A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP

deBergh, D. van; vanDoorn, J.; Marsman, Maarten; Draws, Tim; Kesteren, E.; Derks, Koen; Dablander, Fabian; Gronau, Quentin Frederik; Kucharský, Šimon; Gupta, Akanksha; Sarafoglou, Alexandra; Voelkel, Jan G.; Stefan, Angelika; Ly, Alexander; Hinne, Max; Matzke, Dora; Wagenmakers, Eric Jan

doi:10.3917/anpsy1.201.0073

Cited by 180 publications

(82 citation statements)

References 38 publications

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“…The analysis was conducted using JASP software version 0.14 (JASP Team, 2020; https://jaspstats.org/; for theoretical and practical introductions see Dablander et al, 2020;Faulkenberry et al, 2020;Keysers et al, 2020;Wagenmakers, Love, et al, 2018;Wagenmakers, Marsman, et al, 2018). The Bayesian repeated measures ANOVA included Grammaticality and TMS as factors.…”

Section: Bayesian Repeated Measures Anova On the Esn Amplitudementioning

confidence: 99%

Towards a causal role of Broca’s area in language: A TMS-EEG study on syntactic prediction

Maran

Numssen

Hartwigsen

et al. 2021

Preprint

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Categorical predictions have been proposed as the key mechanism supporting the fast pace of syntactic composition in human language. Accordingly, grammar-based expectations facilitate the analysis of incoming syntactic information - e.g., hearing the determiner 'the' enhances the prediction of a noun - which is then checked against a single or few other word categories. Previous functional neuroimaging studies point towards Broca's area in the left inferior frontal gyrus (IFG) as one fundamental cortical region involved in categorical prediction during on-line language processing. Causal evidence for this hypothesis is however still missing. In this study, we combined Electroencephalography (EEG) and Transcranial Magnetic Stimulation (TMS) to test whether Broca's area is functionally relevant in predictive mechanisms for language. Specifically, we transiently perturbed Broca's area during the categorical prediction phase in two-word constructions, while simultaneously measuring the Event-Related Potential (ERP) correlates of syntactic composition. We reasoned that if Broca's area is involved in predictive mechanisms for syntax, disruptive TMS during the processing of the first word (determiner/pronoun) would mitigate the difference in ERP responses for predicted and unpredicted categories when composing basic phrases and sentences. Contrary to our hypothesis, perturbation of Broca's area at the predictive stage did not affect the ERP correlates of basic composition. The correlation strength between the electrical field induced by TMS and the magnitude of the EEG response on the scalp further confirmed this pattern. We discuss the present results in light of an alternative account of the role of Broca's area in syntactic composition, namely the bottom-up integration of words into constituents.

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Section: Bayesian Repeated Measures Anova On the Esn Amplitudementioning

confidence: 99%

Towards a causal role of Broca’s area in language: A TMS-EEG study on syntactic prediction

Maran

Numssen

Hartwigsen

et al. 2021

Preprint

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“…Importantly, models that are averaged in this way provide evidence that is corrected for multiple b All correlation tests used JASP's default r prior: a uniform distribution spanning the values between -1 and 1. testing 45,46 . These Bayesian analyses allowed us to directly test whether v.avg and PC1 contain unique versus redundant predictive information by quantifying evidence for a model that contains both variables relative to simpler models that contain only one of each.…”

Section: Inferential Analysesmentioning

confidence: 99%

Evidence accumulation and associated error-related brain activity as computationally-informed prospective predictors of substance use in emerging adulthood

Weigard

Brislin

Cope

et al. 2020

Preprint

Self Cite

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Substance use peaks during the developmental period known as emerging adulthood (roughly ages 18-25), but not every individual who uses substances during this period engages in frequent or problematic use. Previous studies have suggested that individual differences in neurocognition may prospectively predict problematic substance use, but mechanistic neurocognitive risk factors with clear links to both behavior and neural circuitry have not yet been identified. Here we take an approach rooted in computational psychiatry, an emerging field in which formal models of neurocognition are used to identify candidate biobehavioral dimensions that confer risk for psychopathology. Specifically, we test whether lower efficiency of evidence accumulation (EEA), a computationally tractable process that drives neurocognitive performance across many tasks, is a risk factor for substance use in emerging adults. In an fMRI substudy within a sociobehavioral longitudinal study (n=106), we find that lower EEA and reductions in a robust neural-level correlate of EEA (error-related activations in salience network and parietal structures) measured at ages 18-21 are both prospectively related to higher levels of substance use during ages 22-26, even after adjusting for other well-known risk factors. Results from Bayesian model comparisons corroborated inferences from conventional hypothesis testing and provided evidence that both EEA and its neural correlates contain unique predictive information about substance use involvement. Overall, these findings suggest that EEA is a mechanistic, computationally tractable neurocognitive risk factor for substance use at a critical developmental period, with clear links to both neural correlates and well-established formal theories of brain function.

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“…We reported results of the Bayesian ANOVA as analysis of effects [107]. That is, evidence for an effect (i.e., a main effect of error type or group or the interaction of error type and group) in the data was quantified by inclusion Bayes factors (BF inclusion ).…”

Section: Error Analysismentioning

confidence: 99%

A Computational Study of Executive Dysfunction in Amyotrophic Lateral Sclerosis

Steinke

Lange

Seer

et al. 2020

JCM

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Executive dysfunction is a well-documented, yet nonspecific corollary of various neurological diseases and psychiatric disorders. Here, we applied computational modeling of latent cognition for executive control in amyotrophic lateral sclerosis (ALS) patients. We utilized a parallel reinforcement learning model of trial-by-trial Wisconsin Card Sorting Test (WCST) behavior. Eighteen ALS patients and 21 matched healthy control participants were assessed on a computerized variant of the WCST (cWCST). ALS patients showed latent cognitive symptoms, which can be characterized as bradyphrenia and haphazard responding. A comparison with results from a recent computational Parkinson’s disease (PD) study (Steinke et al., 2020, J Clin Med) suggests that bradyphrenia represents a disease-nonspecific latent cognitive symptom of ALS and PD patients alike. Haphazard responding seems to be a disease-specific latent cognitive symptom of ALS, whereas impaired stimulus-response learning seems to be a disease-specific latent cognitive symptom of PD. These data were obtained from the careful modeling of trial-by-trial behavior on the cWCST, and they suggest that computational cognitive neuropsychology provides nosologically specific indicators of latent facets of executive dysfunction in ALS (and PD) patients, which remain undiscoverable for traditional behavioral cognitive neuropsychology. We discuss implications for neuropsychological assessment, and we discuss opportunities for confirmatory computational brain imaging studies.

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A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP

Cited by 180 publications

References 38 publications

Towards a causal role of Broca’s area in language: A TMS-EEG study on syntactic prediction

Towards a causal role of Broca’s area in language: A TMS-EEG study on syntactic prediction

Evidence accumulation and associated error-related brain activity as computationally-informed prospective predictors of substance use in emerging adulthood

A Computational Study of Executive Dysfunction in Amyotrophic Lateral Sclerosis

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