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.

Previous studies have suggested that action constraints influence visual perception of distances. For instance, the greater the effort to cover a distance, the longer people perceive this distance to be. The present multilevel Bayesian meta-analysis (37 studies with 1,035 total participants) supported the existence of a small action-constraint effect on distance estimation, Hedges’s g = 0.29, 95% credible interval = [0.16, 0.47]. This effect varied slightly according to the action-constraint category (effort, weight, tool use) but not according to participants’ motor intention. Some authors have argued that such effects reflect experimental demand biases rather than genuine perceptual effects. Our meta-analysis did not allow us to dismiss this possibility, but it also did not support it. We provide field-specific conventions for interpreting action-constraint effect sizes and the minimum sample sizes required to detect them with various levels of power. We encourage researchers to help us update this meta-analysis by directly uploading their published or unpublished data to our online repository (https://osf.io/bc3wn/).

Bayesian multilevel models are increasingly used to overcome the limitations of frequentist approaches in the analysis of complex structured data. This paper introduces Bayesian multilevel modelling for the specific analysis of speech data, using the brms package developed in R. In this tutorial, we provide a practical introduction to Bayesian multilevel modelling, by reanalysing a phonetic dataset containing formant (F1 and F2) values for five vowels of Standard Indonesian (ISO 639-3:ind), as spoken by eight speakers (four females), with several repetitions of each vowel. We first give an introductory overview of the Bayesian framework and multilevel modelling. 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. Through this tutorial, we demonstrate some of the advantages of the Bayesian framework for statistical modelling and provide a detailed case study, with complete source code for full reproducibility of the analyses.

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.

A gentle conceptual and practical primer to the frequentist and Bayesian philosophies of statistics.

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’.