When the statistical MMN meets the physical MMN

Tsogli, Vera; Jentschke, Sebastian; Daikoku, Tatsuya; Koelsch, Stefan

doi:10.1038/s41598-019-42066-4

Cited by 34 publications

(51 citation statements)

References 46 publications

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“…The effect consists of an attenuated N2 amplitude in pattern (vs. random) presentations, which has been also related to the deployment of more attentional resources. In the same latency range, Koelsch et al ( 2016 ) and Tsogli et al ( 2019 ) recently reported an analog of the mismatch negativity (MMN) wave, called the statistical MMN (sMMN), to index the automatic change detection processes based on implicit extraction of statistical regularities embedded in the input.…”

Section: Introductionmentioning

confidence: 99%

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

Soares

Gutiérrez-Domínguez

Vasconcelos

et al. 2020

Front. Hum. Neurosci.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Soares

Gutiérrez-Domínguez

Vasconcelos

et al. 2020

Front. Hum. Neurosci.

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“…Another example is the mismatch negativity, which is elicited by stimuli with a low probability of occurrence, even when presented outside the focus of attention (e.g. Koelsch et al, 2016; Tsogli et al, 2019). Using MEG, Paraskevopoulos et al (2012) demonstrated a mismatch response to part-triplets compared to triplets during exposure to a statistically regular stream, as early as 50ms after stimulus onset, even though participants’ post-exposure recognition was at chance level.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of nonlinguistic statistical learning: From neural entrainment to the emergence of explicit knowledge

Moser

Batterink

Hegner

et al. 2020

Preprint

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Humans are highly attuned to patterns in the environment. This ability to detect environmental patterns, referred to as statistical learning, plays a key role in many diverse aspects of cognition. However, the spatiotemporal neural mechanisms underlying implicit statistical learning, and how these mechanisms may relate or give rise to explicit learning, remain poorly understood. In the present study, we investigated these different aspects of statistical learning by using an auditory nonlinguistic statistical learning paradigm combined with magnetoencephalography. Twenty-four healthy volunteers were exposed to structured and random tone sequences, and statistical learning was quantified by neural entrainment. Already early during exposure, participants showed strong entrainment to the embedded tone patterns. A significant increase in entrainment over exposure was detected at central sensors, reflecting the trajectory of learning. While source reconstruction revealed a wide range of brain areas involved in this process, entrainment in right temporo-parietal and frontal areas as well as left pre-central and frontal areas significantly predicted behavioral performance, especially in the last third of stimulus exposure. These results give insights into the dynamic relation between neural entrainment and explicit learning of triplet structures, suggesting that these two components are systematically related yet dissociable. Neural entrainment reflects robust, implicit learning of underlying patterns, whereas the emergence of explicit knowledge, likely built on the implicit encoding of structure, varies across individuals and may depend on both sufficient exposure time and attention.

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“…Koelsch and colleagues (2016) found that the amplitude of this response was negatively related to the probability of an auditory event. It has been suggested that this early component reflects the magnitude of prediction errors in statistical learning contexts (Tsogli, Jentschke, Daikoku, & Koelsch, 2019).…”

Section: Introductionmentioning

confidence: 99%

Auditory but Not Audiovisual Cues Lead to Higher Neural Sensitivity to the Statistical Regularities of an Unfamiliar Musical Style

Zioga

Harrison

Pearce

et al. 2020

Journal of Cognitive Neuroscience

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It is still a matter of debate whether visual aids improve learning of music. In a multisession study, we investigated the neural signatures of novel music sequence learning with or without aids (auditory-only: AO, audiovisual: AV). During three training sessions on 3 separate days, participants (nonmusicians) reproduced (note by note on a keyboard) melodic sequences generated by an artificial musical grammar. The AV group ( n = 20) had each note color-coded on screen, whereas the AO group ( n = 20) had no color indication. We evaluated learning of the statistical regularities of the novel music grammar before and after training by presenting melodies ending on correct or incorrect notes and by asking participants to judge the correctness and surprisal of the final note, while EEG was recorded. We found that participants successfully learned the new grammar. Although the AV group, as compared to the AO group, reproduced longer sequences during training, there was no significant difference in learning between groups. At the neural level, after training, the AO group showed a larger N100 response to low-probability compared to high-probability notes, suggesting an increased neural sensitivity to statistical properties of the grammar; this effect was not observed in the AV group. Our findings indicate that visual aids might improve sequence reproduction while not necessarily promoting better learning, indicating a potential dissociation between sequence reproduction and learning. We suggest that the difficulty induced by auditory-only input during music training might enhance cognitive engagement, thereby improving neural sensitivity to the underlying statistical properties of the learned material.

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When the statistical MMN meets the physical MMN

Cited by 34 publications

References 46 publications

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

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

Dynamics of nonlinguistic statistical learning: From neural entrainment to the emergence of explicit knowledge

Auditory but Not Audiovisual Cues Lead to Higher Neural Sensitivity to the Statistical Regularities of an Unfamiliar Musical Style

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