Ladislas Nalborczyk

Ladislas Nalborczyk

Postdoctoral researcher

LPC, CNRS UMR 7290

Aix-Marseille University

Research interests

Motor imagery and covert speech

I am a cognitive scientist interested in the conscious experience of imagined actions (motor imagery, covert speech) and their behavioural, psychophysiological, and neural correlates. In the fundamental part of my research, I am using a combination of behavioural, psychophysiological, and computational methods to evaluate predictions of motor control models applied to imagined actions. In the applied dimension of my research, I am using machine learning and deep artificial neural networks to decode the content of both overt and covert verbal actions from electrophysiological data.

Statistical modelling and slow science

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

Interests

  • Speech production
  • Covert speech
  • Motor imagery
  • Deep learning
  • Statistical modelling
  • Computational modelling

Education

  • 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

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.

Action effects on visual perception of distances: A multilevel Bayesian meta-analysis

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/).

An introduction to Bayesian multilevel models using brms: A case study of gender effects on vowel variability in standard Indonesian

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.

A Cognitive Neuroscience View of Inner Language: To Predict and to Hear, See, Feel

Inner verbalisation can be willful, when we deliberately engage in inner speech (e.g., mental rehearsing, counting, list making) or more involuntary, when unbidden verbal thoughts occur. It can either be expanded (fully phonologically specified) or condensed (cast in a prelinguistic format). Introspection and empirical data suggest that willful expanded inner speech recruits the motor system and involves auditory, proprioceptive, tactile as well as perhaps visual sensations. We present a neurocognitive predictive control model, in which willful inner speech is considered as deriving from multisensory goals arising from sensory cortices. An inverse model transforms desired sensory states into motor commands which are specified in motor regions and inhibited by prefrontal cortex. An efference copy of these motor commands is transformed by a forward model into simulated multimodal acts (inner phonation, articulation, gesture). These simulated acts provide predicted multisensory percepts that are processed in sensory regions and perceived as an inner voice unfolding over time. The comparison between desired sensory states and predicted sensory end states provides the sense of agency, of feeling in control of one’s inner speech. Three types of inner verbalisation can be accounted for in this framework: unbidden thoughts, willful expanded inner speech, and auditory verbal hallucination.

Recent & Upcoming Talks

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.

How to avoid common misinterpretations of p-values and Bayes factors

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

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’.

Teaching

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 am also teaching the following doctoral course once a year at Univ. Grenoble Alpes:

  • PhD students, all disciplines: Introduction to Bayesian statistical modelling. 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.

Contact