The neurobiology of uncertainty: implications for statistical learning

Forward and backward blocking in statistical learning

Prediction errors have a prominent role in many forms of learning. For example, in reinforcement learning, agents learn by updating the association between states and outcomes as a function of the prediction error elicited by the event. One paradigm often used to study error-driven learning is blocking. In forward blocking, participants are first presented with stimulus A, followed by outcome X (A→X). In the second phase, A and B are presented together, followed by X (AB→X). Here, A→X blocks the formation of B→X, given that X is already fully predicted by A. In backward blocking, the order of phases is reversed. Here, the association between B and X that is formed during the first learning phase of AB→X is weakened when participants learn exclusively A→X in the second phase. The present study asked the question whether forward and backward blocking occur during visual statistical learning, i.e., the incidental learning of the statistical structure of the environment. In a series of studies, using both forward and backward blocking, we observed statistical learning of temporal associations among pairs of images. While we found no forward blocking, we observed backward blocking, thereby suggesting a retrospective revaluation process in statistical learning and supporting a functional similarity between statistical learning and reinforcement learning.

Virtual-reality-enhanced mannequin to train emergency physicians to examine dizzy patients using the HINTS method

Introduction

Acute vertigo is a frequent chief complaint in the emergency departments, and its efficient management requires thorough training. The HINTS protocol is a valid method to screen patients in the emergency room, but its application in routine is hindered by the lack of training. This study aimed to evaluate the training of emergency physicians for the HINTS method based on a mannequin-based virtual reality simulator (MBVRS).

Methods

We conducted a monocenter, prospective, longitudinal, and randomized cohort study in an Emergency Department at a regional university hospital. We included 34 emergency physicians randomized into two equal groups matched by age and professional experience. The control group attended a theoretical lesson with video demonstrations and the test group received a simulation-based training in addition to the lecture.

Results

We showed that the test group had a higher diagnosis performance for the HINTS method compared to the control group as evaluated by the simulator at 1 month (89% sensitivity versus 45, and 100% specificity versus 86% respectively, p < 001, Fisher's exact test). Evaluation at 6 months showed a similar advantage to the test group.

Discussion

The MBVRS is a useful pedagogic tool for the HINTS protocol in the emergency department. The advantage of a unique training session can be measured up to 6 months after the lesson.

Seeking the neural representation of statistical properties in print during implicit processing of visual words

Statistical learning (SL) plays a key role in literacy acquisition. Studies have increasingly revealed the influence of distributional statistical properties of words on visual word processing, including the effects of word frequency (lexical level) and mappings between orthography, phonology, and semantics (sub-lexical level). However, there has been scant evidence to directly confirm that the statistical properties contained in print can be directly characterized by neural activities. Using time-resolved representational similarity analysis (RSA), the present study examined neural representations of different types of statistical properties in visual word processing. From the perspective of predictive coding, an equal probability sequence with low built-in prediction precision and three oddball sequences with high built-in prediction precision were designed with consistent and three types of inconsistent (orthographically inconsistent, orthography-to-phonology inconsistent, and orthography-to-semantics inconsistent) Chinese characters as visual stimuli. In the three oddball sequences, consistent characters were set as the standard stimuli (probability of occurrence p = 0.75) and three types of inconsistent characters were set as deviant stimuli (p = 0.25), respectively. In the equal probability sequence, the same consistent and inconsistent characters were presented randomly with identical occurrence probability (p = 0.25). Significant neural representation activities of word frequency were observed in the equal probability sequence. By contrast, neural representations of sub-lexical statistics only emerged in oddball sequences where short-term predictions were shaped. These findings reveal that the statistical properties learned from long-term print environment continues to play a role in current word processing mechanisms and these mechanisms can be modulated by short-term predictions.

