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13 min read Philosophy of science, Meehl

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

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18 min read Philosophy of science, Meehl

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

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7 min read R, Visualisation, Bayes factor, Bayesian

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

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7 min read Tutorial, R, visualisation

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).

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9 min read Tutorial, R, Bayesian, assumptions, binomial, PPC

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’.

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Selected Publications

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.
Journal of Speech, Language, and Hearing Research, 2019.
Details Journal OSF project Preprint Supplementary materials

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.
Inner Speech: new voices, 2018.
Details Preprint Publisher

We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable.
Frontiers in Psychology, 2018.
Details Journal PDF Preprint

Rumination is predominantly experienced in the form of repetitive verbal thoughts. Verbal rumination is a particular case of inner speech. According to the Motor Simulation view, inner speech is a kind of motor action, recruiting the speech motor system. In this framework, we predicted an increase in speech muscle activity during rumination as compared to rest. We also predicted increased forehead activity, associated with anxiety during rumination. We measured electromyographic activity over the orbicularis oris superior and inferior, frontalis and flexor carpi radialis muscles. Results showed increased lip and forehead activity after rumination induction compared to an initial relaxed state, together with increased self-reported levels of rumination. Moreover, our data suggest that orofacial relaxation is more effective in reducing rumination than non-orofacial relaxation. Altogether, these results support the hypothesis that verbal rumination involves the speech motor system, and provide a promising psychophysiological index to assess the presence of verbal rumination.
Biological Psychology, 2017.
Details Journal Data Preprint

Teaching

I teach 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
  • PhD, all disciplines: Introduction to Bayesian statistical modelling

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