Leanne Hirshfield

Charlotte Genevier Wyman, Leanne Hirshfield

Interpretable brain-computer interface classifiers that generalize across subjects without calibration remain an open challenge. We test whether prototype-based cross-attention can provide competitive, interpretable event-related potential (ERP) classification under deployment-compatible conditions. We propose ERP-XTTN, a cross-attention architecture that routes input EEG patches to fixed difference-wave prototypes via query-key-only cross-attention with no value projection, so classification depends entirely on attention routing and attention faithfulness is structural rather than post-hoc. Prototypes are derived automatically from extrema in the training-fold difference wave. We evaluate across three public sources (BNCI Horizon 2020, HRI Cursor, and ERP CORE) spanning eight ERP components (ERN, LRP, ErrP, N170, P300, N2pc, MMN, N400), using leave-one-subject-out (LOSO) evaluation with causal filtering at two channel counts (3-channel and full montage), against EEGNet and xDAWN with Riemannian geometry (xDAWN+RG). The mean gap between the best baseline and ERP-XTTN was .018 AUROC at 3 channels and .034 at full montage, arising from two largely distinct sources: a temporal-flexibility cost relative to EEGNet and a spatial-exploitation cost relative to xDAWN+RG, the latter driven by signal-to-noise ratio at full montage. Beyond accuracy, the transparent routing reveals cross-subject signal structure that black-box models cannot: false positives resembled true positives more than true negatives did, indicating that classification errors are neurophysiologically explicable. ERP-XTTN generalizes across diverse ERPs under causal, calibration-free conditions with a small interpretability cost at minimal montages. To our knowledge, this is the first epoch-level LOSO benchmark on ERP CORE.

Cara A. Spencer, Christopher D. Wickens, Jalynn B. Nicoly et al.

Advance in technology offer the potential for future adoption of a combination of virtual reality (VR) and real-time adaptivity to enhance training and education. Providing a valid neuro-ergonomic measure of cognitive load can enable an adaptive training regime to continuously adjust tas difficulty to an optimal level as training progresses. The current study validated the functional near-infrared spectroscopy (fNIRS) measure of cognitive load to reflect the demands of two different forms of lad within Cognitive Load Theory: extraneous and intrinsic to he task to be mastered. Thirty-six participants completed a VR shape assembly training task followed by a test of their skill retention They wore near-full head coverage fNIRS and provided subjective ratings of ther workload. The fNIRS findings largely corroborate intrinsic workload literature with significant activation in cortical regions (dorsolateral and rostral prefrontal cortex and left angular gyrus) associated with working memory, short term memory buffers, multisensory integration, and attention. These fNIRS results were tracked closely by NASA TLS measures of mental workload. The results also revealed far less brain activity associated with extraneous load, namely just the right angular gyrus, deemed irrelevant to the mastery of the task.

Aaron R. Allred, Erin E. Richardson, Sarah R. Bostrom et al.

Technological systems increasingly mediate human information exchange, spanning interactions among humans as well as between humans and artificial agents. The unprecedented scale and reliance on information disseminated through these systems substantially expand the scope of information-based influence that can both enable and undermine sound decision-making. Consequently, understanding and protecting decision-making today faces growing challenges, as individuals and organizations must navigate evolving opportunities and information-based threats across varied domains and information environments. While these risks are widely recognized, research remains fragmented: work evaluating information-based threat phenomena has progressed largely in isolation from foundational studies of human information processing. In this review, we synthesize insights from both domains to identify shared cognitive mechanisms that mediate vulnerability to information-based threats and shape behavioral outcomes. Finally, we outline directions for future research aimed at integrating these perspectives, emphasizing the importance of such integration for mitigating human vulnerabilities and aligning human-machine representations.