Prior Experience Biases Subcortical Sensitivity to Sound Patterns

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.

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

“…The word segmentation paradigm is frequently used to examine the neural basis underlying SL (e.g., [ 34 , 41 , 43 , 44 , 46 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 ]). This paradigm basically consists of a concatenation of pseudo-words ( Figure 2 a).…”

“…Finally, the effects of SL manifest as a difference in the ERP and ERF amplitudes between stimuli with lower and higher TPs ( Figure 4 ). 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 ].…”

Unraveling the Biology of Auditory Learning: A Cognitive–Sensorimotor–Reward Framework

“…Infants quickly learn statistical regularities in the acoustic environment, and this is thought to contribute to language acquisition [52]. But not even these ostensibly passive learning processes are exempt from cognitive influence: prior experience and active expectations guide statistical learning [53,54]. Thus, as young as infancy, listeners can connect incoming sounds to meaning, and also exert meaning on incoming sounds, reinforcing the interplay between sensorimotor and cognitive systems in auditory learning.…”

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

“…There is compelling empirical evidence supporting the view that language (or music) experience enhances neural representation of temporal attributes of pitch that are behaviorally relevant at both cortical and subcortical levels (Gandour & Krishnan, 2014; Gandour & Krishnan, 2016; Intartaglia et al, 2016; Krishnan et al, 2012a; Skoe et al, 2015; Song et al, 2008; Wong et al, 2007; Zatorre & Gandour, 2008; Zatorre & Baum, 2012). …”