First impressions bias on sound sequence learning onmultiple timescales: an order-driven phenomenon inauditory mismatch negativity

摘要

H umans are prone to systematic biases in perception that impact rationality in judgement. First-impression bias occurs when judgement is overly affected by infor- mation presented during an initial encoun- ter. Using the amplitude of a specific brain response, the mismatch negativity (MMN), our team discovered that the brain is prone to this bias effect during the very early stages of sound-sequence learning preceding know- ing awareness. In our research program, we aim to determine which experimental condi- tions expose or modify first-impression bias effects on sound-pattern learning on mul- tiple timescales. Predictive coding models assume the brain is hierarchically organised and uses perception to make inferences about the sensory world whilst updating predic- tions about incoming sensory information. Recurring comparisons between bottom-up input and top-down predictions consider environmental noise, and determine the inferential modelling process. MMN, an event-related response evoked by violating regularity in a structured sound sequence, is an example of a prediction error signal. Its presence informs on prediction model con- tent whereas its amplitude informs on model confidence (or precision). Prediction error amplitude to a pattern violation is largest when model confidence is very high and may require engagement of additional, higher- order resources. First-impression bias shows that the network uses contextual information at sound sequence onset to modulate MMN amplitude to probabilistic changes thereafter. Our data show that first-impression bias is a remarkably robust and long-lasting phenom- enon that can be interrupted if participants undertake an attention-demanding task whilst hearing multi-timescale sequences or are provided with accurate foreknowledge about sound structures before sequence exposure. In interpreting these data, we dis- cuss how models assuming only local sound probabilistic information drives the MMN- generating process cannot explain bias effects on MMN amplitude. Rather, the bias is a striking example of a hierarchical inference process incorporating attentional resources that considers the potential relevance of sound information and its stability over time.