@article{Zabbah2026.01.28.701672,

title = {An integrated virtual reality platform for naturalistic neuroimaging with magnetoencephalography},

author = {Sajjad Zabbah and Nicholas A Alexander and Yousef Mohammadi and Alberto Mariola and Robert A Seymour and Sahitya Puvvada and Gareth R Barnes and Dominik R. Bach},

url = {https://www.biorxiv.org/content/early/2026/01/30/2026.01.28.701672},

doi = {10.64898/2026.01.28.701672},

year  = {2026},

date = {2026-01-01},

journal = {bioRxiv},

publisher = {Cold Spring Harbor Laboratory},

abstract = {Studying the brain in motion promises deep insights into the neural circuits that support complex, real-world behaviour. In humans, wearable optically pumped magnetometers (OPMs) enable magnetoencephalography (MEG) with millisecond temporal resolution and millimetre spatial precision during movement. Integrating this technology with virtual reality (VR) could enable fully naturalistic experimental paradigms, but magnetic interference from existing head-mounted displays (HMDs) prevents reliable whole-brain MEG recordings. Here, we present and validate a VR system that integrates with wearable, OPM-based MEG. At its core is a purpose-designed HMD with minimal ferromagnetic material, resulting in magnetic flux density two orders of magnitude lower than consumer-grade alternatives at comparable resolution and weight. Using phantom measurements and established perceptual and cognitive benchmark tasks across participants, we demonstrate robust stimulus-induced neuronal activity at both sensor and source level. Crucially, these sources span the entire brain, including visual, motor and prefrontal cortices, as well as hippocampus. Our proposed VR system is straightforward to produce, readily extendable, and enables whole-brain MEG during immersive, naturalistic behaviours.Competing Interest StatementThe authors have declared no competing interest.European Research Council, https://ror.org/0472cxd90, ERC-2018 CoG-816564 ActionContraThreatWellcome Trust, 226793/Z/22/ZMinistry of Culture and Science of the State of North Rhine-Westphalia, Germany, iBehave, PB22-063A InVirtuo 4.0},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BROCHARD2025106213,

title = {Critical intelligence: Computing defensive behaviour},

author = {Jules Brochard and Peter Dayan and Dominik R. Bach},

url = {https://www.sciencedirect.com/science/article/pii/S0149763425002131},

doi = {https://doi.org/10.1016/j.neubiorev.2025.106213},

issn = {0149-7634},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Neuroscience & Biobehavioral Reviews},

volume = {174},

pages = {106213},

abstract = {Characterising the mechanisms underlying naturalistic defensive behavior remains a significant challenge. While substantial progress has been made in unravelling the neural basis of tightly constrained behaviors, a critical gap persists in our comprehension of the circuits that implement algorithms capable of generating the diverse defensive responses observed outside experimental restrictions. Recent advancements in neuroscience technology now allow for an unprecedented examination of naturalistic behaviour. To help provide a theoretical grounding for this nascent experimental programme, we summarise the main computational and statistical challenges of defensive decision making, encapsulated in the concept of critical intelligence. Next, drawing from an extensive literature in biology, machine learning, and decision theory, we explore a range of candidate solutions to these challenges. While the proposed solutions offer insights into potential adaptive strategies, they also present inherent trade-offs and limitations in their applicability across different biological contexts. Ultimately, we propose series of experiments designed to differentiate between these candidate solutions, providing a roadmap for future investigations into the fundamental defensive algorithms utilized by biological agents and their neural implementation. Thus, our work aims to provide a roadmap towards broader understanding of how complex defensive behaviors are orchestrated in the brain, with implications for both neuroscience research and the development of more sophisticated artificial intelligence systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BACH2025102950,

title = {Experiment-based calibration in psychology: Foundational and data-generating model},

author = {Dominik R. Bach},

url = {https://www.sciencedirect.com/science/article/pii/S0022249625000513},

doi = {https://doi.org/10.1016/j.jmp.2025.102950},

issn = {0022-2496},

year  = {2025},

date = {2025-01-01},

journal = {Journal of Mathematical Psychology},

volume = {127},

pages = {102950},

abstract = {Experiment-based calibration is a novel method for measurement validation, which – unlike classical validity metrics – does not require stable between-person variance. In this approach, the latent variable to be measured is manipulated by an experiment, and its predicted scores – termed standard scores – are compared against the measured scores. Previous work has shown that under plausible boundary conditions, the correlation between standard and measured scores – termed retrodictive validity – is informative about measurement accuracy, i.e. combined trueness and precision. Here, I expand these findings in several directions. First, I formalise the approach in a probability-theoretic framework with the concept of a standardised calibration space. Second, I relate this framework to classical validity theory and show that the boundary conditions in fact apply to any form of criterion validity, including classical convergent validity. Thus, I state precise and empirically quantifiable boundary conditions under which criterion validity metrics are informative on validity. Third, I relate these boundary conditions to confounding variables, i.e. correlated latent variables. I show that in the limit, calibration will converge on the latent variable that is most closely related to the standard. Finally, I provide a framework for modelling the data-generating process with Markov kernels, and identify sufficient conditions under which the data generation model results in a calibration space. In sum, this article provides a formal probability-theoretic framework for experiment-based calibration and facilitates modelling and empirical assessment of the data generating processes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mancinelli2024,

title = {Dimensionality and optimal combination of autonomic fear-conditioning measures in humans},

author = {Federico Mancinelli and Juliana K. Sporrer and Vladislav Myrov and Filip Melinscak and Josua Zimmermann and Huaiyu Liu and Dominik R. Bach},

url = {https://doi.org/10.3758/s13428-024-02341-3},

doi = {10.3758/s13428-024-02341-3},

issn = {1554-3528},

year  = {2024},

date = {2024-09-01},

journal = {Behavior Research Methods},

volume = {56},

number = {6},

pages = {6119–6129},

abstract = {Fear conditioning, also termed threat conditioning, is a commonly used learning model with clinical relevance. Quantification of threat conditioning in humans often relies on conditioned autonomic responses such as skin conductance responses (SCR), pupil size responses (PSR), heart period responses (HPR), or respiration amplitude responses (RAR), which are usually analyzed separately. Here, we investigate whether inter-individual variability in differential conditioned responses, averaged across acquisition, exhibits a multi-dimensional structure, and the extent to which their linear combination could enhance the precision of inference on whether threat conditioning has occurred. In a mega-analytic approach, we re-analyze nine data sets including 256 individuals, acquired by the group of the last author, using standard routines in the framework of psychophysiological modeling (PsPM). Our analysis revealed systematic differences in effect size between measures across datasets, but no evidence for a multidimensional structure across various combinations of measures. We derive the statistically optimal weights for combining the four measures and subsets thereof, and we provide out-of-sample performance metrics for these weights, accompanied by bias-corrected confidence intervals. We show that to achieve the same statistical power, combining measures allows for a relevant reduction in sample size, which in a common scenario amounts to roughly 24%. To summarize, we demonstrate a one-dimensional structure of threat conditioning measures, systematic differences in effect size between measures, and provide weights for their optimal linear combination in terms of maximal retrodictive validity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}