Flexible structure learning under uncertainty

Experience is known to facilitate our ability to interpret sequences of events and make predictions about the future by extracting temporal regularities in our environments. Here, we ask whether uncertainty in dynamic environments affects our ability to learn predictive structures. We exposed participants to sequences of symbols determined by first-order Markov models and asked them to indicate which symbol they expected to follow each sequence. We introduced uncertainty in this prediction task by manipulating the: (a) probability of symbol co-occurrence, (b) stimulus presentation rate. Further, we manipulated feedback, as it is known to play a key role in resolving uncertainty. Our results demonstrate that increasing the similarity in the probabilities of symbol co-occurrence impaired performance on the prediction task. In contrast, increasing uncertainty in stimulus presentation rate by introducing temporal jitter resulted in participants adopting a strategy closer to probability maximization than matching and improving in the prediction tasks. Next, we show that feedback plays a key role in learning predictive statistics. Trial-by-trial feedback yielded stronger improvement than block feedback or no feedback; that is, participants adopted a strategy closer to probability maximization and showed stronger improvement when trained with trial-by-trial feedback. Further, correlating individual strategy with learning performance showed better performance in structure learning for observers who adopted a strategy closer to maximization. Our results indicate that executive cognitive functions (i.e., selective attention) may account for this individual variability in strategy and structure learning ability. Taken together, our results provide evidence for flexible structure learning; individuals adapt their decision strategy closer to probability maximization, reducing uncertainty in temporal sequences and improving their ability to learn predictive statistics in variable environments.

Delay and Acceleration Threshold of Movement Perception in Patients Suffering from Vertigo or Dizziness

Background: The objective was to evaluate the delay and the acceleration threshold (AT) of movement perception in a population of patients suffering from dizziness and analyze the factors influencing these parameters. Methods: This prospective study included 256 adult subjects: 16 control and 240 patients (5 acute unilateral vestibular loss, 13 compensated unilateral loss, 32 Meniere diseases, 48 persistent postural-perceptual dizziness (PPPD), 95 benign paroxysmal positional vertigo (BPPV), 10 central cases, 19 bilateral vestibulopathy, 14 vestibular migraine, and 4 age-related dizziness). Patients were evaluated for the sound–movement synchronicity perception (maximum delay between the bed oscillation peak and a beep perceived as synchronous, PST) and AT during a pendular movement on a swinging bed. Results: We observed higher PST in women and in senior patients regardless of etiology. AT was higher in senior patients. AT was not influenced by etiology except in patients with bilateral vestibulopathy who had higher thresholds. AT was related to unipodal stance performance, past history of fall, and stop-walking-when-talking test. Conclusions: Delay and acceleration thresholds appear to be coherent with clinical findings and open insights on the exploration of symptoms that cannot be explained by routine otoneurological tests.

Order of statistical learning depends on perceptive uncertainty

Statistical learning (SL) is an innate mechanism by which the brain automatically encodes the n-th order transition probability (TP) of a sequence and grasps the uncertainty of the TP distribution. Through SL, the brain predicts a subsequent event (e _n+1 ) based on the preceding events (e _n ) that have a length of "n". It is now known that uncertainty modulates prediction in top-down processing by the human predictive brain. However, the manner in which the human brain modulates the order of SL strategies based on the degree of uncertainty remains an open question. The present study examined how uncertainty modulates the neural effects of SL and whether differences in uncertainty alter the order of SL strategies. It used auditory sequences in which the uncertainty of sequential information is manipulated based on the conditional entropy. Three sequences with different TP ratios of 90:10, 80:20, and 67:33 were prepared as low-, intermediate, and high-uncertainty sequences, respectively (conditional entropy: 0.47, 0.72, and 0.92 bit, respectively). Neural responses were recorded when the participants listened to the three sequences. The results showed that stimuli with lower TPs elicited a stronger neural response than those with higher TPs, as demonstrated by a number of previous studies. Furthermore, we found that participants adopted higher-order SL strategies in the high uncertainty sequence. These results may indicate that the human brain has an ability to flexibly alter the order based on the uncertainty. This uncertainty may be an important factor that determines the order of SL strategies. Particularly, considering that a higher-order SL strategy mathematically allows the reduction of uncertainty in information, we assumed that the brain may take higher-order SL strategies when encountering high uncertain information in order to reduce the uncertainty. The present study may shed new light on understanding individual differences in SL performance across different uncertain situations.

Attention Shifts to More Complex Structures With Experience

Our environments are saturated with learnable information. What determines which of this information is prioritized for limited attentional resources? Although previous studies suggest that learners prefer medium-complexity information, here we argue that what counts as medium should change as someone learns an input's structure. Specifically, we examined the hypothesis that attention is directed toward more complicated structures as learners gain more experience with the environment. College students watched four simultaneous streams of information that varied in complexity. RTs to intermittent search trials (Experiment 1, N = 75) and eye tracking (Experiment 2, N = 45) indexed where participants attended during the experiment. Using two participant- and trial-specific measures of complexity, we demonstrated that participants attended to increasingly complex streams over time. Individual differences in structure learning also predicted attention allocation, with better learners attending to complex structures earlier in learning, suggesting that the ability to prioritize different information over time is related to learning success.

