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

Abstract

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

“…There is considerable evidence that the reproduction of a time interval depends significantly on several factors, including stimuli, age, gender, learning abilities, and cognitive function ( Szelag, 1997 ; Szelag et al, 1998 , 2002 , 2004a , b ; Kanabus et al, 2004 ). The cerebral cortex is a learning machine that can work regardless of attention and domain of learning ( Daikoku et al, 2012 , 2014 , 2015 , 2016 , 2017a , b , c ; Koelsch et al, 2016 ; Daikoku and Yumoto, 2017 ; Daikoku, 2018 ), and that can be developed by cognitive and motor learning throughout life ( Merzenich et al, 1996 ). Szelag et al (1998) suggested that the prefrontal cortex, which is responsible for the developmental effect, also plays an important role in temporal processing, and is involved in working memory up to around 3 s. Previous studies report that males and older individuals tend to outperform women and younger individuals on perception tests related to temporal processing, respectively ( Linn and Petersen, 1985 ; Szelag et al, 1997 , 1998 ).…”

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

“…Although many studies of word segmentation detected SL effects on the N400 component [ 43 , 46 , 88 , 89 , 93 , 94 , 105 ], which is generally considered to reflect a semantic meaning in language and music [ 106 , 107 , 108 ], auditory brainstem response (ABR) [ 96 ], P50 [ 41 ], N100 [ 94 ], mismatch negativity (MMN) [ 40 , 44 , 98 ], P200 [ 46 , 89 , 105 ], N200–250 [ 44 , 47 ], and P300 [ 83 ] have also been reported to reflect SL effects ( Table 1 ). In addition, other studies using Markov models also reported that SL is reflected in the P50 [ 14 , 36 , 37 ], N100 [ 10 , 14 , 32 , 35 ], and P200 components [ 35 ]. Compared with later auditory responses such as N400, the auditory responses that peak earlier than 10 ms after stimulus presentation (e.g., ABR) and at 20–80 ms, which is around P50 latency, have been attributed to parallel thalamo–cortical connections or cortico–cortical connections between the primary auditory cortex and the superior temporal gyrus [ 109 ].…”

“…Recent neural studies have demonstrated that SL of speech, pitch, timbre, and chord sequences can be performed and reflected in ERP/ERF [ 10 , 36 , 37 , 40 , 46 ]. Furthermore, the brain codes statistics of auditory sequences as relative information, such as relative distribution of pitch and formant frequencies, which could be used for comprehension of another sequential structure [ 10 , 32 ], suggesting that SL is ubiquitous and domain-general.…”

“…As a result of the implicit nature of SL, however, humans cannot verbalize exactly what they statistically learn. Nonetheless, a body of evidence indicates that neurophysiological and behavioural responses can unveil musical and linguistic SL effects [ 14 , 32 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ] in the framework of predictive coding [ 20 ]. Furthermore, recent studies have detected the effects of musical training on linguistic SL of words [ 41 , 43 , 45 , 46 , 47 ] and the interactions between musical and linguistic SL [ 10 ] and between auditory and visual SL [ 44 , 48 , 49 , 50 ].…”

Time-course variation of statistics embedded in music: Corpus study on implicit learning and knowledge

“…A tone with a higher transitional probability of sequences in a musical score may be one that a composer is more likely to choose compared to other tones with lower transitional probability [ 20 – 23 , 58 – 60 ]. Thus, the transitional probability matrix calculated from music may represent the characteristics of a composer’s statistical knowledge by which a forthcoming tone is implicitly defined.…”