The nature of inner language has long been under the scrutiny of humanities, through the practice of introspection. The use of experimental methods in cognitive neurosciences provides complementary insights. This chapter focuses on wilful expanded inner language, bearing in mind that other forms coexist. It first considers the abstract vs. concrete (or embodied) dimensions of inner language. In a second section, it argues that inner language should be considered as an action-perception phenomenon. In a third section, it proposes a revision of the “predictive control” account, fitting with our sensory-motor view. Inner language is considered as deriving from multisensory goals, generating multimodal acts (inner phonation, articulation, sign) with multisensory percepts (in the mind’s ear, tact, and eye). In the final section, it presents a landscape of the cerebral substrates of wilful inner verbalization, including multisensory and motor cortices as well as cognitive control networks.

Purpose Bayesian multilevel models are increasingly used to overcome the limitations of frequentist approaches in the analysis of complex structured data. This tutorial introduces Bayesian multilevel modeling for the specific analysis of speech data, using the brms package developed in R. Method In this tutorial, we provide a practical introduction to Bayesian multilevel modeling by reanalyzing a phonetic data set containing formant (F1 and F2) values for 5 vowels of standard Indonesian (ISO 639-3:ind), as spoken by 8 speakers (4 females and 4 males), with several repetitions of each vowel. Results We first give an introductory overview of the Bayesian framework and multilevel modeling. We then show how Bayesian multilevel models can be fitted using the probabilistic programming language Stan and the R package brms, which provides an intuitive formula syntax. Conclusions Through this tutorial, we demonstrate some of the advantages of the Bayesian framework for statistical modeling and provide a detailed case study, with complete source code for full reproducibility of the analyses ( https://osf.io/dpzcb /). Supplemental Material https://doi.org/10.23641/asha.7973822

. 2 15 Rumination is predominantly experienced in the form of repetitive verbal thoughts. Verbal 16 rumination is a particular case of inner speech. According to the Motor Simulation view, inner 17 speech is a kind of motor action, recruiting the speech motor system. In this framework, we 18 predicted an increase in speech muscle activity during rumination as compared to rest. We also 19 predicted increased forehead activity, associated with anxiety during rumination. We measured 20 electromyographic activity over the orbicularis oris superior and inferior, frontalis and flexor 21 carpi radialis muscles. Results showed increased lip and forehead activity after rumination 22 induction compared to an initial relaxed state, together with increased self-reported levels of 23 rumination. Moreover, our data suggest that orofacial relaxation is more effective in reducing 24 rumination than non-orofacial relaxation. Altogether, these results support the hypothesis that 25 verbal rumination involves the speech motor system, and provide a promising 26 psychophysiological index to assess the presence of verbal rumination. 27