Rethinking statistical learning as a continuous dynamic stochastic process, from the motor systems perspective

The brain integrates streams of sensory input and builds accurate predictions, while arriving at stable percepts under disparate time scales. This stochastic process bears different unfolding dynamics for different people, yet statistical learning (SL) currently averages out, as noise, individual fluctuations in data streams registered from the brain as the person learns. We here adopt a new analytical approach that instead of averaging out fluctuations in continuous electroencephalographic (EEG)-based data streams, takes these gross data as the important signals. Our new approach reassesses how individuals dynamically learn predictive information in stable and unstable environments. We find neural correlates for two types of learners in a visuomotor task: narrow-variance learners, who retain explicit knowledge of the regularity embedded in the stimuli. They seem to use an error-correction strategy steadily present in both stable and unstable environments. This strategy can be captured by current optimization-based computational frameworks. In contrast, broad-variance learners emerge only in the unstable environment. Local analyses of the moment-by-moment fluctuations, naïve to the overall outcome, reveal an initial period of memoryless learning, well characterized by a continuous gamma process starting out exponentially distributed whereby all future events are equally probable, with high signal (mean) to noise (variance) ratio. The empirically derived continuous Gamma process smoothly converges to predictive Gaussian signatures comparable to those observed for the error-corrective mode that is captured by current optimization-driven computational models. We coin this initially seemingly purposeless stage exploratory. Globally, we examine a posteriori the fluctuations in distributions' shapes over the empirically estimated stochastic signatures. We then confirm that the exploratory mode of those learners, free of expectation, random and memoryless, but with high signal, precedes the acquisition of the error-correction mode boasting smooth transition from exponential to symmetric distributions' shapes. This early naïve phase of the learning process has been overlooked by current models driven by expected, predictive information and error-based learning. Our work demonstrates that (statistical) learning is a highly dynamic and stochastic process, unfolding at different time scales, and evolving distinct learning strategies on demand.

Brain-correlates of processing local dependencies within a statistical learning paradigm

Statistical learning refers to the implicit mechanism of extracting regularities in our environment. Numerous studies have investigated the neural basis of statistical learning. However, how the brain responds to violations of auditory regularities based on prior (implicit) learning requires further investigation. Here, we used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of processing events that are irregular based on learned local dependencies. A stream of consecutive sound triplets was presented. Unbeknown to the subjects, triplets were either (a) standard, namely triplets ending with a high probability sound or, (b) statistical deviants, namely triplets ending with a low probability sound. Participants (n = 33) underwent a learning phase outside the scanner followed by an fMRI session. Processing of statistical deviants activated a set of regions encompassing the superior temporal gyrus bilaterally, the right deep frontal operculum including lateral orbitofrontal cortex, and the right premotor cortex. Our results demonstrate that the violation of local dependencies within a statistical learning paradigm does not only engage sensory processes, but is instead reminiscent of the activation pattern during the processing of local syntactic structures in music and language, reflecting the online adaptations required for predictive coding in the context of statistical learning.

Synthetic Spatial Foraging With Active Inference in a Geocaching Task

Humans are highly proficient in learning about the environments in which they operate. They form flexible spatial representations of their surroundings that can be leveraged with ease during spatial foraging and navigation. To capture these abilities, we present a deep Active Inference model of goal-directed behavior, and the accompanying belief updating. Active Inference rests upon optimizing Bayesian beliefs to maximize model evidence or marginal likelihood. Bayesian beliefs are probability distributions over the causes of observable outcomes. These causes include an agent's actions, which enables one to treat planning as inference. We use simulations of a geocaching task to elucidate the belief updating-that underwrites spatial foraging-and the associated behavioral and neurophysiological responses. In a geocaching task, the aim is to find hidden objects in the environment using spatial coordinates. Here, synthetic agents learn about the environment via inference and learning (e.g., learning about the likelihoods of outcomes given latent states) to reach a target location, and then forage locally to discover the hidden object that offers clues for the next location.

