Neural correlates of auditory pattern learning in the auditory cortex

Auksztulewicz, Ryszard; An, Hee-Jung; Abichacra, Nicolas; Sutter, Mitchell L.; Schnupp, Jan W. H.

doi:10.1101/2020.09.24.311464

Cited by 3 publications

(5 citation statements)

References 36 publications

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“…Statistical Learning (SL) refers to the remarkable ability to implicitly learn the rules and relationship between different stimuli and events in the environment. The capacity for SL has been studied in both humans and non-human species (Santolin et al 2016;Santolin and Saffran 2018;Kang et al 2021), and has been demonstrated across different sensory domains, emerging relatively early in infancy (Saffran, Jenny R., Aslin, Richard N., Newport 1996;Saffran et al 1997;Gómez and Gerken 2000;Graf et al 2007;Pelucchi et al 2009). SL has been hypothesized to play an important role in the development of many key cognitive abilities such as communication skills, object recognition and sensory-motor learning (Kent and Read 2002;Saffran 2003, 2007;Evans et al 2009;Emberson et al 2011;Arciuli and von Koss Torkildsen 2012;Kidd 2012;Misyak and Christiansen 2012;Hsu et al 2014;Spencer et al 2015;Erickson and Thiessen 2015;Kidd and Arciuli 2016;Siegelman et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Sensitivity of EEG-Based Frequency-Tagging as a Metric for Statistical Learning

Pinto

Prior

Golumbic

2022

Neurobiology of Language

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Statistical Learning (SL) is hypothesized to play an important role in language development. However, the measures typically used to assess SL, particularly at the level of individual participants, are largely indirect and have low sensitivity. Recently, a neural metric based on frequency-tagging has been proposed as an alternative measure for studying SL. We tested the sensitivity of frequency-tagging measures for studying SL in individual participants in an artificial language paradigm, using non-invasive EEG recordings of neural activity in humans. Importantly, we use carefully constructed controls to address potential acoustic confounds of the frequency-tagging approach, and compared the sensitivity of EEG-based metrics to both explicit and implicit behavioral tests of SL. Group-level results confirm that frequency-tagging can provide a robust indication of SL for an artificial language, above and beyond potential acoustic confounds. However, this metric had very low sensitivity at the level of individual participants, with significant effects found only in 30% of participants. Comparison of the neural metric to previously established behavioral measures for assessing SL showed a significant yet weak correspondence with performance on an implicit task, which was above-chance in 70% of participants, but no correspondence with the more common explicit 2AFC task, where performance did not exceed chance-level. Given the proposed ubiquitous nature of SL, our results highlight some of the operational and methodological challenges of obtaining robust metrics for assessing SL, as well as the potential confounds that should be taken into account when using the frequency-tagging approach in EEG studies.

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Section: Introductionmentioning

confidence: 99%

Assessing the Sensitivity of EEG-Based Frequency-Tagging as a Metric for Statistical Learning

Pinto

Prior

Golumbic

2022

Neurobiology of Language

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“…On the other hand, A2 cells have less variability for encoding sound inputs across sound duration, at least between 180 ms and 900 ms, and process sound input as a whole, instead of more fine-tuned spectrotemporal features of shorter segment embedded within the sequence. This finding is parallel to more local temporal processing in A1 and global processing in A2 (Asokan et al, 2021;Kang et al, 2021). Furthermore, greater noise correlation representing functional connectivity was observed only for Target sounds with short segment duration, and only in superficial layers of A1 and A2 and, where more corticocortical projections are predominant.…”

Section: Discussionmentioning

confidence: 61%

“…We here show that a subset of neurons in ACtx of mice show neural adaptation to complex stimuli in a passive, implicit learning setting. We adapted an experimental paradigm from human studies that efficiently triggered implicit learning of abstract and complex sounds (e.g., (Agus et al, 2010;Andrillon et al, 2015;Kang et al, 2021). Our findings provide advances in understanding how efficient encoding of sound information is processed in ACtx.…”

Section: Discussionmentioning

confidence: 99%

“…Dynamic Random Chords (DRCs): Adapted from previous studies (Agus et al, 2010;Luo et al, 2013;Andrillon et al, 2015;Kang et al, 2021), we presented a series of spectro-temporally complex artificial sounds, dynamic random chords; DRCs. One segment of DRC is defined as about 200-ms duration in which at each time bin (20 ms) filled with equivalent length of a tone pip with varying sound levels (50 -90 dB SPL) at each frequency bin (4-40 kHz separated by 20 frequency bins following a logarithmic scale) (Fig.…”

Section: Stimuli and Paradigmmentioning

confidence: 99%

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Auditory memory of complex sounds in sparsely distributed, highly correlated neurons in the auditory cortex

