Tatsuya Daikoku

Implicit and explicit statistical learning of tone sequences across spectral shifts

We investigated how the statistical learning of auditory sequences is reflected in neuromagnetic responses in implicit and explicit learning conditions. Complex tones with fundamental frequencies (F0s) in a five-tone equal temperament were generated by a formant synthesizer. The tones were subsequently ordered with the constraint that the probability of the forthcoming tone was statistically defined (80% for one tone; 5% for the other four) by the latest two successive tones (second-order Markov chains). The tone sequence consisted of 500 tones and 250 successive tones with a relative shift of F0s based on the same Markov transitional matrix. In explicit and implicit learning conditions, neuromagnetic responses to the tone sequence were recorded from fourteen right-handed participants. The temporal profiles of the N1m responses to the tones with higher and lower transitional probabilities were compared. In the explicit learning condition, the N1m responses to tones with higher transitional probability were significantly decreased compared with responses to tones with lower transitional probability in the latter half of the 500-tone sequence. Furthermore, this difference was retained even after the F0s were relatively shifted. In the implicit learning condition, N1m responses to tones with higher transitional probability were significantly decreased only for the 250 tones following the relative shift of F0s. The delayed detection of learning effects across the sound-spectral shift in the implicit condition may imply that learning may progress earlier in explicit learning conditions than in implicit learning conditions. The finding that the learning effects were retained across spectral shifts regardless of the learning modality indicates that relative pitch processing may be an essential ability for humans.

Statistical learning of music- and language-like sequences and tolerance for spectral shifts

In our previous study (Daikoku, Yatomi, & Yumoto, 2014), we demonstrated that the N1m response could be a marker for the statistical learning process of pitch sequence, in which each tone was ordered by a Markov stochastic model. The aim of the present study was to investigate how the statistical learning of music- and language-like auditory sequences is reflected in the N1m responses based on the assumption that both language and music share domain generality. By using vowel sounds generated by a formant synthesizer, we devised music- and language-like auditory sequences in which higher-ordered transitional rules were embedded according to a Markov stochastic model by controlling fundamental (F0) and/or formant frequencies (F1-F2). In each sequence, F0 and/or F1-F2 were spectrally shifted in the last one-third of the tone sequence. Neuromagnetic responses to the tone sequences were recorded from 14 right-handed normal volunteers. In the music- and language-like sequences with pitch change, the N1m responses to the tones that appeared with higher transitional probability were significantly decreased compared with the responses to the tones that appeared with lower transitional probability within the first two-thirds of each sequence. Moreover, the amplitude difference was even retained within the last one-third of the sequence after the spectral shifts. However, in the language-like sequence without pitch change, no significant difference could be detected. The pitch change may facilitate the statistical learning in language and music. Statistically acquired knowledge may be appropriated to process altered auditory sequences with spectral shifts. The relative processing of spectral sequences may be a domain-general auditory mechanism that is innate to humans.

Pitch-class distribution modulates the statistical learning of atonal chord sequences

The present study investigated whether neural responses could demonstrate the statistical learning of chord sequences and how the perception underlying a pitch class can affect the statistical learning of chord sequences. Neuromagnetic responses to two chord sequences of augmented triads that were presented every 0.5s were recorded from fourteen right-handed participants. One sequence was a series of 360 chord triplets, each of which consisted of three chords in the same pitch class (clustered pitch-classes sequences). The other sequence was a series of 360 chord triplets, each of which consisted of three chords in different pitch classes (dispersed pitch-classes sequences). The order of the triplets was constrained by a first-order Markov stochastic model such that a forthcoming triplet was statistically defined by the most recent triplet (80% for one; 20% for the other two). We performed a repeated-measures ANOVA with the peak amplitude and latency of the P1m, N1m and P2m. In the clustered pitch-classes sequences, the P1m responses to the triplets that appeared with higher transitional probability were significantly reduced compared with those with lower transitional probability, whereas no significant result was detected in the dispersed pitch-classes sequences. Neuromagnetic significance was concordant with the results of familiarity interviews conducted after each learning session. The P1m response is a useful index for the statistical learning of chord sequences. Domain-specific perception based on the pitch class may facilitate the domain-general statistical learning of chord sequences.

Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering

ABSTRACT Previous neural studies have supported the hypothesis that statistical learning mechanisms are used broadly across different domains such as language and music. However, these studies have only investigated a single aspect of statistical learning at a time, such as recognizing word boundaries or learning word order patterns. In this study, we neutrally investigated how the two levels of statistical learning for recognizing word boundaries and word ordering could be reflected in neuromagnetic responses and how acquired statistical knowledge is reorganised when the syntactic rules are revised. Neuromagnetic responses to the Japanese‐vowel sequence (a, e, i, o, and u), presented every .45 s, were recorded from 14 right‐handed Japanese participants. The vowel order was constrained by a Markov stochastic model such that five nonsense words (aue, eao, iea, oiu, and uoi) were chained with an either‐or rule: the probability of the forthcoming word was statistically defined (80% for one word; 20% for the other word) by the most recent two words. All of the word transition probabilities (80% and 20%) were switched in the middle of the sequence. In the first and second quarters of the sequence, the neuromagnetic responses to the words that appeared with higher transitional probability were significantly reduced compared with those that appeared with a lower transitional probability. After switching the word transition probabilities, the response reduction was replicated in the last quarter of the sequence. The responses to the final vowels in the words were significantly reduced compared with those to the initial vowels in the last quarter of the sequence. The results suggest that both within‐word and between‐word statistical learning are reflected in neural responses. The present study supports the hypothesis that listeners learn larger structures such as phrases first, and they subsequently extract smaller structures, such as words, from the learned phrases. The present study provides the first neurophysiological evidence that the correction of statistical knowledge requires more time than the acquisition of new statistical knowledge. HIGHLIGHTSP1m and N1m can be neurophysiological probes for statistical learning.The correction of knowledge requires more time than the acquisition of new knowledge.Statistical learning contributes to recognition of word boundaries and word ordering.Listeners learn larger structures first and subsequently extract smaller structures.

Single, but not dual, attention facilitates statistical learning of two concurrent auditory sequences

When we are exposed to a novel stimulus sequence, we can learn the sequence by extracting a statistical structure that is potentially embedded in the sequence. This mechanism is called statistical learning, and is considered a fundamental and domain-general process that is innate in humans. In the real-world environment, humans are inevitably exposed to auditory sequences that often overlap with one another, such as speech sound streams from multiple speakers or entangled melody lines generated by multiple instruments. The present study investigated how single and dual attention modulates brain activity, reflecting statistical learning when two auditory sequences were presented simultaneously. The results demonstrated that the effect of statistical learning had more pronounced neural activity when listeners paid attention to only one sequence and ignored the other, rather than paying attention to both sequences. Biased attention may thus be an essential strategy when learners are exposed to multiple information streams.

Motor Reproduction of Time Interval Depends on Internal Temporal Cues in the Brain: Sensorimotor Imagery in Rhythm

How the human brain perceives time intervals is a fascinating topic that has been explored in many fields of study. This study examined how time intervals are replicated in three conditions: with no internalized cue (PT), with an internalized cue without a beat (AS), and with an internalized cue with a beat (RS). In PT, participants accurately reproduced the time intervals up to approximately 3 s. Over 3 s, however, the reproduction errors became increasingly negative. In RS, longer presentations of over 5.6 s and 13 beats induced accurate time intervals in reproductions. This suggests longer exposure to beat presentation leads to stable internalization and efficiency in the sensorimotor processing of perception and reproduction. In AS, up to approximately 3 s, the results were similar to those of RS whereas over 3 s, the results shifted and became similar to those of PT. The time intervals between the first two stimuli indicate that the strategies of time-interval reproduction in AS may shift from RS to PT. Neural basis underlying the reproduction of time intervals without a beat may depend on length of time interval between adjacent stimuli in sequences.