Unpredictability of the “when” influences prediction error processing of the “what” and “where”

The capability to establish accurate predictions is an integral part of learning. Whether predictions about different dimensions of a stimulus interact with each other, and whether such an interaction affects learning, has remained elusive. We conducted a statistical learning study with EEG (electroencephalography), where a stream of consecutive sound triplets was presented with deviants that were either: (a) statistical, depending on the triplet ending probability, (b) physical, due to a change in sound location or (c) double deviants, i.e. a combination of the two. We manipulated the predictability of stimulus-onset by using random stimulus-onset asynchronies. Temporal unpredictability due to random onsets reduced the neurophysiological responses to statistical and location deviants, as indexed by the statistical mismatch negativity (sMMN) and the location MMN. Our results demonstrate that the predictability of one stimulus attribute influences the processing of prediction error signals of other stimulus attributes, and thus also learning of those attributes.

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

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.

Statistical learning 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. This study uses a combination of computational modelling and functional MRI to reveal a fundamental link between online language statistical learning and reinforcement learning at the algorithmic and implementational levels.

Perceptual uncertainty modulates auditory statistical learning: A magnetoencephalography study

Statistical learning allows comprehension of structured information, such as that in language and music. The brain computes a sequence's transition probability and predicts future states to minimise sensory reaction and derive entropy (uncertainty) from sequential information. Neurophysiological studies have revealed that early event-related neural responses (P1 and N1) reflect statistical learning - when the brain encodes transition probability in stimulus sequences, it predicts an upcoming stimulus with a high transition probability and suppresses the early event-related responses to a stimulus with a high transition probability. This amplitude difference between high and low transition probabilities reflects statistical learning effects. However, how a sequence's transition probability ratio affects neural responses contributing to statistical learning effects remains unknown. This study investigated how transition-probability ratios or conditional entropy (uncertainty) in auditory sequences modulate the early event-related neuromagnetic responses of P1m and N1m. Sequence uncertainties were manipulated using three different transition-probability ratios: 90:10%, 80:20%, and 67:33% (conditional entropy: 0.47, 0.72, and 0.92 bits, respectively). Neuromagnetic responses were recorded when participants listened to sequential sounds with these three transition probabilities. Amplitude differences between lower and higher probabilities were larger in sequences with transition-probability ratios of 90:10% and smaller in sequences with those of 67:33%, compared to sequences with those of 80:20%. This suggests that the transition-probability ratio finely tunes P1m and N1m. Our study also showed larger amplitude differences between frequent- and rare-transition stimuli in P1m than in N1m. This indicates that information about transition-probability differences may be calculated in earlier cognitive processes.

The measurement, evolution, and neural representation of action grammars of human behavior

Human behaviors from toolmaking to language are thought to rely on a uniquely evolved capacity for hierarchical action sequencing. Testing this idea will require objective, generalizable methods for measuring the structural complexity of real-world behavior. Here we present a data-driven approach for extracting action grammars from basic ethograms, exemplified with respect to the evolutionarily relevant behavior of stone toolmaking. We analyzed sequences from the experimental replication of ~ 2.5 Mya Oldowan vs. ~ 0.5 Mya Acheulean tools, finding that, while using the same "alphabet" of elementary actions, Acheulean sequences are quantifiably more complex and Oldowan grammars are a subset of Acheulean grammars. We illustrate the utility of our complexity measures by re-analyzing data from an fMRI study of stone toolmaking to identify brain responses to structural complexity. Beyond specific implications regarding the co-evolution of language and technology, this exercise illustrates the general applicability of our method to investigate naturalistic human behavior and cognition.

Early life stress and neural development: Implications for understanding the developmental effects of COVID-19

Chronic and/or extreme stress in childhood, often referred to as early life stress, is associated with a wide range of long-term effects on development. Given this, the COVID-19 pandemic has led to concern about how stress due to the pandemic will affect children's development and mental health. Although early life stress has been linked to altered functioning of a number of neural and biological systems, there is a wide range of variability in children's outcomes. The mechanisms that influence these individual differences are still not well understood. In the past, studies of stress in childhood focused on the type of events that children encountered in their lives. We conducted a review of the literature to formulate a new perspective on the effects of early life stress on development. This new, topological model, may increase understanding of the potential effects of the COVID-19 pandemic on children's development. This model is oriented on children's perceptions of their environment and their social relationships, rather than specific events. These factors influence central and peripheral nervous system development, changing how children interpret, adapt, and respond to potentially stressful events, with implications for children's mental and physical health outcomes.

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

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.

