Adjudicating Between Local and Global Architectures of Predictive Processing in the Subcortical Auditory Pathway

Abstract

Predictive processing, a leading theoretical framework for sensory processing, suggests that the brain constantly generates predictions on the sensory world and that perception emerges from the comparison between these predictions and the actual sensory input. This requires two distinct neural elements: generative units, which encode the model of the sensory world; and prediction error units, which compare these predictions against the sensory input. Although predictive processing is generally portrayed as a theory of cerebral cortex function, animal and human studies over the last decade have robustly shown the ubiquitous presence of prediction error responses in several nuclei of the auditory, somatosensory, and visual subcortical pathways. In the auditory modality, prediction error is typically elicited using so-called oddball paradigms, where sequences of repeated pure tones with the same pitch are at unpredictable intervals substituted by a tone of deviant frequency. Repeated sounds become predictable promptly and elicit decreasing prediction error; deviant tones break these predictions and elicit large prediction errors. The simplicity of the rules inducing predictability make oddball paradigms agnostic about the origin of the predictions. Here, we introduce two possible models of the organizational topology of the predictive processing auditory network: (1) the global view, that assumes that predictions on the sensory input are generated at high-order levels of the cerebral cortex and transmitted in a cascade of generative models to the subcortical sensory pathways; and (2) the local view, that assumes that independent local models, computed using local information, are used to perform predictions at each processing stage. In the global view information encoding is optimized globally but biases sensory representations along the entire brain according to the subjective views of the observer. The local view results in a diminished coding efficiency, but guarantees in return a robust encoding of the features of sensory input at each processing stage. Although most experimental results to-date are ambiguous in this respect, recent evidence favors the global model.

Parallel processing in speech perception with local and global representations of linguistic context

“…This question has appeared in different forms, for example in early debates about whether sensory systems are modular (Fodor, 1985), or whether sensory input and contextual constraints are combined immediately in speech perception (Marslen- Wilson and Tyler, 1975;Tanenhaus et al, 1995). A similar distinction has also surfaced more recently between the local and global architectures of predictive coding (Tabas and von Kriegstein, 2021).…”

“…Such a unified representation is consistent with empirical evidence for top-down modulation of sensory representations, for example, suggesting that recognizing a word can bias subsequent phonetic representations ( Luthra et al, 2021 ), that listeners weight cues like a Bayes-optimal observer during speech perception ( Bejjanki et al, 2011 ; Feldman et al, 2009 ), and that they immediately interpret incoming speech with regard to communicative goals ( Chambers et al, 2004 ; Heller et al, 2016 ). A recent implementation proposed for such a model is the global variant of hierarchical predictive coding, which assumes a cascade of generative models predicting sensory input from higher-level expectations ( Clark, 2013 ; Friston, 2010 ; Tabas and Kriegstein, 2021 ). A unified model is also assumed by classical interactive models of speech processing, which rely on cross-hierarchy interactions to generate a globally consistent interpretation of the input ( McClelland and Rumelhart, 1981 ; McClelland and Elman, 1986 ; Magnuson et al, 2018 ).…”

Parallel processing in speech perception with local and global representations of linguistic context

“…Such a unified representation is consistent with empirical results suggesting that word recognition can bias subsequent phonetic representations 28 , that listeners weight cues like a Bayes-optimal observer during speech perception 29,30 , and that they immediately interpret incoming speech with regard to communicative goals 31,32 . A recent implementation proposed for such a model is the global variant of hierarchical predictive coding, which assumes a cascade of generative models predicting sensory input from higher level expectations 25,33,34 . However, a unified model is also assumed by classical interactive models of speech processing, which rely on cross-hierarchy interactions to generate a globally consistent interpretation of the input [35][36][37] .…”

Frequency and frequency modulation share the same predictive encoding mechanisms in human auditory cortex

“…Formulations of the predictive coding framework disagree on whether predictions from generative model units inform prediction errors only at the immediate lower stage [10,79] or also at subsequent stages of the processing hierarchy (see [21] for a review of the empirical evidence on both standpoints). MMN studies showed that prediction error is elicited with respect to high-level expectations; namely: by the violation of complex statistical regularities (see [80] for review), the omission of expected sounds [81][82][83],…”

“…However, whether SSA truly represents prediction error is unclear: its phenomenology can be explained by habituation to local stimulus statistics [18][19][20] (see [21] for review, and [22] for a different perspective). One way to disambiguate habituation and prediction error is to manipulate participants' subjective stimulus expectations orthogonally to local stimulus statistics [21,23]. Prediction error to subjective expectations has been studied measuring the mismatch negativity (MMN) [24][25][26][27][28][29][30], partially generated in the frontal cortex [31,32].…”