Abstract
Recent computational models of perception conceptualize auditory oddball responses as signatures of a (Bayesian) learning process, in line with the influential view of the mismatch negativity (MMN) as a prediction error signal. Novel MMN experimental paradigms have put an emphasis on neurophysiological effects of manipulating regularity and predictability in sound sequences. This raises the question of the contextual adaptation of the learning process itself, which on the computational side speaks to the mechanisms of gain-modulated (or precision-weighted) prediction error. In this study using electrocorticographic (ECoG) signals, we manipulated the predictability of oddball sound sequences with two objectives: (i) Uncovering the computational process underlying trial-by-trial variations of the cortical responses. The fluctuations between trials, generally ignored by approaches based on averaged evoked responses, should reflect the learning involved. We used a general linear model (GLM) and Bayesian Model Reduction (BMR) to assess the respective contributions of experimental manipulations and learning mechanisms under probabilistic assumptions. (ii) To validate and expand on previous findings regarding the effect of changes in predictability using simultaneous EEG-MEG recordings. Our trial-by-trial analysis revealed only a few stimulus-responsive sensors but the measured effects appear to be consistent over subjects in both time and space. In time, they occur at the typical latency of the MMN (between 100 and 250 ms post-stimulus). In space, we found a dissociation between time-independent effects in more anterior temporal locations and time-dependent (learning) effects in more posterior locations. However, we could not observe any clear and reliable effect of our manipulation of predictability modulation onto the above learning process. Overall, these findings clearly demonstrate the potential of trial-to-trial modeling to unravel perceptual learning processes and their neurophysiological counterparts.
Highlights
Recent computational models of perception address sound processing in oddball paradigms as the learning of regularities that pertain to the repetition of an acoustic pattern
The larger value we found under predictability can be interpreted as a larger memory span that fits well with the fact that the more structured the sound sequence, the more past information is integrated to make predictions These findings and few others speak to the plausibility of perceptual models engaged in oddball processing and the trial-by-trial fluctuations they prescribe (Ostwald et al, 2012; Lieder et al, 2013; Stefanics et al, 2014; Meyniel et al, 2016; Sedley et al, 2016; Weber et al, 2020)
In order to test Bayesian learning as the perceptual model the brain would use when exposed to oddball sounds, as well as its automatic modulation under predictability, we considered a modeling framework based on advanced Bayesian methods and applying to single-trial activity
Summary
The Mismatch Negativity (MMN; Näätänen et al, 1978) has been suggested to reflect a prediction error (Friston, 2005) This model of the MMN leverages on complex underlying (Bayesian) computations that raise the practical question of their neuronal implementation (Knill and Pouget, 2004). An important aspect that computational models of perception have put forward is the influence of the acoustic context onto sound processing in oddball paradigms, that is, as we shall see, explicitly formalized in popular predictive coding implementation (Friston, 2005; Spratling, 2016) This is in line with recent MMN findings which emphasized the importance of the ordering of experimental conditions (Fitzgerald and Todd, 2020; Todd et al, 2021), pointing out the need for refining our understanding of mismatch responses
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