Rethinking Concepts and Categories for Understanding the Neurodevelopmental Effects of Childhood Adversity

Discovering the processes through which early adverse experiences affect children's nervous-system development, health, and behavior is critically important for developing effective interventions. However, advances in our understanding of these processes have been constrained by conceptualizations that rely on categories of adversity that are overlapping, have vague boundaries, and lack consistent biological evidence. Here, we discuss central problems in understanding the link between early-life adversity and children's brain development. We conclude by suggesting alternative formulations that hold promise for advancing knowledge about the neurobiological mechanisms through which adversity affects human development.

Not All Words Are Equally Acquired: Transitional Probabilities and Instructions Affect the Electrophysiological Correlates of Statistical Learning

Statistical learning (SL), the process of extracting regularities from the environment, is a fundamental skill of our cognitive system to structure the world regularly and predictably. SL has been studied using mainly behavioral tasks under implicit conditions and with triplets presenting the same level of difficulty, i.e., a mean transitional probability (TP) of 1.00. Yet, the neural mechanisms underlying SL under other learning conditions remain largely unknown. Here, we investigated the neurofunctional correlates of SL using triplets (i.e., three-syllable nonsense words) with a mean TP of 1.00 (easy "words") and 0.50 (hard "words") in an SL task performed under incidental (implicit) and intentional (explicit) conditions, to determine whether the same core mechanisms were recruited to assist learning. Event-related potentials (ERPs) were recorded while participants listened firstly to a continuous auditory stream made of the concatenation of four easy and four hard "words" under implicit instructions, and subsequently to another auditory stream made of the concatenation of four easy and four hard "words" drawn from another artificial language under explicit instructions. The stream in each of the SL tasks was presented in two consecutive blocks of ~3.5-min each (~7-min in total) to further examine how ERP components might change over time. Behavioral measures of SL were collected after the familiarization phase of each SL task by asking participants to perform a two-alternative forced-choice (2-AFC) task. Results from the 2-AFC tasks revealed a moderate but reliable level of SL, with no differences between conditions. ERPs were, nevertheless, sensitive to the effect of TPs, showing larger amplitudes of N400 for easy "words," as well as to the effect of instructions, with a reduced N250 for "words" presented under explicit conditions. Also, significant differences in the N100 were found as a result of the interaction between TPs, instructions, and the amount of exposure to the auditory stream. Taken together, our findings suggest that triplets' predictability impacts the emergence of "words" representations in the brain both for statistical regularities extracted under incidental and intentional instructions, although the prior knowledge of the "words" seems to favor the recruitment of different SL mechanisms.

Exploring and Exploiting Uncertainty: Statistical Learning Ability Affects How We Learn to Process Language Along Multiple Dimensions of Experience

While the effects of pattern learning on language processing are well known, the way in which pattern learning shapes exploratory behavior has long gone unnoticed. We report on the way in which individual differences in statistical pattern learning affect performance in the domain of language along multiple dimensions. Analyzing data from healthy monolingual adults' performance on a serial reaction time task and a self-paced reading task, we show how individual differences in statistical pattern learning are reflected in readers' knowledge of linguistic co-occurrence patterns and in their exploration and exploitation of content-specific and task-general information. First, we investigated the extent to which an individual's pattern learning correlates with his or her sensitivity to systematic morphological and syntactic co-occurrences, as evidenced while reading authentic sentences. We found that the stream of morphological and syntactic information has a more pronounced effect on the reading speed of, as we will label them, content-sensitive learners in that the more probable the co-occurrence pattern, the faster their reading of that pattern will be. Next, we investigated how differences in pattern learning are reflected in the ways in which individuals approach the reading task itself and adapt to it. Casting this relation in terms of exploration/exploitation strategies, known from Reinforcement Learning, we conclude that content-sensitive learners are also more likely to initially probe (explore) a wider range of directly relevant patterns, which they can later use (exploit) to optimize their reading performance further. By affecting exploratory behavior, pattern learning influences the information that is gathered and becomes available for exploitation, thereby increasing the effect pattern learning has on language cognition.

