Literature DB >> 25061926

Prior experience biases subcortical sensitivity to sound patterns.

Erika Skoe1, Jennifer Krizman, Emily Spitzer, Nina Kraus.   

Abstract

To make sense of our ever-changing world, our brains search out patterns. This drive can be so strong that the brain imposes patterns when there are none. The opposite can also occur: The brain can overlook patterns because they do not conform to expectations. In this study, we examined this neural sensitivity to patterns within the auditory brainstem, an evolutionarily ancient part of the brain that can be fine-tuned by experience and is integral to an array of cognitive functions. We have recently shown that this auditory hub is sensitive to patterns embedded within a novel sound stream, and we established a link between neural sensitivity and behavioral indices of learning [Skoe, E., Krizman, J., Spitzer, E., & Kraus, N. The auditory brainstem is a barometer of rapid auditory learning. Neuroscience, 243, 104-114, 2013]. We now ask whether this sensitivity to stimulus statistics is biased by prior experience and the expectations arising from this experience. To address this question, we recorded complex auditory brainstem responses (cABRs) to two patterned sound sequences formed from a set of eight repeating tones. For both patterned sequences, the eight tones were presented such that the transitional probability (TP) between neighboring tones was either 33% (low predictability) or 100% (high predictability). Although both sequences were novel to the healthy young adult listener and had similar TP distributions, one was perceived to be more musical than the other. For the more musical sequence, participants performed above chance when tested on their recognition of the most predictable two-tone combinations within the sequence (TP of 100%); in this case, the cABR differed from a baseline condition where the sound sequence had no predictable structure. In contrast, for the less musical sequence, learning was at chance, suggesting that listeners were "deaf" to the highly predictable repeating two-tone combinations in the sequence. For this condition, the cABR also did not differ from baseline. From this, we posit that the brainstem acts as a Bayesian sound processor, such that it factors in prior knowledge about the environment to index the probability of particular events within ever-changing sensory conditions.

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Mesh:

Year:  2015        PMID: 25061926     DOI: 10.1162/jocn_a_00691

Source DB:  PubMed          Journal:  J Cogn Neurosci        ISSN: 0898-929X            Impact factor:   3.225


  10 in total

1.  Tone language experience-dependent advantage in pitch representation in brainstem and auditory cortex is maintained under reverberation.

Authors:  Ananthanarayan Krishnan; Chandan H Suresh; Jackson T Gandour
Journal:  Hear Res       Date:  2019-03-15       Impact factor: 3.208

2.  Stability and plasticity in neural encoding of linguistically relevant pitch patterns.

Authors:  Zilong Xie; Rachel Reetzke; Bharath Chandrasekaran
Journal:  J Neurophysiol       Date:  2017-01-11       Impact factor: 2.714

3.  Language experience-dependent advantage in pitch representation in the auditory cortex is limited to favorable signal-to-noise ratios.

Authors:  Chandan H Suresh; Ananthanarayan Krishnan; Jackson T Gandour
Journal:  Hear Res       Date:  2017-09-14       Impact factor: 3.208

Review 4.  Unraveling the Biology of Auditory Learning: A Cognitive-Sensorimotor-Reward Framework.

Authors:  Nina Kraus; Travis White-Schwoch
Journal:  Trends Cogn Sci       Date:  2015-10-08       Impact factor: 20.229

5.  Detecting change in stochastic sound sequences.

Authors:  Benjamin Skerritt-Davis; Mounya Elhilali
Journal:  PLoS Comput Biol       Date:  2018-05-29       Impact factor: 4.475

Review 6.  Neurophysiological Markers of Statistical Learning in Music and Language: Hierarchy, Entropy, and Uncertainty.

Authors:  Tatsuya Daikoku
Journal:  Brain Sci       Date:  2018-06-19

7.  When the statistical MMN meets the physical MMN.

Authors:  Vera Tsogli; Sebastian Jentschke; Tatsuya Daikoku; Stefan Koelsch
Journal:  Sci Rep       Date:  2019-04-03       Impact factor: 4.379

Review 8.  Evolving perspectives on the sources of the frequency-following response.

Authors:  Emily B J Coffey; Trent Nicol; Travis White-Schwoch; Bharath Chandrasekaran; Jennifer Krizman; Erika Skoe; Robert J Zatorre; Nina Kraus
Journal:  Nat Commun       Date:  2019-11-06       Impact factor: 14.919

Review 9.  Active inference, communication and hermeneutics.

Authors:  Karl J Friston; Christopher D Frith
Journal:  Cortex       Date:  2015-04-15       Impact factor: 4.027

10.  Concurrent Statistical Learning of Ignored and Attended Sound Sequences: An MEG Study.

Authors:  Tatsuya Daikoku; Masato Yumoto
Journal:  Front Hum Neurosci       Date:  2019-04-17       Impact factor: 3.169

  10 in total

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