Humans have the ability to mentally examine speech. This covert form of speech production is often accompanied by sensory (e.g., auditory) percepts. However, the cognitive and neural mechanisms that generate these percepts are still debated. According to a prominent proposal, inner speech has at least two distinct phenomenological components: inner speaking and inner hearing. We used transcranial magnetic stimulation to test whether these two phenomenologically distinct processes are supported by distinct neural mechanisms. We hypothesised that inner speaking relies more strongly on an online motor-to-sensory simulation that constructs a multisensory experience, whereas inner hearing relies more strongly on a memory-retrieval process, where the multisensory experience is reconstructed from stored motor-to-sensory associations. Accordingly, we predicted that the speech motor system will be involved more strongly during inner speaking than inner hearing. This would be revealed by modulations of TMS evoked responses at muscle level following stimulation of the lip primary motor cortex. Overall, data collected from 31 participants corroborated this prediction, showing that inner speaking increases the excitability of the primary motor cortex more than inner hearing. Moreover, this effect was more pronounced during the inner production of a syllable that strongly recruits the lips (vs. a syllable that recruits the lips to a lesser extent). These results are compatible with models assuming a central role of the primary motor cortex for inner speech production and contribute to clarify the neural implementation of the fundamental ability of silently speaking in one’s mind.

Short journal club about the unexpected consequences of randomisation in psychological research.

Covert speech is accompanied by a subjective multisensory experience with auditory and kinaesthetic components. An influential hypothesis states that these sensory percepts result from a simulation of the corresponding motor action that relies on the same internal models recruited for the control of overt speech. This simulationist view raises the question of how it is possible to imagine speech without executing it. In this perspective, we discuss the possible role(s) played by motor inhibition during covert speech production. We suggest that considering covert speech as an inhibited form of overt speech maps naturally to the purported progressive internalisation of overt speech during childhood. We further argue that the role of motor inhibition may differ widely across different forms of covert speech (e.g., condensed vs. expanded covert speech) and that considering this variety helps reconciling seemingly contradictory findings from the neuroimaging literature.

Despite many cultural, methodological, and technical improvements, one of the major obstacle to results reproducibility remains the pervasive low statistical power. In response to this problem, a lot of attention has recently been drawn to sequential analyses. This type of procedure has been shown to be more efficient (to require less observations and therefore less resources) than classical fixed-N procedures. However, these procedures are submitted to both intrapersonal and interpersonal biases during data collection and data analysis. In this tutorial, we explain how automation can be used to prevent these biases. We show how to synchronise open and free experiment software programs with the Open Science Framework and how to automate sequential data analyses in R. This tutorial is intended to researchers with beginner experience with R but no previous experience with sequential analyses is required.

Although having a long history of scrutiny in experimental psychology, it is still controversial whether wilful inner speech (covert speech) production is accompanied by specific activity in speech muscles. We present the results of a preregistered experiment looking at the electromyographic correlates of both overt speech and inner speech production of two phonetic classes of nonwords. An automatic classification approach was undertaken to discriminate between two articulatory features contained in nonwords uttered in both overt and covert speech. Although this approach led to reasonable accuracy rates during overt speech production, it failed to discriminate inner speech phonetic content based on surface electromyography signals. However, exploratory analyses conducted at the individual level revealed that it seemed possible to distinguish between rounded and spread nonwords covertly produced, in two participants. We discuss these results in relation to the existing literature and suggest alternative ways of testing the engagement of the speech motor system during wilful inner speech production.

Inner speech is accompanied by a subjective multisensory experience. However, the origins (causes) of this subjective experience are debated. In this study, we used transcranial magnetic stimulation to disentangle the predictions of competing theoretical frameworks.

Motor imagery is accompanied by a subjective multisensory experience. This sensory experience is thought to result from internal models that control the execution of overt actions. If so, how is it that motor imagery does not to lead to overt execution?

With the aim of moving beyond mindless statistics, Wasserstein, Schirm, & Lazar (2019) formulated the ATOM guidelines: ‘Accept uncertainty. Be thoughtful, open, and modest.’ In this talk, I explore some consequences of these guidelines when applied to the analysis of empirical data, in the light of core concepts from the philosophy of statistics.

A gentle conceptual and practical primer to Bayesian multilevel models using R, brms, and Stan.

The second part of my compiled reading notes on Meehl’s metatheory and related meta-peregrinations.

My compiled reading notes on Meehl’s metatheory and related meta-peregrinations.

An attempt to illustrate what a Bayes factor looks like, using GIFs.

Why can’t we be more idiographic in our research? It is the individual organism that is the principle unit of analysis in the science of psychology (Barlow & Nock, 2009).

As put by Gelman et al. (2013, page 148): ‘because a probability model can fail to reflect the process that generated the data in any number of ways, posterior predictive p-values can be computed for a variety of test quantities in order to evaluate more than one possible model failure’.