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

Abstract

a b s t r a c tThe 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 0.5 s 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

“…As a result, the differences in the amplitude and latency of the responses to tones between higher and lower transitional probabilities could occur during statistical learning. In previous studies, such differences could be detected in P1m (Paraskevopoulos et al, 2012;Daikoku et al, 2016) and N1 responses (Abla et al, 2008) when participants listened to a continuous concatenation of tone words. Because tone transitions between words were more variable than within words, the amplitudes for the final tone within words were significantly lower than those for the initial tone, after learning word borders (i.e., word segmentation).…”

“…In addition, these statistical learning effects reflected in neural responses could be observed in both the ERPs and the magnetic counterparts of ERPs. This neural effect could be observed in the responses at approximately 50 ms (i.e., P1/P1m) (Paraskevopoulos et al, 2012;Daikoku et al, 2016), 100 ms (i.e., N1/N1m) (Abla et al, 2008;Furl et al, 2011;Daikoku et al, 2014Daikoku et al, , 2015Koelsch et al, 2016), and 200 ms (i.e., P2/P2m) (Furl et al, 2011) after stimulus onset.…”

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

“…However, a previous study suggests that, based on the implicitness of statistical learning in humans, we cannot delineate learned statistical knowledge, even when we tried to “intentionally” learn statistical knowledge 13 . Although humans are unaware of the learned statistical structure of auditory sequences, they can, instead, recognize sequences with at least nine tones transitioned with high probability as familiar tone sequences 13 , 14 , 16 , 17 . Thus, statistical knowledge itself is implicit and does not reach the level of explicit awareness, whereas it can be alternatively expressed via an abstract medium such as a musical melody.…”

“…Using a Markov chain 11 , 13 , 14 , 16 , 17 or a word segmentation task 10 , 12 , 15 , 17 , previous studies demonstrated that auditory statistical learning could be reflected in auditory event-related responses, such as P1/P1 m 12 , 16 , N1/N1 m or mismatch negativity/mismatch field 10 – 15 , 17 , and P2/P2 m 11 , peaking in a latency range from 50 to 200 ms after stimulus onset. Some studies suggest that the learning effect relationship with P1 m involves music expertise and specialised training experience 12 , 16 . When pure tone sequences were presented, learning effects on P1 m, but not N1 m, were larger in musicians compared with non-musicians 12 .…”

Physical fitness modulates incidental but not intentional statistical learning of simultaneous auditory sequences during concurrent physical exercise

“…In other words, improvement in physical fitness could increase neural resources necessary to perform statistical learning as well as physical exercise. Incidental statistical learning is considered to be a domain-general and implicit learning process innate to humans, regardless of learner's ages [1,2], suggesting that we might constantly and unconsciously perform statistical learning of sequential stimuli such as language and music [34,35]. Some researchers reported that dyslexia is difficult to perform statistical learning compared to healthy learners [36,37].…”