Abstract
Pattern learning facilitates prediction about upcoming events. Within the auditory system such predictions can be studied by examining effects on a component of the auditory-evoked potential known as mismatch negativity (MMN). MMN is elicited when sound does not conform to the characteristics inferred from statistical probabilities derived from the recent past. Stable patterning in sequences elevates confidence in automatically generated perceptual inferences about what sound should come next and when. MMN amplitude should be larger when sequence is highly stable compared to when it is more volatile. This expectation has been tested using a multi-timescale paradigm. In this study, two sounds of different duration alternate roles as a predictable repetitive “standard” and rare MMN-eliciting “deviation.” The paradigm consists of sound sequences that differ in the rate at which the roles of two tones alternate, varying from slowly changing (high stability) to rapidly alternating (low stability). Previous studies using this paradigm discovered a “primacy bias” affecting how stability in patterning impacts MMN amplitude. The primacy bias refers to the observation that the effect of longer-term stability within sequences only appears to impact MMN to the sound first encountered as deviant (the sound that is rare when the sequence commences). This study determines whether this order-driven bias generalizes to sequences that contain two tones differing in pitch. By manipulating (within-subjects) the order in which sounds are encountered as deviants the data demonstrate the two defining characteristics of primacy bias: (1) sequence stability only ever impacts MMN amplitude to the first-deviant sound; and (2) within higher stability sequences, MMN is significantly larger when a sound is the first compared to when it is the second deviant. The results are consistent with a general order-driven bias exerting modulating effects on MMN amplitude over a longer timescale.
Highlights
Bayesian models of perception stipulate that learning is fundamentally linked to the ability to continuously update the brain’s estimates of conditional probabilities (Mathys et al, 2011)
BIAS PATTERN 1: SLOW SEQUENCE mismatch negativity (MMN) LARGER THAN FAST SEQUENCE MMN FOR THE FIRST DEVIANT ONLY The main hypothesis based on prior data for duration MMN was that there should be a sequence by tone interaction that is dependent on order (1, 2, 3)
In order 3 the High-Low-High group produces a main effect of sequence [F(1, 14) = 25.01, p < 0.001, η2 = 0.64] with a trend for MMN to both tones being larger in slow sequences
Summary
Bayesian models of perception stipulate that learning is fundamentally linked to the ability to continuously update the brain’s estimates of conditional probabilities (Mathys et al, 2011). The accuracy of these estimates determines the accuracy of predicting upcoming events. MMN occurs when a sound violates some regular pattern within a sequence of sounds. The learned pattern enables the brain to infer the most likely subsequent state of brain activation to follow the present state, in other words, to form predictions about what stimulus should come based on a dynamically updated probabilistic inference. MMN is evoked when the prediction does not match the state encountered
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