Statistical learning and the uncertainty of melody and bass line in music

Statistical learning is the ability to learn based on transitional probability (TP) in sequential information, which has been considered to contribute to creativity in music. The interdisciplinary theory of statistical learning examines statistical learning as a mechanism of human learning. This study investigated how TP distribution and conditional entropy in TP of the melody and bass line in music interact with each other, using the highest and lowest pitches in Beethoven’s piano sonatas and Johann Sebastian Bach’s Well-Tempered Clavier. Results for the two composers were similar. First, the results detected specific statistical characteristics that are unique to each melody and bass line as well as general statistical characteristics that are shared between the melody and bass line. Additionally, a correlation of the conditional entropies sampled from the TP distribution could be detected between the melody and bass line. This suggests that the variability of entropies interacts between the melody and bass line. In summary, this study suggested that TP distributions and the entropies of the melody and bass line interact with but are partly independent of each other.

Music predictability and liking enhance pupil dilation and promote motor learning in non-musicians

Humans can anticipate music and derive pleasure from it. Expectations facilitate the learning of movements associated with anticipated events, and they are also linked with reward, which may further facilitate learning of the anticipated rewarding events. The present study investigates the synergistic effects of predictability and hedonic responses to music on arousal and motor-learning in a naïve population. Novel melodies were manipulated in their overall predictability (predictable/unpredictable) as objectively defined by a model of music expectation, and ranked as high/medium/low liked based on participants' self-reports collected during an initial listening session. During this session, we also recorded ocular pupil size as an implicit measure of listeners' arousal. During the following motor task, participants learned to play target notes of the melodies on a keyboard (notes were of similar motor and musical complexity across melodies). Pupil dilation was greater for liked melodies, particularly when predictable. Motor performance was facilitated in predictable rather than unpredictable melodies, but liked melodies were learned even in the unpredictable condition. Low-liked melodies also showed learning but mostly in participants with higher scores of task perceived competence. Taken together, these results highlight  the effects of stimuli predictability on learning, which can be however overshadowed by the effects of stimulus liking or task-related intrinsic motivation.

Communicating Uncertainty: a Narrative Review and Framework for Future Research

Discussing the uncertainty associated with a clinical decision is thought to be a critical element of shared decision-making. Yet, empirical evidence suggests that clinicians rarely communicate clinical uncertainty to patients, and indeed the culture within healthcare environments is often to equate uncertainty with ignorance or failure. Understanding the rationale for discussion of uncertainty along with the current evidence about approaches to communicating and managing uncertainty can advance shared decision-making as well as highlight gaps in evidence. With an increasing focus on personalized healthcare, and advances in genomics and new disease biomarkers, a more sophisticated understanding of how to communicate the limitations and errors that come from applying population-based, epidemiologic findings to predict individuals' futures is going to be essential. This article provides a narrative review of studies relating to the communication of uncertainty, highlighting current strategies together with challenges and barriers, and outlining a framework for future research.

Tonality Tunes the Statistical Characteristics in Music: Computational Approaches on Statistical Learning

Statistical learning is a learning mechanism based on transition probability in sequences such as music and language. Recent computational and neurophysiological studies suggest that the statistical learning contributes to production, action, and musical creativity as well as prediction and perception. The present study investigated how statistical structure interacts with tonalities in music based on various-order statistical models. To verify this in all 24 major and minor keys, the transition probabilities of the sequences containing the highest pitches in Bach's Well-Tempered Clavier, which is a collection of two series (No. 1 and No. 2) of preludes and fugues in all of the 24 major and minor keys, were calculated based on nth-order Markov models. The transition probabilities of each sequence were compared among tonalities (major and minor), two series (No. 1 and No. 2), and music types (prelude and fugue). The differences in statistical characteristics between major and minor keys were detected in lower- but not higher-order models. The results also showed that statistical knowledge in music might be modulated by tonalities and composition periods. Furthermore, the principal component analysis detected the shared components of related keys, suggesting that the tonalities modulate statistical characteristics in music. The present study may suggest that there are at least two types of statistical knowledge in music that are interdependent on and independent of tonality, respectively.

