scholarly journals Linking the neural basis of hierarchical prediction with statistical learning: The paradox of attention

2021 ◽  
Author(s):  
Julie M. Schneider ◽  
Yi-Lun Weng ◽  
Anqi Hu ◽  
Zhenghan Qi
Keyword(s):  
Statistical Learning ◽  
Learning Task ◽  
Prediction Errors ◽  
Neural Basis ◽  
Attentional Shift ◽  
Cortical Responses ◽  
Distributional Information ◽  
Segmentation Task ◽  
Dependent Learning

Statistical learning, the process of tracking distributional information and discovering embedded patterns, is traditionally regarded as a form of implicit learning. However, recent studies proposed that both implicit (attention-independent) and explicit (attention-dependent) learning systems are involved in statistical learning. To understand the role of attention in statistical learning, the current study investigates the cortical processing of prediction errors in speech based on either local or global distributional information. We then ask how these cortical responses relate to statistical learning behavior in a word segmentation task. We found ERP evidence of pre-attentive processing of both the local (mismatching negativity) and global distributional information (late discriminative negativity). However, as speech elements became less frequent and more surprising, some participants showed an involuntary attentional shift, reflected in a P3a response. Individuals who displayed attentive neural tracking of distributional information showed faster learning in a speech statistical learning task. These results provide important neural evidence elucidating the facilitatory role of attention in statistical learning.

scholarly journals Language statistical learning responds to reinforcement learning principles rooted in the striatum

PLoS Biology ◽  
2021 ◽  
Vol 19 (9) ◽  
pp. e3001119
Author(s):  
Joan Orpella ◽  
Ernest Mas-Herrero ◽  
Pablo Ripollés ◽  
Josep Marco-Pallarés ◽  
Ruth de Diego-Balaguer
Keyword(s):  
Reinforcement Learning ◽  
Language Learning ◽  
Statistical Learning ◽  
Dorsal Striatum ◽  
Rule Learning ◽  
Prediction Errors ◽  
Neural Basis ◽  
Structural Rules ◽  
Learning Principles ◽  
Striatal Function

Statistical learning (SL) is the ability to extract regularities from the environment. In the domain of language, this ability is fundamental in the learning of words and structural rules. In lack of reliable online measures, statistical word and rule learning have been primarily investigated using offline (post-familiarization) tests, which gives limited insights into the dynamics of SL and its neural basis. Here, we capitalize on a novel task that tracks the online SL of simple syntactic structures combined with computational modeling to show that online SL responds to reinforcement learning principles rooted in striatal function. Specifically, we demonstrate—on 2 different cohorts—that a temporal difference model, which relies on prediction errors, accounts for participants’ online learning behavior. We then show that the trial-by-trial development of predictions through learning strongly correlates with activity in both ventral and dorsal striatum. Our results thus provide a detailed mechanistic account of language-related SL and an explanation for the oft-cited implication of the striatum in SL tasks. This work, therefore, bridges the long-standing gap between language learning and reinforcement learning phenomena.

scholarly journals Statistical learning in domestic chicks is modulated by strain and sex

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Chiara Santolin ◽  
Orsola Rosa-Salva ◽  
Bastien S. Lemaire ◽  
Lucia Regolin ◽  
Giorgio Vallortigara
Keyword(s):  
Sex Differences ◽  
Statistical Learning ◽  
Comparative Research ◽  
Learning Task ◽  
Human Infants ◽  
Sensory Inputs ◽  
Automated Tracking ◽  
Domestic Chicks ◽  
Continuous Stream

