Predictive mechanisms in adaptive chunking during rapid extraction of meaning from spoken signals

Abstract

Speech understanding requires rapid of categorizations of auditory inputs. Neuronal rhythms play key roles in these computations; however, the principles by which timing, acoustics, and context are combined on-line to accomplish speech perception remain murky. Here, we outline a model of how temporal constraints sculpt meaning-making in speech processing according to an adaptively quasi-rhythmic process driven by fluctuations in certainty and predictability of timing and content. We propose that predictive mechanisms are leveraged to reduce possible identities of inputs based on prior inputs in which the smallest chunks of input can be rapidly compressed, recoded, and passed to higher representational linguistic levels. Evidence accumulation at each level of representation is used to assess likelihoods of candidate interpretations and their reliabilities at different timescales in a manner pegged to speech rate and situation-specific speed-accuracy tradeoffs. This synthesis illuminates mechanisms of human speech processing while making predictions for neuronal implementations and behavioral psychophysics.