Research interests

Mental production of speech (inner speech)

I am a computational cognitive neuroscientist interested in the conscious experience of inner speech and its neural underpinnings. My research combines experimental (e.g., psychophysics, EMG, MEG, TMS) and computational (e.g., mathematical modelling, deep neural networks) methods to understand how complex patterns of neural activity (in both biological and artificial neural networks) give rise to algorithms supporting the mental simulation of speech (inner speech).

Statistical modelling and slow science

In parallel, I also work on the development and dissemination of rigorous experimental and statistical methods. Besides, I feel very concerned about the issue of making our research more open, reproducible, and sustainable.


  • Inner speech
  • Motor control
  • Motor imagery
  • Time perception
  • Speech production
  • Psycholinguistics
  • Cognitive modelling
  • Statistical modelling


  • PhD in Cognitive Psychology, 2019

    Univ. Grenoble Alpes

  • PhD in Clinical and Experimental Psychology, 2019

    Ghent University

  • MSc in Cognitive Science, 2015

    Grenoble Institute of Technology

  • BA in Psychology, 2013

    Pierre-Mendès France University

Selected publications

Distinct neural mechanisms support inner speaking and inner hearing

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.

Time perception: When randomization hurts

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

The role of motor inhibition during covert speech production

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.

A fully automated, transparent, reproducible, and blind protocol for sequential analyses

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.

Can we decode phonetic features in inner speech using surface electromyography?

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.

Recent & Upcoming Talks

Uncovering the origins of inner speech sensory content

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 inhibition prevents execution during imagined typing: Evidence from an action‐mode switching paradigm

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?

Moving to a World beyond p < .05

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.

An introduction to Bayesian multilevel models using R, brms, and Stan

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

Recent Posts

The Meehlian Corroboration-Verisimilitude Theory of Science - Part II

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

The Meehlian Corroboration-Verisimilitude Theory of Science - Part I

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

What does a Bayes factor look like?

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

Visualising within-subject effects and stochastic dominance with (augmented) modified Brinley plots

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).

Checking the asumption of independence in binomial trials using posterior predictive checking

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’.


During my PhD, I have taught the following courses at Univ. Grenoble Alpes:

  • BA Psychology: Data analysis (ANOVA)
  • BA Psychology: Introduction to Cognitive Psychology
  • BA Psychology: Cognitive Psychology: perception, action and categorisation

Since 2017, I also teach the following doctoral course once a year at Univ. Grenoble Alpes:

  • PhD students, all disciplines: Introduction to Bayesian statistical modelling in R. Code and slides are available here (in French).

I regularly give workshops or short courses on Bayesian statistics in R. Do not hesitate to reach out if you would like to organise an event in your department.