Abstract Statistical learning is a key mechanism for detecting regularities from a variety of sensory inputs. Precocial newborn domestic chicks provide an excellent model for (1) exploring unsupervised forms of statistical learning in a comparative perspective, and (2) elucidating the ecological function of statistical learning using imprinting procedures. Here we investigated the role of the sex of the chicks in modulating the direction of preference (for familiarity or novelty) in a visual statistical learning task already employed with chicks and human infants. Using both automated tracking and direct human coding, we confirmed chicks’ capacity to recognize the presence of a statistically defined structure underlying a continuous stream of shapes. Using a different chicken strain than previous studies, we were also able to highlight sex differences in chicks’ propensity to approach the familiar or novel sequence. This could also explain a previous failure to reveal statistical learning in chicks which sex was however not determined. Our study confirms chicks’ ability to track visual statistics. The pivotal role of sex in determining familiarity or novelty preferences in this species and the interaction with the animals’ strain highlight the importance to contextualize comparative research within the ecology of each species.

scholarly journals What drives individual differences in statistical learning? The role of perceptual fluency and familiarity

2018 ◽  
Author(s):  
Amy Perfors ◽  
Evan Kidd
Keyword(s):  
Individual Differences ◽  
Statistical Learning ◽  
Learning Task ◽  
Perceptual Fluency ◽  
Learning Performance ◽  
Visual Modality ◽  
Learning Skills ◽  
Domain Specific ◽  
Different Levels

Humans have the ability to learn surprisingly complicated statistical information in a variety of modalities and situations, often based on relatively little input. These statistical learning (SL) skills appear to underlie many kinds of learning, but despite their ubiquity, we still do not fully understand precisely what SL is and what individual differences on SL tasks reflect. Here we present experimental work suggesting that at least some individual differences arise from variation in perceptual fluency — the ability to rapidly or efficiently code and remember the stimuli that statistical learning occurs over. We show that performance on a standard SL task varies substantially within the same (visual) modality as a function of whether the stimuli involved are familiar or not, independent of stimulus complexity. Moreover, we find that test-retest correlations of performance in a statistical learning task using stimuli of the same level of familiarity (but distinct items) are stronger than correlations across the same task with different levels of familiarity. Finally, we demonstrate that statistical learning performance is predicted by an independent measure of stimulus-specific perceptual fluency which contains no statistical learning component at all. Our results suggest that a key component of SL performance may be unrelated to either domain-specific statistical learning skills or modality-specific perceptual processing.

scholarly journals Electrophysiological signatures of hierarchical learning

2021 ◽  
Author(s):  
Meng Liu ◽  
Wenshan Dong ◽  
Shaozheng Qin ◽  
Tom Verguts ◽  
Qi Chen
Keyword(s):  
Cognitive Process ◽  
Human Perception ◽  
Learning Task ◽  
Prediction Errors ◽  
Neural Basis ◽  
Hierarchical Learning ◽  
Low Level ◽  
High Level ◽  
The Brain ◽  
Better Than

AbstractHuman perception and learning is thought to rely on a hierarchical generative model that is continuously updated via precision-weighted prediction errors (pwPEs). However, the neural basis of such cognitive process and how it unfolds during decision making, remain poorly understood. To investigate this question, we combined a hierarchical Bayesian model (i.e., Hierarchical Gaussian Filter, HGF) with electrophysiological (EEG) recording, while participants performed a probabilistic reversal learning task in alternatingly stable and volatile environments. Behaviorally, the HGF fitted significantly better than two control, non-hierarchical, models. Neurally, low-level and high-level pwPEs were independently encoded by the P300 component. Low-level pwPEs were reflected in the theta (4-8 Hz) frequency band, but high-level pwPEs were not. Furthermore, the expressions of high-level pwPEs were stronger for participants with better HGF fit. These results indicate that the brain employs hierarchical learning, and encodes both low- and high-level learning signals separately and adaptively.

scholarly journals Statistical learning as reinforcement learning phenomena

2021 ◽  
Author(s):  
J Orpella ◽  
E Mas-Herrero ◽  
P Ripollés ◽  
J Marco-Pallarés ◽  
R de Diego-Balaguer
Keyword(s):  
Reinforcement Learning ◽  
Language Learning ◽  
Statistical Learning ◽  
Computational Modelling ◽  
Dorsal Striatum ◽  
Rule Learning ◽  
Prediction Errors ◽  
Neural Basis ◽  
Structural Rules ◽  
Learning Principles

