of the present

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2013291117 ◽

2020 ◽

Vol 117 (37) ◽

pp. 22760-22770 ◽

Cited By ~ 4

Author(s):

Brynn E. Sherman ◽

Nicholas B. Turk-Browne

Keyword(s):

Episodic Memory ◽

Predictive Model ◽

Statistical Learning ◽

Statistical Prediction ◽

Behavioral Tests ◽

The Past ◽

The Future ◽

Episodic Encoding ◽

Past Experiences ◽

The Brain

Memory is typically thought of as enabling reminiscence about past experiences. However, memory also informs and guides processing of future experiences. These two functions of memory are often at odds: Remembering specific experiences from the past requires storing idiosyncratic properties that define particular moments in space and time, but by definition such properties will not be shared with similar situations in the future and thus may not be applicable to future situations. We discovered that, when faced with this conflict, the brain prioritizes prediction over encoding. Behavioral tests of recognition and source recall showed that items allowing for prediction of what will appear next based on learned regularities were less likely to be encoded into memory. Brain imaging revealed that the hippocampus was responsible for this interference between statistical learning and episodic memory. The more that the hippocampus predicted the category of an upcoming item, the worse the current item was encoded. This competition may serve an adaptive purpose, focusing encoding on experiences for which we do not yet have a predictive model.

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Neural Mechanisms of Memory Enhancement and Impairment Induced by Visual Statistical Learning

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01589 ◽

2020 ◽

Vol 32 (9) ◽

pp. 1749-1763

Author(s):

Sachio Otsuka ◽

Jun Saiki

Keyword(s):

Statistical Learning ◽

Superior Temporal Gyrus ◽

Brain Regions ◽

Neural Mechanisms ◽

Fmri Data ◽

Precentral Gyrus ◽

Memory Enhancement ◽

Visual Statistical Learning ◽

Mechanisms Of Memory ◽

The Brain

Prior research has reported that the medial temporal, parietal, and frontal brain regions are associated with visual statistical learning (VSL). However, the neural mechanisms involved in both memory enhancement and impairment induced by VSL remain unknown. In this study, we examined this issue using event-related fMRI. fMRI data from the familiarization scan showed a difference in the activation level of the superior frontal gyrus (SFG) between structured triplets, where three objects appeared in the same order, and pseudorandom triplets. More importantly, the precentral gyrus and paracentral lobule responded more strongly to Old Turkic letters inserted into the structured triplets than to those inserted into the random triplets, at the end of the familiarization scan. Furthermore, fMRI data from the recognition memory test scan, where participants were asked to decide whether the objects or letters shown were old (presented during familiarization scan) or new, indicated that the middle frontal gyrus and SFG responded more strongly to objects from the structured triplets than to those from the random triplets, which overlapped with the brain regions associated with VSL. In contrast, the response of the lingual gyrus, superior temporal gyrus, and cuneus was weaker to letters inserted into the structured triplets than to those inserted into the random triplets, which did not overlap with the brain regions associated with observing the letters during the familiarization scan. These findings suggest that different brain regions are involved in memory enhancement and impairment induced by VSL.

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

npj Science of Learning ◽

10.1038/s41539-021-00093-9 ◽

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.

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An expanding manifold in transmodal regions characterizes adolescent reconfiguration of structural connectome organization

eLife ◽

10.7554/elife.64694 ◽

2021 ◽

Vol 10 ◽

Author(s):

Bo-yong Park ◽

Richard AI Bethlehem ◽

Casey Paquola ◽

Sara Larivière ◽

Raul Rodríguez-Cruces ◽

...