Kang,

Kanold

2023

Preprint

Self Cite

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Listening in complex sound environments requires rapid segregation of different sound sources e.g., speakers from each other, speakers from other sounds, or different instruments in an orchestra, and also adjust auditory processing on the prevailing sound conditions. Thus, fast encoding of inputs and identifying and adapting to reoccurring sounds are necessary for efficient and agile sound perception. This adaptation process represents an early phase of developing implicit learning of sound statistics and thus represents a form of auditory memory. The auditory cortex (ACtx) is known to play a key role in this encoding process but the underlying circuits and if hierarchical processing exists are not known. To identify ACtx regions and cells involved in this process, we simultaneously imaged population of neurons in different ACtx subfields using in vivo 2-photon imaging in awake mice. We used an experimental stimulus paradigm adapted from human studies that triggers rapid and robust implicit learning to passively present complex sounds and imaged A1 Layer 4 (L4), A1 L2/3, and A2 L2/3. In this paradigm, a frozen spectro-temporally complex 'Target' sound would be randomly re-occurring within a stream of random other complex sounds. We find distinct groups of cells that are specifically responsive to complex acoustic sequences across all subregions indicating that even the initial thalamocortical input layers (A1 L4) respond to complex sounds. Cells in all imaged regions showed decreased response amplitude for reoccurring Target sounds indicating that a memory signature is present even in the thalamocortical input layers. On the population level we find increased synchronized activity across cells to the Target sound and that this synchronized activity was more consistent across cells regardless of the duration of frozen token within Target sounds in A2, compared to A1. These findings suggest that ACtx and its input layers play a role in auditory memory for complex sounds and suggest a hierarchical structure of processes for auditory memory.

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“…Statistical Learning (SL) refers to the remarkable ability to implicitly learn the rules and relationship between different stimuli and events in the environment. The capacity for SL has been studied in both humans and non-human species (Santolin and Saffran, 2018;Kang et al, 2021), and has been demonstrated across different sensory domains, emerging relatively early in infancy (Saffran, Jenny R., Aslin, Richard N., Newport, 1996;Saffran et al, 1997;Gómez and Gerken, 2000;Graf et al, 2007;Pelucchi et al, 2009). SL has been hypothesized to play an important role in the development of many key cognitive abilities such as communication skills, object recognition and sensory-motor learning (Read, 1992;Saffran, 2003, 2007;Evans and Robe-torres, 2009;Emberson et al, 2011;Arciuli and von Koss Torkildsen, 2012;Kidd, 2012;Misyak and Christiansen, 2012;Hsu et al, 2014;Spencer et al, 2015;Erickson and Thiessen, 2015;Kidd and Arciuli, 2016;Siegelman et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Assessing the sensitivity of EEG-based frequency-tagging as a metric for statistical learning

Pinto

Prior

Golumbic

2021

Preprint

View full text Add to dashboard Cite

Statistical Learning (SL) is hypothesized to play an important role in language development. However, the behavioral measures typically used to assess SL, particularly at the level of individual participants, are largely indirect and often have low sensitivity. Recently, a neural metric based on frequency-tagging has been proposed as an alternative and more direct measure for studying SL. Here we tested the sensitivity of frequency-tagging measures for studying SL in individual participants in an artificial language paradigm, using non-invasive EEG recordings of neural activity in humans. Importantly, we use carefully constructed controls, in order to address potential acoustic confounds of the frequency-tagging approach. We compared the sensitivity of EEG-based metrics to both explicit and implicit behavioral tests of SL, and the correspondence between these presumed converging operations. Group-level results confirm that frequency-tagging can provide a robust indication of SL for an artificial language, above and beyond potential acoustic confounds. However, this metric had very low sensitivity at the level of individual participants, with significant effects found only in 30% of participants. Conversely, the implicit behavior measures indicated that SL has occurred in 70% of participants, which is more consistent with the proposed ubiquitous nature of SL. Moreover, there was low correspondence between the different measures used to assess SL. Taken together, while some researchers may find the frequency-tagging approach suitable for their needs, our results highlight the methodological challenges of assessing SL at the individual level, and the potential confounds that should be taken into account when interpreting frequency-tagged EEG data.

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Neural correlates of auditory pattern learning in the auditory cortex

Cited by 3 publications

References 36 publications

Assessing the Sensitivity of EEG-Based Frequency-Tagging as a Metric for Statistical Learning

Assessing the Sensitivity of EEG-Based Frequency-Tagging as a Metric for Statistical Learning

Auditory memory of complex sounds in sparsely distributed, highly correlated neurons in the auditory cortex

Assessing the sensitivity of EEG-based frequency-tagging as a metric for statistical learning

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