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

Abstract

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 hypothesize 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 network-style structure and deviant 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.