Bodily maps of uncertainty and surprise in musical chord progression and the underlying emotional response.

Music has profoundly shaped the human experience across cultures and generations, yet its impact on our minds and bodies remains elusive. This study examined how the perception of musical chord elicits bodily sensations and emotions through the brain's predictive processing. By deploying body-mapping tests and emotional evaluations on 527 participants exposed to chord progressions, we unveiled the intricate interplay between musical uncertainty, prediction error in eliciting specific bodily sensations and emotions. Our results demonstrated that certain chord progressions elicit cardiac and abdominal sensations, linked to interoception, and associated with aesthetic appreciation and positive valence. These findings highlight the crucial role of musical uncertainty and prediction error in emotional response and sound embodiment. This study offers insight into the potential connection between music-induced interoception and mental well-being, underscoring the musical effects on our minds and bodies.

Emergence of integrated behaviors through direct optimization for homeostasis.

Homeostasis is a self-regulatory process, wherein an organism maintains a specific internal physiological state. Homeostatic reinforcement learning (RL) is a framework recently proposed in computational neuroscience to explain animal behavior. Homeostatic RL organizes the behaviors of autonomous embodied agents according to the demands of the internal dynamics of their bodies, coupled with the external environment. Thus, it provides a basis for real-world autonomous agents, such as robots, to continually acquire and learn integrated behaviors for survival. However, prior studies have generally explored problems pertaining to limited size, as the agent must handle observations of such coupled dynamics. To overcome this restriction, we developed an advanced method to realize scaled-up homeostatic RL using deep RL. Furthermore, several rewards for homeostasis have been proposed in the literature. We identified that the reward definition that uses the difference in drive function yields the best results. We created two benchmark environments for homeostasis and performed a behavioral analysis. The analysis showed that the trained agents in each environment changed their behavior based on their internal physiological states. Finally, we extended our method to address vision using deep convolutional neural networks. The analysis of a trained agent revealed that it has visual saliency rooted in the survival environment and internal representations resulting from multimodal input.

Temporal dynamics of statistical learning in children's song contributes to phase entrainment and production of novel information in multiple cultures.

Statistical learning is thought to be linked to brain development. For example, statistical learning of language and music starts at an early age and is shown to play a significant role in acquiring the delta-band rhythm that is essential for language and music learning. However, it remains unclear how auditory cultural differences affect the statistical learning process and the resulting probabilistic and acoustic knowledge acquired through it. This study examined how children's songs are acquired through statistical learning. This study used a Hierarchical Bayesian statistical learning (HBSL) model, mimicking the statistical learning processes of the brain. Using this model, I conducted a simulation experiment to visualize the temporal dynamics of perception and production processes through statistical learning among different cultures. The model learned from a corpus of children's songs in MIDI format, which consists of English, German, Spanish, Japanese, and Korean songs as the training data. In this study, I investigated how the probability distribution of the model is transformed over 15 trials of learning in each song. Furthermore, using the probability distribution of each model over 15 trials of learning each song, new songs were probabilistically generated. The results suggested that, in learning processes, chunking and hierarchical knowledge increased gradually through 15 rounds of statistical learning for each piece of children's songs. In production processes, statistical learning led to the gradual increase of delta-band rhythm (1-3 Hz). Furthermore, by combining the acquired chunks and hierarchy through statistical learning, statistically novel music was generated gradually in comparison to the original songs (i.e. the training songs). These findings were observed consistently, in multiple cultures. The present study indicated that the statistical learning capacity of the brain, in multiple cultures, contributes to the acquisition and generation of delta-band rhythm, which is critical for acquiring language and music. It is suggested that cultural differences may not significantly modulate the statistical learning effects since statistical learning and slower rhythm processing are both essential functions in the human brain across cultures. Furthermore, statistical learning of children's songs leads to the acquisition of hierarchical knowledge and the ability to generate novel music. This study may provide a novel perspective on the developmental origins of creativity and the importance of statistical learning through early development.

