The importance of paying attention to a task at hand is emphasized from an early age and extends throughout life. The costs of attentional focus, however, include the potential to miss important changes in the environment, so some process for monitoring nontask information is essential. In this study, a model of latent cognitive variables was applied to data obtained from a two-alternative forced-choice task where participants identified the longer of two sounds. Using an adaptive procedure task, accuracy was maintained at a higher or lower level creating two difficulties, and the sounds were heard either where frequency changes in the sound were rare or common (oddball and multistandard conditions, respectively). Frequency changes created stimulus-driven “distraction” effects in the oddball sequence only, and cognitive modeling (using the linear ballistic accumulator) attributed these effects to slowed accumulation of evidence about tone length on these trials. Concurrent recording of auditory ERPs revealed these delays in evidence accumulation to be related to the amplitude of N2 or mismatch negativity period and P300 response components. In contrast, the response time on trials after a rare frequency change was associated with increased caution in decision-making. Results support the utility of mapping behavioral and ERP measures of performance to latent cognitive processes that contribute to performance and are consistent with a momentary diversion of resources to evaluate the deviant sound feature and remodel predictions about sound.

Our understanding of the sensory environment is contextualized on the basis of prior experience. Measurement of auditory ERPs provides insight into automatic processes that contextualize the relevance of sound as a function of how sequences change over time. However, task-independent exposure to sound has revealed that strong first impressions exert a lasting impact on how the relevance of sound is contextualized. Dynamic causal modeling was applied to auditory ERPs collected during presentation of alternating pattern sequences. A local regularity (a rare p = .125 vs. common p = .875 sound) alternated to create a longer timescale regularity (sound probabilities alternated regularly creating a predictable block length), and the longer timescale regularity changed halfway through the sequence (the regular block length became shorter or longer). Predictions should be revised for local patterns when blocks alternated and for longer patterning when the block length changed. Dynamic causal modeling revealed an overall higher precision for the error signal to the rare sound in the first block type, consistent with the first impression. The connectivity changes in response to errors within the underlying neural network were also different for the two blocks with significantly more revision of predictions in the arrangement that violated the first impression. Furthermore, the effects of block length change suggested errors within the first block type exerted more influence on the updating of longer timescale predictions. These observations support the hypothesis that automatic sequential learning creates a high-precision context (first impression) that impacts learning rates and updates to those learning rates when predictions arising from that context are violated. The results further evidence automatic pattern learning over multiple timescales simultaneously, even during task-independent passive exposure to sound.