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. 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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