Exploring cognitive individuality and the underlying creativity in statistical learning and phase entrainment.

Statistical learning starts at an early age and is intimately linked to brain development and the emergence of individuality. Through such a long period of statistical learning, the brain updates and constructs statistical models, with the model's individuality changing based on the type and degree of stimulation received. However, the detailed mechanisms underlying this process are unknown. This paper argues three main points of statistical learning, including 1) cognitive individuality based on "reliability" of prediction, 2) the construction of information "hierarchy" through chunking, and 3) the acquisition of "1-3Hz rhythm" that is essential for early language and music learning. We developed a Hierarchical Bayesian Statistical Learning (HBSL) model that takes into account both reliability and hierarchy, mimicking the statistical learning processes of the brain. Using this model, we conducted a simulation experiment to visualize the temporal dynamics of perception and production processes through statistical learning. By modulating the sensitivity to sound stimuli, we simulated three cognitive models with different reliability on bottom-up sensory stimuli relative to top-down prior prediction: hypo-sensitive, normal-sensitive, and hyper-sensitive models. We suggested that statistical learning plays a crucial role in the acquisition of 1-3 Hz rhythm. Moreover, a hyper-sensitive model quickly learned the sensory statistics but became fixated on their internal model, making it difficult to generate new information, whereas a hypo-sensitive model has lower learning efficiency but may be more likely to generate new information. Various individual characteristics may not necessarily confer an overall advantage over others, as there may be a trade-off between learning efficiency and the ease of generating new information. This study has the potential to shed light on the heterogeneous nature of statistical learning, as well as the paradoxical phenomenon in which individuals with certain cognitive traits that impede specific types of perceptual abilities exhibit superior performance in creative contexts.

Non-autistic persons modulate their speech rhythm while talking to autistic individuals.

How non-autistic persons modulate their speech rhythm while talking to autistic (AUT) individuals remains unclear. We investigated two types of phonological characteristics: (1) the frequency power of each prosodic, syllabic, and phonetic rhythm and (2) the dynamic interaction among these rhythms using speech between AUT and neurotypical (NT) individuals. Eight adults diagnosed with AUT (all men; age range, 24-44 years) and eight age-matched non-autistic NT adults (three women, five men; age range, 23-45 years) participated in this study. Six NT and eight AUT respondents were asked by one of the two NT questioners (both men) to share their recent experiences on 12 topics. We included 87 samples of AUT-directed speech (from an NT questioner to an AUT respondent), 72 of NT-directed speech (from an NT questioner to an NT respondent), 74 of AUT speech (from an AUT respondent to an NT questioner), and 55 of NT speech (from an NT respondent to an NT questioner). We found similarities between AUT speech and AUT-directed speech, and between NT speech and NT-directed speech. Prosody and interactions between prosodic, syllabic, and phonetic rhythms were significantly weaker in AUT-directed and AUT speech than in NT-directed and NT speech, respectively. AUT speech showed weaker dynamic processing from higher to lower phonological bands (e.g. from prosody to syllable) than NT speech. Further, we found that the weaker the frequency power of prosody in NT and AUT respondents, the weaker the frequency power of prosody in NT questioners. This suggests that NT individuals spontaneously imitate speech rhythms of the NT and AUT interlocutor. Although the speech sample of questioners came from just two NT individuals, our findings may suggest the possibility that the phonological characteristics of a speaker influence those of the interlocutor.

Neural correlates of statistical learning in developmental dyslexia: An electroencephalography study.

