Probabilistic Inference: Task Dependency and Individual Differences of Probability Weighting Revealed by Hierarchical Bayesian Modeling

The Effect of Transcutaneous Auricular Vagal Nerve Stimulation (taVNS) on P3 Event-Related Potentials during a Bayesian Oddball Task

Transcutaneous auricular Vagal Nerve Stimulation (taVNS) is a non-invasive brain stimulation technique associated with possible modulation of norepinephrinergic (NE) activity. NE is suspected to contribute to generation of the P3 event-related potential. Recent evidence has produced equivocal evidence whether taVNS influences the P3 in healthy individuals during oddball tasks. We examined the effect of taVNS on P3 amplitudes using a novel visual Bayesian oddball task, which presented 200 sequences of three stimuli. The three consecutive stimuli in each sequence are labelled Draw 1, Draw 2 and Draw 3. In total, 47 Subjects completed this visual Bayesian oddball task under randomised sham and active taVNS stimulation in parallel with an electroencephalographic (EEG) recording. We conducted exploratory analyses of the effect of taVNS on P3 amplitudes separately for Draws. We found typical oddball effects on P3 amplitudes at Draws 1 and 2, but not Draw 3. At Draw 2, the oddball effect was enhanced during active compared to sham taVNS stimulation. These data provide evidence that taVNS influences parietal P3 amplitudes under specific circumstances. Only P3 amplitudes at Draw 2 were affected, which may relate to closure of Bayesian inference after Draw 2. Our findings seemingly support previously reported links between taVNS and the NE system.

Mind the Noise When Identifying Computational Models of Cognition from Brain Activity

The aim of this study was to analyze how measurement error affects the validity of modeling studies in computational neuroscience. A synthetic validity test was created using simulated P300 event-related potentials as an example. The model space comprised four computational models of single-trial P300 amplitude fluctuations which differed in terms of complexity and dependency. The single-trial fluctuation of simulated P300 amplitudes was computed on the basis of one of the models, at various levels of measurement error and at various numbers of data points. Bayesian model selection was performed based on exceedance probabilities. At very low numbers of data points, the least complex model generally outperformed the data-generating model. Invalid model identification also occurred at low levels of data quality and under low numbers of data points if the winning model's predictors were closely correlated with the predictors from the data-generating model. Given sufficient data quality and numbers of data points, the data-generating model could be correctly identified, even against models which were very similar to the data-generating model. Thus, a number of variables affects the validity of computational modeling studies, and data quality and numbers of data points are among the main factors relevant to the issue. Further, the nature of the model space (i.e., model complexity, model dependency) should not be neglected. This study provided quantitative results which show the importance of ensuring the validity of computational modeling via adequately prepared studies. The accomplishment of synthetic validity tests is recommended for future applications. Beyond that, we propose to render the demonstration of sufficient validity via adequate simulations mandatory to computational modeling studies.

Logistic Mixed Models to Investigate Implicit and Explicit Belief Tracking

We investigated the proposition of a two-systems Theory of Mind in adults' belief tracking. A sample of N = 45 participants predicted the choice of one of two opponent players after observing several rounds in an animated card game. Three matches of this card game were played and initial gaze direction on target and subsequent choice predictions were recorded for each belief task and participant. We conducted logistic regressions with mixed effects on the binary data and developed Bayesian logistic mixed models to infer implicit and explicit mentalizing in true belief and false belief tasks. Although logistic regressions with mixed effects predicted the data well a Bayesian logistic mixed model with latent task- and subject-specific parameters gave a better account of the data. As expected explicit choice predictions suggested a clear understanding of true and false beliefs (TB/FB). Surprisingly, however, model parameters for initial gaze direction also indicated belief tracking. We discuss why task-specific parameters for initial gaze directions are different from choice predictions yet reflect second-order perspective taking.