AbstractStatistical learning (SL) is the ability to extract regularities from the environment. In the domain of language, this ability is fundamental in the learning of words and structural rules. In lack of reliable online measures, statistical word and rule learning have been primarily investigated using offline (post-familiarization) tests, which gives limited insights into the dynamics of SL and its neural basis. Here, we capitalize on a novel task that tracks the online statistical learning of language rules combined with computational modelling to show that online SL responds to reinforcement learning principles rooted in striatal function. Specifically, we demonstrate - on two different cohorts - that a Temporal Difference model, which relies on prediction errors, accounts for participants’ online learning behavior. We then show that the trial-by-trial development of predictions through learning strongly correlates with activity in both ventral and dorsal striatum. Our results thus provide a detailed mechanistic account of language-related SL and an explanation for the oft-cited implication of the striatum in SL tasks. This work, therefore, bridges the longstanding gap between language learning and reinforcement learning phenomena.

scholarly journals What drives individual differences in statistical learning? The role of perceptual fluency and familiarity

2020 ◽  
Author(s):  
Andrew Perfors ◽  
Evan Kidd
Keyword(s):  
Individual Differences ◽  
Statistical Learning ◽  
Learning Task ◽  
Perceptual Processing ◽  
Perceptual Fluency ◽  
Learning Performance ◽  
Visual Modality ◽  
Stimulus Familiarity ◽  
Different Levels

Humans have the ability to learn surprisingly complicated statistical information in avariety of modalities and situations, often based on relatively little input. These statistical learning (SL) skills appear to underlie many kinds of learning, but despite their ubiquity, we still do not fully understand precisely what SL is and what individual differences on SL tasks reflect. Here we present experimental work suggesting that at least some individual differences arise from variation in perceptual fluency — the ability to rapidly or efficiently code and remember the stimuli that statistical learning occurs over – and that perceptual fluency is driven at least in part by stimulus familiarity: performance on a standard SL task varies substantially within the same (visual) modality as a function of whether the stimuli involved are familiar or not, independent of stimulus complexity. Moreover, we find that test-retest correlations of performance in a statistical learning task using stimuli of the same level of familiarity (but distinct items) are stronger than correlations across the same task with stimuli of different levels of familiarity. Finally, we demonstrate that statistical learning performance is predicted by an independent measure of stimulus-specific perceptual fluency that contains no statistical learning component at all. Our results suggest that a key component of statistical learning performance may be related to stimulus-specific perceptual processing and familiarity.

scholarly journals Precision weighting of cortical unsigned prediction errors is mediated by dopamine and benefits learning

2018 ◽  
Author(s):  
J. Haarsma ◽  
P.C. Fletcher ◽  
H. Ziauddeen ◽  
T.J. Spencer ◽  
K.M.J. Diederen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Predictive Coding ◽  
Learning Task ◽  
Anterior Cingulate ◽  
Dopamine Antagonist ◽  
Prediction Errors ◽  
Data Set ◽  
Health And Illness ◽  
Hierarchical Bayesian Inference

AbstractThe predictive coding framework construes the brain as performing a specific form of hierarchical Bayesian inference. In this framework the precision of cortical unsigned prediction error (surprise) signals is proposed to play a key role in learning and decision-making, and to be controlled by dopamine. To test this hypothesis, we re-analysed an existing data-set from healthy individuals who received a dopamine agonist, antagonist or placebo and who performed an associative learning task under different levels of outcome precision. Computational reinforcement-learning modelling of behaviour provided support for precision-weighting of unsigned prediction errors. Functional MRI revealed coding of unsigned prediction errors relative to their precision in bilateral superior frontal gyri and dorsal anterior cingulate. Cortical precision-weighting was (i) perturbed by the dopamine antagonist sulpiride, and (ii) associated with task performance. These findings have important implications for understanding the role of dopamine in reinforcement learning and predictive coding in health and illness.

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