The human brain extracts statistical regularities from the surrounding environment in a process called statistical learning. Behavioural evidence suggests that developmental dyslexia affects statistical learning. However, surprisingly few studies have assessed how developmental dyslexia affects the neural processing underlying this type of learning. We used electroencephalography to explore the neural correlates of an important aspect of statistical learning - sensitivity to transitional probabilities - in individuals with developmental dyslexia. Adults diagnosed with developmental dyslexia (n = 17) and controls (n = 19) were exposed to a continuous stream of sound triplets. Every so often, a triplet ending had a low transitional probability given the triplet's first two sounds ("statistical deviants"). Furthermore, every so often a triplet ending was presented from a deviant location ("acoustic deviants"). We examined mismatch negativity elicited by statistical deviants (sMMN), and MMN elicited by location deviants (i.e., acoustic changes). Acoustic deviants elicited a MMN which was larger in the control group than in the developmental dyslexia group. Statistical deviants elicited a small, yet significant, sMMN in the control group, but not in the developmental dyslexia group. However, the difference between the groups was not significant. Our findings indicate that the neural mechanisms underlying pre-attentive acoustic change detection and implicit statistical auditory learning are both affected in developmental dyslexia.

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.

Hierarchical amplitude modulation structures and rhythm patterns: Comparing Western musical genres, song, and nature sounds to Babytalk.

Statistical learning of physical stimulus characteristics is important for the development of cognitive systems like language and music. Rhythm patterns are a core component of both systems, and rhythm is key to language acquisition by infants. Accordingly, the physical stimulus characteristics that yield speech rhythm in "Babytalk" may also describe the hierarchical rhythmic relationships that characterize human music and song. Computational modelling of the amplitude envelope of "Babytalk" (infant-directed speech, IDS) using a demodulation approach (Spectral-Amplitude Modulation Phase Hierarchy model, S-AMPH) can describe these characteristics. S-AMPH modelling of Babytalk has shown previously that bands of amplitude modulations (AMs) at different temporal rates and their phase relations help to create its structured inherent rhythms. Additionally, S-AMPH modelling of children's nursery rhymes shows that different rhythm patterns (trochaic, iambic, dactylic) depend on the phase relations between AM bands centred on ~2 Hz and ~5 Hz. The importance of these AM phase relations was confirmed via a second demodulation approach (PAD, Probabilistic Amplitude Demodulation). Here we apply both S-AMPH and PAD to demodulate the amplitude envelopes of Western musical genres and songs. Quasi-rhythmic and non-human sounds found in nature (birdsong, rain, wind) were utilized for control analyses. We expected that the physical stimulus characteristics in human music and song from an AM perspective would match those of IDS. Given prior speech-based analyses, we also expected that AM cycles derived from the modelling may identify musical units like crotchets, quavers and demi-quavers. Both models revealed an hierarchically-nested AM modulation structure for music and song, but not nature sounds. This AM modulation structure for music and song matched IDS. Both models also generated systematic AM cycles yielding musical units like crotchets and quavers. Both music and language are created by humans and shaped by culture. Acoustic rhythm in IDS and music appears to depend on many of the same physical characteristics, facilitating learning.

Unlocking adults' implicit statistical learning by cognitive depletion.

Human learning is supported by multiple neural mechanisms that maturate at different rates and interact in mostly cooperative but also sometimes competitive ways. We tested the hypothesis that mature cognitive mechanisms constrain implicit statistical learning mechanisms that contribute to early language acquisition. Specifically, we tested the prediction that depleting cognitive control mechanisms in adults enhances their implicit, auditory word-segmentation abilities. Young adults were exposed to continuous streams of syllables that repeated into hidden novel words while watching a silent film. Afterward, learning was measured in a forced-choice test that contrasted hidden words with nonwords. The participants also had to indicate whether they explicitly recalled the word or not in order to dissociate explicit versus implicit knowledge. We additionally measured electroencephalography during exposure to measure neural entrainment to the repeating words. Engagement of the cognitive mechanisms was manipulated by using two methods. In experiment 1 (n = 36), inhibitory theta-burst stimulation (TBS) was applied to the left dorsolateral prefrontal cortex or to a control region. In experiment 2 (n = 60), participants performed a dual working-memory task that induced high or low levels of cognitive fatigue. In both experiments, cognitive depletion enhanced word recognition, especially when participants reported low confidence in remembering the words (i.e., when their knowledge was implicit). TBS additionally modulated neural entrainment to the words and syllables. These findings suggest that cognitive depletion improves the acquisition of linguistic knowledge in adults by unlocking implicit statistical learning mechanisms and support the hypothesis that adult language learning is antagonized by higher cognitive mechanisms.