Keyword(s):

Statistical Learning ◽

Adolescent Development ◽

Critical Time ◽

Cognitive Outcomes ◽

High Dimensional ◽

Structural Connectome ◽

Maturational Change ◽

Connectivity Patterns ◽

The Brain

Adolescence is a critical time for the continued maturation of brain networks. Here, we assessed structural connectome development in a large longitudinal sample ranging from childhood to young adulthood. By projecting high-dimensional connectomes into compact manifold spaces, we identified a marked expansion of structural connectomes with the strongest effects in transmodal regions during adolescence. Findings reflected increased within-module connectivity together with increased segregation, indicating increasing differentiation of higher-order association networks from the rest of the brain. Projection of subcortico-cortical connectivity patterns into these manifolds showed parallel alterations in pathways centered on the caudate and thalamus. Connectome findings were contextualized via spatial transcriptome association analysis, highlighting genes enriched in cortex, thalamus, and striatum. Statistical learning of cortical and subcortical manifold features at baseline and their maturational change predicted measures of intelligence at follow-up. Our findings demonstrate that connectome manifold learning can bridge the conceptual and empirical gaps between macroscale network reconfigurations, microscale processes, and cognitive outcomes in adolescent development.

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Statistical prediction of the future impairs episodic encoding of the present

10.1101/851147 ◽

2019 ◽

Cited By ~ 1

Author(s):

Brynn E. Sherman ◽

Nicholas B. Turk-Browne

Keyword(s):

Episodic Memory ◽

Predictive Model ◽

Statistical Learning ◽

Statistical Prediction ◽

Behavioral Tests ◽

The Past ◽

The Future ◽

Episodic Encoding ◽

Past Experiences ◽

The Brain

AbstractMemory is typically thought of as enabling reminiscence about past experiences. However, memory also informs and guides processing of future experiences. These two functions of memory are often at odds: remembering specific experiences from the past requires storing idiosyncratic properties that define particular moments in space and time, but by definition such properties will not be shared with similar situations in the future and thus may not be applicable to future situations. We discovered that, when faced with this conflict, the brain prioritizes prediction over encoding. Behavioral tests of recognition and source recall showed that items allowing for prediction of what will appear next based on learned regularities were less likely to be encoded into memory. Brain imaging revealed that the hippocampus was responsible for this interference between statistical learning and episodic memory. The more that the hippocampus predicted the category of an upcoming item, the worse the current item was encoded. This competition may serve an adaptive purpose, focusing encoding on experiences for which we do not yet have a predictive model.

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Remembering the pattern: A longitudinal case study on statistical learning in spatial navigation and memory consolidation

10.1101/2021.10.18.464818 ◽

2021 ◽

Author(s):

Kathryn Nicole Graves ◽

Brynn Elizabeth Sherman ◽

David Huberdeau ◽

Eyiyemisi Damisah ◽

Imran Habib Quraishi ◽

...

Keyword(s):

Statistical Learning ◽

Spatial Navigation ◽

Longitudinal Case Study ◽

Rapid Acquisition ◽

Initial Work ◽

Prolonged Retention ◽

Cortical Regions ◽

Intracranial Electroencephalography ◽

The Brain

Distinct brain systems are thought to support statistical learning over different timescales. Regularities encountered during online perceptual experience can be acquired rapidly by the hippocampus. Further processing during offline consolidation can establish these regularities gradually in cortical regions, including the medial prefrontal cortex (mPFC). These mechanisms of statistical learning may be critical during spatial navigation, for which knowledge of the structure of an environment can facilitate future behavior. Rapid acquisition and prolonged retention of regularities have been investigated in isolation, but how they interact in the context of spatial navigation is unknown. We had the rare opportunity to study the brain systems underlying both rapid and gradual timescales of statistical learning using intracranial electroencephalography (iEEG) longitudinally in the same patient over a period of three weeks. As hypothesized, spatial patterns were represented in the hippocampus but not mPFC for up to one week after statistical learning and then represented in the mPFC but not hippocampus two and three weeks after statistical learning. Taken together, these findings clarify that the hippocampus may do the initial work of extracting regularities and transfer these integrated memories to cortex, rather than only storing individual experiences and leaving it up to cortex to extract regularities.

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