Depth and the Uncertainty of Statistical Knowledge on Musical Creativity Fluctuate Over a Composer's Lifetime

Brain models music as a hierarchy of dynamical systems that encode probability distributions and complexity (i.e., entropy and uncertainty). Through musical experience over lifetime, a human is intrinsically motivated in optimizing the internalized probabilistic model for efficient information processing and the uncertainty resolution, which has been regarded as rewords. Human's behavior, however, appears to be not necessarily directing to efficiency but sometimes act inefficiently in order to explore a maximum rewards of uncertainty resolution. Previous studies suggest that the drive for novelty seeking behavior (high uncertain phenomenon) reflects human's curiosity, and that the curiosity rewards encourage humans to create and learn new regularities. That is to say, although brain generally minimizes uncertainty of music structure, we sometimes derive pleasure from music with uncertain structure due to curiosity for novelty seeking behavior by which we anticipate the resolution of uncertainty. Few studies, however, investigated how curiosity for uncertain and novelty seeking behavior modulates musical creativity. The present study investigated how the probabilistic model and the uncertainty in music fluctuate over a composer's lifetime (all of the 32 piano sonatas by Ludwig van Beethoven). In the late periods of the composer's lifetime, the transitional probabilities (TPs) of sequential patterns that ubiquitously appear in all of his music (familiar phrase) were decreased, whereas the uncertainties of the whole structure were increased. Furthermore, these findings were prominent in higher-, rather than lower-, order models of TP distribution. This may suggest that the higher-order probabilistic model is susceptible to experience and psychological phenomena over the composer's lifetime. The present study first suggested the fluctuation of uncertainty of musical structure over a composer's lifetime. It is suggested that human's curiosity for uncertain and novelty seeking behavior may modulate optimization and creativity in human's brain.

Confidence Predictions Affect Performance Confidence and Neural Preparation in Perceptual Decision Making

Decisions are usually accompanied by a feeling of being wrong or right - a subjective confidence estimate. But what information is this confidence estimate based on, and what is confidence used for? To answer these questions, research has largely focused on confidence regarding current or past decisions, for example identifying how characteristics of the stimulus affect confidence, how confidence can be used as an internally generated feedback signal, and how communicating confidence can affect group decisions. Here, we report two studies which implemented a novel metacognitive measure: predictions of confidence for future perceptual decisions. Using computational modeling of behaviour and EEG, we established that experience-based confidence predictions are one source of information that affects how confident we are in future decision-making, and that learned confidence-expectations affect neural preparation for future decisions. Results from both studies show that participants develop precise confidence predictions informed by past confidence experience. Notably, our results also show that confidence predictions affect performance confidence rated after a decision is made; this finding supports the proposal that confidence judgments are based on multiple sources of information, including expectations. We found strong support for this link in neural correlates of stimulus preparation and processing. EEG measures of preparatory neural activity (contingent negative variation; CNV) and evidence accumulation (centro-parietal positivity; CPP) show that predicted confidence affects neural preparation for stimulus processing, supporting the proposal that one purpose of confidence judgments may be to learn about performance for future encounters and prepare accordingly.

Entropy, Uncertainty, and the Depth of Implicit Knowledge on Musical Creativity: Computational Study of Improvisation in Melody and Rhythm

Recent neurophysiological and computational studies have proposed the hypothesis that our brain automatically codes the nth-order transitional probabilities (TPs) embedded in sequential phenomena such as music and language (i.e., local statistics in nth-order level), grasps the entropy of the TP distribution (i.e., global statistics), and predicts the future state based on the internalized nth-order statistical model. This mechanism is called statistical learning (SL). SL is also believed to contribute to the creativity involved in musical improvisation. The present study examines the interactions among local statistics, global statistics, and different levels of orders (mutual information) in musical improvisation interact. Interactions among local statistics, global statistics, and hierarchy were detected in higher-order SL models of pitches, but not lower-order SL models of pitches or SL models of rhythms. These results suggest that the information-theoretical phenomena of local and global statistics in each order may be reflected in improvisational music. The present study proposes novel methodology to evaluate musical creativity associated with SL based on information theory.

Predictive entrainment of natural speech through two fronto-motor top-down channels

Natural communication between interlocutors is enabled by the ability to predict upcoming speech in a given context. Previously we showed that these predictions rely on a fronto-motor top-down control of low-frequency oscillations in auditory-temporal brain areas that track intelligible speech. However, a comprehensive spatio-temporal characterisation of this effect is still missing. Here, we applied transfer entropy to source-localised MEG data during continuous speech perception. First, at low frequencies (1-4 Hz, brain delta phase to speech delta phase), predictive effects start in left fronto-motor regions and progress to right temporal regions. Second, at higher frequencies (14-18 Hz, brain beta power to speech delta phase), predictive patterns show a transition from left inferior frontal gyrus via left precentral gyrus to left primary auditory areas. Our results suggest a progression of prediction processes from higher-order to early sensory areas in at least two different frequency channels.

