🚨PREPRINT🚨 Prior expectations guide multisensory integration during face-to-face communication. w/ Giulia Mazzi and @stefbenetti @cimec_unitrento .🔗🧵👇.

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Always happy to get feedback. Give it a try and let us know! .🔗 GitHub project (with tutorial): 🔗 Sample data:

We also offer an illustrated tutorial with sample infant EEG data designed to support novice users, enhance accessibility and promote the transparent and reproducible usage of WTools.

We provide a detailed description of the WTools algorithm for wavelet transformation, and we compare it with established state-of-the-art methods.

While the default settings follow established pipelines in developmental research, WTools also provides the flexibility to accommodate settings more commonly used in adult neuroscience.

Being free and open-source, WTools can be integrated and expanded with new features, making it a handy tool that is growing its popularity in developmental cognitive neuroscience.

🚨NEW PAPER🚨 Happy to introduce WTools, a new toolbox for time-frequency analysis that is particularly suited for infant EEG. Work done in the @cimec_unitrento BabyLab.🔗

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These results prove that face-to-face communication is shaped by deeply ingrained prior expectations about how signals should be structured and interpreted. They also raise fascinating questions about the evolutionary, developmental, and cultural origins of these priors.

We found that people tend to combine vocal and bodily signals more strongly when they convey the same communicative intent. Thus, the brain uses prior expectations to bind multisensory signals that carry converging communicative meaning.

We tackled this issue by framing face-to-face communication as a Bayesian Causal Inference problem. Combining psychophysics with computational modelling, we formally tested whether prior expectations arbitrate between the integration and segregation of vocal and bodily signals.