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.

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.

Musical expertise facilitates statistical learning of rhythm and the perceptive uncertainty: A cross-cultural study.

The brain extracts statistical regularities from sequential information around our environment. This is referred to as statistical learning (SL). Statistical learning is considered an innate function in the human brain and contributes to the brain's development. Within the framework of predictive coding, this learning system allows us to predict a future state to minimize sensory reaction and resolve uncertainty around the world. By auditory statistical learning, over the brain's development, humans become able to comprehend language and music. An increasing number of studies has revealed that Western-classical musical training optimizes the brain's probabilistic model of music and enhances the accuracy of perceptive uncertainty (entropy) in newly encountered melody. No study, however, investigates how musical training modulates the probabilistic model of rhythm, and how the musical culture tunes them. The present study investigated how SL of temporal sequences with and without a beat is reflected in neural responses, and how the SL is modulated by the two types of musical training in different cultures: Western- and Japanese-classical music (i.e., Hougaku). The neural representation showed evidence that the SL effects of beat sequence were prominent in the left hemisphere. This finding was larger in Western- and Japanese-classical musicians compared with non-musicians. Further, the entropy (uncertainty) of the sequences negatively correlated with neural effects of SL, mainly in the left hemisphere of the both Western- and Japanese-classical musicians. These suggest that, regardless of musical culture, musical training may generally facilitate SL of rhythm. However, the specific neural components showed differences between groups of musicians: an earlier component, referred to as P1, represented the left lateralization for perceptive uncertainty in both groups of musicians, whereas a later component, referred to as N1, represented the left lateralization only in Japanese Classical musicians. These findings may suggest that the types of musical training differently modulate neural representation of underlying temporal SL, particularly global processing of uncertainty rather than local processing of transitional probability. The present study sheds new light on the neurophysiological account of Japanese classical music.

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.

Author Correction: When the statistical MMN meets the physical MMN.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

Concurrent Statistical Learning of Ignored and Attended Sound Sequences: An MEG Study.

In an auditory environment, humans are frequently exposed to overlapping sound sequences such as those made by human voices and musical instruments, and we can acquire information embedded in these sequences via attentional and nonattentional accesses. Whether the knowledge acquired by attentional accesses interacts with that acquired by nonattentional accesses is unknown, however. The present study examined how the statistical learning (SL) of two overlapping sound sequences is reflected in neurophysiological and behavioral responses, and how the learning effects are modulated by attention to each sequence. SL in this experimental paradigm was reflected in a neuromagnetic response predominantly in the right hemisphere, and the learning effects were not retained when attention to the tone streams was switched during the learning session. These results suggest that attentional and nonattentional learning scarcely interact with each other and that there may be a specific system for nonattentional learning, which is independent of attentional learning.

When the statistical MMN meets the physical MMN.

How do listeners respond to prediction errors within patterned sequence of sounds? To answer this question we carried out a statistical learning study using electroencephalography (EEG). In a continuous auditory stream of sound triplets the deviations were either (a) statistical, in terms of transitional probability, (b) physical, due to a change in sound location (left or right speaker) or (c) a double deviants, i.e. a combination of the two. Statistical and physical deviants elicited a statistical mismatch negativity and a physical MMN respectively. Most importantly, we found that effects of statistical and physical deviants interacted (the statistical MMN was smaller when co-occurring with a physical deviant). Results show, for the first time, that processing of prediction errors due to statistical learning is affected by prediction errors due to physical deviance. Our findings thus show that the statistical MMN interacts with the physical MMN, implying that prediction error processing due to physical sound attributes suppresses processing of learned statistical properties of sounds.