Neurophysiological Markers of Statistical Learning in Music and Language: Hierarchy, Entropy, and Uncertainty

Statistical learning (SL) is a method of learning based on the transitional probabilities embedded in sequential phenomena such as music and language. It has been considered an implicit and domain-general mechanism that is innate in the human brain and that functions independently of intention to learn and awareness of what has been learned. SL is an interdisciplinary notion that incorporates information technology, artificial intelligence, musicology, and linguistics, as well as psychology and neuroscience. A body of recent study has suggested that SL can be reflected in neurophysiological responses based on the framework of information theory. This paper reviews a range of work on SL in adults and children that suggests overlapping and independent neural correlations in music and language, and that indicates disability of SL. Furthermore, this article discusses the relationships between the order of transitional probabilities (TPs) (i.e., hierarchy of local statistics) and entropy (i.e., global statistics) regarding SL strategies in human's brains; claims importance of information-theoretical approaches to understand domain-general, higher-order, and global SL covering both real-world music and language; and proposes promising approaches for the application of therapy and pedagogy from various perspectives of psychology, neuroscience, computational studies, musicology, and linguistics.

Cross-modal and non-monotonic representations of statistical regularity are encoded in local neural response patterns

Current neurobiological models assign a central role to predictive processes calibrated to environmental statistics. Neuroimaging studies examining the encoding of stimulus uncertainty have relied almost exclusively on manipulations in which stimuli were presented in a single sensory modality, and further assumed that neural responses vary monotonically with uncertainty. This has left a gap in theoretical development with respect to two core issues: (i) are there cross-modal brain systems that encode input uncertainty in way that generalizes across sensory modalities, and (ii) are there brain systems that track input uncertainty in a non-monotonic fashion? We used multivariate pattern analysis to address these two issues using auditory, visual and audiovisual inputs. We found signatures of cross-modal encoding in frontoparietal, orbitofrontal, and association cortices using a searchlight cross-classification analysis where classifiers trained to discriminate levels of uncertainty in one modality were tested in another modality. Additionally, we found widespread systems encoding uncertainty non-monotonically using classifiers trained to discriminate intermediate levels of uncertainty from both the highest and lowest uncertainty levels. These findings comprise the first comprehensive report of cross-modal and non-monotonic neural sensitivity to statistical regularities in the environment, and suggest that conventional paradigms testing for monotonic responses to uncertainty in a single sensory modality may have limited generalizability.

Towards a theory of individual differences in statistical learning

In recent years, statistical learning (SL) research has seen a growing interest in tracking individual performance in SL tasks, mainly as a predictor of linguistic abilities. We review studies from this line of research and outline three presuppositions underlying the experimental approach they employ: (i) that SL is a unified theoretical construct; (ii) that current SL tasks are interchangeable, and equally valid for assessing SL ability; and (iii) that performance in the standard forced-choice test in the task is a good proxy of SL ability. We argue that these three critical presuppositions are subject to a number of theoretical and empirical issues. First, SL shows patterns of modality- and informational-specificity, suggesting that SL cannot be treated as a unified construct. Second, different SL tasks may tap into separate sub-components of SL that are not necessarily interchangeable. Third, the commonly used forced-choice tests in most SL tasks are subject to inherent limitations and confounds. As a first step, we offer a methodological approach that explicitly spells out a potential set of different SL dimensions, allowing for better transparency in choosing a specific SL task as a predictor of a given linguistic outcome. We then offer possible methodological solutions for better tracking and measuring SL ability. Taken together, these discussions provide a novel theoretical and methodological approach for assessing individual differences in SL, with clear testable predictions.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

Infant Statistical Learning

Perception involves making sense of a dynamic, multimodal environment. In the absence of mechanisms capable of exploiting the statistical patterns in the natural world, infants would face an insurmountable computational problem. Infant statistical learning mechanisms facilitate the detection of structure. These abilities allow the infant to compute across elements in their environmental input, extracting patterns for further processing and subsequent learning. In this selective review, we summarize findings that show that statistical learning is both a broad and flexible mechanism (supporting learning from different modalities across many different content areas) and input specific (shifting computations depending on the type of input and goal of learning). We suggest that statistical learning not only provides a framework for studying language development and object knowledge in constrained laboratory settings, but also allows researchers to tackle real-world problems, such as multilingualism, the role of ever-changing learning environments, and differential developmental trajectories.