Cortical Processing of Arithmetic and Simple Sentences in an Auditory Attention Task

Abstract

Cortical processing of arithmetic and of language rely on both shared and task-specific neural mechanisms, which should also be dissociable from the particular sensory modality used to probe them. Here, spoken arithmetical and non-mathematical statements were employed to investigate neural processing of arithmetic, compared with general language processing, in an attention-modulated cocktail party paradigm. Magnetoencephalography (MEG) data were recorded from 22 human subjects listening to audio mixtures of spoken sentences and arithmetic equations while selectively attending to one of the two speech streams. Short sentences and simple equations were presented diotically at fixed and distinct word/symbol and sentence/equation rates. Critically, this allowed neural responses to acoustics, words, and symbols to be dissociated from responses to sentences and equations. Indeed, the simultaneous neural processing of the acoustics of words and symbols was observed in auditory cortex for both streams. Neural responses to sentences and equations, however, were predominantly to the attended stream, originating primarily from left temporal, and parietal areas, respectively. Additionally, these neural responses were correlated with behavioral performance in a deviant detection task. Source-localized temporal response functions (TRFs) revealed distinct cortical dynamics of responses to sentences in left temporal areas and equations in bilateral temporal, parietal, and motor areas. Finally, the target of attention could be decoded from MEG responses, especially in left superior parietal areas. In short, the neural responses to arithmetic and language are especially well segregated during the cocktail party paradigm, and the correlation with behavior suggests that they may be linked to successful comprehension or calculation.

Temporal scaling of human scalp-recorded potentials

“…Our approach is not only conceptually different from previous work that models variability in timing using a regression framework [27,[34][35][36], it is also a mechanistically important finding. It indicates the brain may support flexible timing by adapting the duration of an otherwise consistent neural response.…”

“…Finally, the fixed-time effect of continuously varying stimuli (including fast/slow speech) on EEG can be quantified using the method of temporal response functions (TRFs), another regression-based approach [27,[34][35][36]. Critically, unlike the proposed method, these methods either fail to quantify a scaled signal (translation-based methods and TRFs) or involve scaling but no unmixing.…”

“…Finally, the effect of a continuous variable on EEG can be quantified using the method of temporal response functions (TRFs), another regression-based approach [27,[34][35][36]. TRFs are particularly flexible in capturing different types of delay activity, e.g., during periods of growing expectancy [34] or while listening to fast/slow speech [35]. Our method is related to TRFs in that it involves the convolution of a to-be-estimated input signal with a continuous regressor.…”

Editorial: Neural Tracking: Closing the Gap Between Neurophysiology and Translational Medicine

“…Nevertheless, a somewhat agnostic view on such underlying neural mechanisms would not prevent us from making valuable theoretical and practical use of such measurements. Work using such measures has already contributed to our understanding of speech (Mesgarani et al, 2014 ; Di Liberto et al, 2015 , 2021a ; Ding et al, 2015 ; Brodbeck et al, 2018 ; Broderick et al, 2018 ) and music perception (Tal et al, 2017 ; Di Liberto et al, 2020 , 2021b ; Marion et al, 2021 ; Zuk et al, 2021 ), selective attention (O'Sullivan et al, 2014 ; Decruy et al, 2020 ; Fuglsang et al, 2020 ), multisensory integration (Crosse et al, 2016 ; Sullivan et al, 2021 ), and even abstract cognitive processes such as arithmetic (Kulasingham et al, 2021 ). The work in this Research Topic attempts to portray a wide set of findings while using consistent terminology.…”

Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech

“…The a-priori parameters may need to be tuned for each predictor type or experiment, or even for each subject Modern TRF analyses involve multiple types of predictors [42] (e.g., envelopes, phoneme onsets, multiple frequency bands for spectrotemporal TRFs). Boosting and banded ridge regression may be suitable for these studies [10], [13], [43], [44]. The component characteristics of TRFs to these higher-level predictors must be determined before SP and EM-SP can be applied.…”