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Introduction This simulation-based computational study develops a non-linear state-space framework for stress-related negative cognitive bias and multivariate EEG/ERP observations. Methods A three-state model represents negative bias, cognitive control, and arousal, while symptom load and normalized model stress input reshape a quartic bias potential. N2, P3, late positive potential, frontal theta, frontal alpha asymmetry (FAA), and frontoparietal phase locking provide complementary observation channels. Additional checks distinguish the non-linear benchmark from additive and interaction-only competing generators, examine equilibria of the full coupled system, and perturb the observation model. Results We establish bounded stochastic dynamics, a conditional scalar multistability boundary, stable-branch stress sensitivity, and a sufficient local state-observability condition. A seeded benchmark with 180 simulated participants yields model-implied stress-by-symptom slopes of 66.83 ms for response time, 1.12 μV for LPP, 0.775 dB for frontal theta, 0.133 for FAA, and 0.0769 for frontoparietal PLV. Under a non-linear generating process, mean held-out RMSE was 0.060 for the pre-specified quartic candidate vs. 0.436 for the interaction-only candidate. State-reconstruction RMSE increased from 0.152 under the nominal loading matrix to 0.232 under 15% loading perturbations. Discussion These results demonstrate implementation consistency and characterize model sensitivity; they are not clinical effect estimates or evidence of empirical parameter identifiability.

Introduction Food craving is a ubiquitous experience, often shaped by cultural familiarity. Although craving may be considered a culture-bound construct, its neural and behavioral underpinnings across food cultures remain limited. Here, we examined the effects of multisensory food exposure on craving in an Indian population, comparing Indian and Western foods across sweet and savory categories. Methods In Experiment 1 (Indian Food Craving; IFC), participants viewed Indian sweet and savory food images before and after multisensory exposure, reporting desire and willingness-to-pay (bid) ratings along with Food Craving Questionnaire-State (FCQ-S) scores. EEG was recorded using a 64-channel system. In Experiment 2 (Western Food Craving; WFC), participants completed a similar paradigm using Western food items. Results Multisensory exposure significantly increased FCQ-S scores in both experiments, indicating successful craving induction. Behaviorally, desire and bid ratings increased for both sweet and savory Indian foods, whereas for Western foods, increases were observed only for sweet items. ERP analyses revealed increased P200 and N200 amplitudes following exposure to Indian sweet foods, while LPP amplitudes increased for savory foods across both Indian and Western food categories. Complementary MVPA revealed enhanced pre- vs. post-exposure classification for sweet foods during the early (0–300 ms) window for both Indian and Western foods, whereas both sweet and savory foods showed significant classification during the later(300–600 ms) window. Discussion Together, these findings suggest that multisensory exposure modulates food-cue processing in a stimulus- and time-dependent manner, with possible contributions from attentional processes. Overall, the findings highlight the influence of food type and cultural context on craving-related neural and behavioral responses.

As human–AI cooperation becomes increasingly prevalent, reliable instruments for assessing the subjective quality of cooperative human–AI interaction are needed. We introduce two theoretically grounded scales: the Perceived Cooperativity Scale (PCS), grounded in joint activity theory, and the Teaming Perception Scale (TPS), grounded in evolutionary cooperation theory. The PCS captures an agent’s perceived cooperative capability and practice within a single interaction sequence; the TPS captures the emergent sense of teaming arising from mutual contribution and support. Both scales were adapted for human–human cooperation to enable cross-agent comparisons. Across three studies (complete N=409) encompassing a cooperative card game (N1=94), LLM interaction (N2=182), and a decision-support system (N3=133), analyses of dimensionality, reliability, and validity indicated that both scales successfully differentiated between cooperation partners of varying cooperative quality and showed construct validity in line with expectations. The scales provide a basis for empirical investigation and system evaluation across a wide range of human–AI cooperation contexts.

با گسترش روزافزون همکاری انسان‌-هوش مصنوعی، ابزارهای قابل اعتماد برای سنجش کیفیت ذهنی تعاملات تعاونی انسان‌-هوش مصنوعی مورد نیاز است. ما دو مقیاس نظریه‌محور معرفی می‌کنیم: مقیاس ادراک همکاری (PCS) مبتنی بر نظریه فعالیت مشترک و مقیاس ادراک تیمی (TPS) مبتنی بر نظریه تکاملی همکاری. PCS توانمندی و عملکرد تعاونی ادراک‌شده یک عامل را در یک توالی تعامل منفرد اندازه‌گیری می‌کند؛ TPS حس پدیداری تیمی ناشی از مشارکت و حمایت متقابل را می‌سنجد. هر دو مقیاس برای همکاری انسان‌-انسان نیز تطبیق داده شده‌اند تا امکان مقایسه بین عامل‌ها فراهم شود. در سه مطالعه (کل N=409) شامل یک بازی کارتی تعاونی (N1=94)، تعامل با LLM (N2=182) و یک سامانه پشتیبان تصمیم‌گیری (N3=133)، تحلیل‌های ابعادی، پایایی و روایی نشان داد که هر دو مقیاس به طور موفقیت‌آمیزی میان شرکای همکاری با کیفیت تعاونی متفاوت تمایز قائل می‌شوند و روایی سازه‌ای مطابق با انتظارات را نشان می‌دهند. این مقیاس‌ها مبنایی برای پژوهش‌های تجربی و ارزیابی سامانه‌ها در طیف وسیعی از زمینه‌های همکاری انسان‌-هوش مصنوعی فراهم می‌کنند.