Probing sensitivity to statistical structure in rapid sound sequences using deviant detection tasks

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發表在:bioRxiv (Feb 13, 2025)
主要作者: Milne, Alice
其他作者: Chait, Maria, Conway, Christopher M
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Cold Spring Harbor Laboratory Press
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001 3166352029
003 UK-CbPIL
022 |a 2692-8205 
024 7 |a 10.1101/2024.04.19.590221  |2 doi 
035 |a 3166352029 
045 0 |b d20250213 
100 1 |a Milne, Alice 
245 1 |a Probing sensitivity to statistical structure in rapid sound sequences using deviant detection tasks 
260 |b Cold Spring Harbor Laboratory Press  |c Feb 13, 2025 
513 |a Working Paper 
520 3 |a Statistical structures and our ability to exploit them are a ubiquitous component of daily life. Yet, we still do not fully understand how we track these sophisticated statistics and the role they play in sensory processing. Predictive coding frameworks hypothesise that for stimuli that can be accurately anticipated based on prior experience, we rely more strongly on our internal model of the sensory world and are more "surprised" when that expectation is unmet. The current study used this phenomenon to probe listeners' sensitivity to probabilistic structures generated using rapid 50 milli-second tone-pip sequences that precluded conscious prediction of upcoming stimuli. Over three experiments we measured listeners' sensitivity and response time to deviants of a frequency outside the expected range. Predictable sequences were generated using either a triplet-based or community structure and deviance detection contrasted against the same set of tones but in a random, unpredictable order. All experiments found structured sequences enhanced deviant detection relative to random sequences. Additionally, Experiment 2 used three different instantiations of the community structure to demonstrate that the level of uncertainty in the structured sequences modulated deviant saliency. Finally, Experiment 3 placed the deviant within an established community or immediately after a transition between communities, where the perceptual boundary should generate momentary uncertainty. However, this manipulation did not impact performance. Together these results demonstrate that probabilistic contexts generated from statistical structures modulate the processing of an ongoing auditory signal, leading to an improved ability to detect unexpected deviant stimuli, consistent with the predictive coding framework.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Updates have been made based on reviewer comments. Primarily we have added additional discussion regarding potential auditory streaming effects, corrected an error and improved readability on the methods figure for experiment 2. 
653 |a Signal processing 
653 |a Statistics 
653 |a Community structure 
653 |a Information processing 
653 |a Sensory integration 
653 |a Auditory system 
653 |a Streaming 
653 |a Neural coding 
700 1 |a Chait, Maria 
700 1 |a Conway, Christopher M 
773 0 |t bioRxiv  |g (Feb 13, 2025) 
786 0 |d ProQuest  |t Biological Science Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3166352029/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u https://www.biorxiv.org/content/10.1101/2024.04.19.590221v3