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传统心理测量学长期受自然科学范式影响，强调实体论意义上的真值探寻。面对智能时代的挑战，探针式测量范式难以充分解释技术对人的重塑作用。基于设计科学思想，心理测量学向工程科学范式拓展的可能性得到论证；通过重构“内部环境—界面—外部环境”的三元架构，心理测量学的本体论转换得以展开。在此基础上，测量被重新界定为连接个体心理黑箱与任务环境的人工界面，界面从生物具身耦合向智能时代人智协同认知界面跨越的演进逻辑得到系统梳理。该视角不仅为化解智能测量的黑箱危机提供本体论依据，也在界面设计层面促进测量学、实验心理学及人因科学的整合，为心理测量学从测评工具转向认知工程系统、推动心理学工学化转型奠定理论基础。

本研究以真实图片为刺激，通过视空间工作记忆范式探讨生命性效应对三种记忆的影响。实验1结果显示，个体对有生命物体图片的回忆在项目记忆中更佳，但在项目—位置联结记忆中则更差，在位置记忆中未呈现优劣势。这验证了生命性效应的不稳定性及在某些情境下的负面作用。实验2的两个子实验进一步引入情境因素，分别在自然场景与生活场景背景下，考察刺激与场景联系强度对生命性效应的边界条件。结果显示，生命性效应在两类场景下的视空间工作记忆模式高度相似；联系强度可提升项目记忆表现，但与场景强联系的物体在位置记忆和项目—位置联结记忆中未显示优势。上述结果表明，生命性效应在视空间工作记忆中具有细致的边界条件，未来研究需进一步探明影响该效应稳定性的深层因素。

工作记忆(WM)表征的存储是动态变化的, 这种动态变化如何影响视觉搜索尚不清楚。本研究通过在保持阶段呈现视觉脉冲刺激操纵WM表征激活, 探讨注意捕获范式下WM表征存储动态对注意引导的影响。结果发现, 呈现视觉脉冲刺激时基于WM的注意捕获效应低于不呈现该刺激; 视觉脉冲刺激呈现早时, 基于WM的注意捕获效应高于视觉脉冲刺激呈现晚和不呈现视觉脉冲刺激时。脑电解码发现, 呈现视觉脉冲刺激后, WM表征得到激活, 但随后被抑制, 同时自额顶区向枕区的Granger因果增强; 被抑制时段内表征解码正确率与基于WM的注意捕获效应负相关。研究表明, 视觉脉冲刺激可以激活WM表征, 且自上而下的认知控制及时抑制表征激活, 避免被动激活的表征捕获注意。

Current electroencephalography (EEG)-based dream detection relies on power spectral density (PSD) and statistical moment features, achieving a state-of-the-art area under the receiver operating characteristic curve (AUC) of approximately 0.70 on the DREAM database (Wong et al., 2025, Nature Communications). We introduce PHINN-EEG (Persistent Homology Inspired Neural Network for EEG), the first topological time-series framework for dream mentation analysis. Using sliding-window Takens delay embeddings and Vietoris-Rips filtrations on multichannel pre-awakening EEG epochs, we extract Dynamic Betti Curves that characterize the geometric architecture of neural activity, not merely its energy. These topological invariants, combined with topology-conditioned flow matching, are analytically projected to outperform existing PSD and catch22 benchmarks, targeting AUC = 0.82-0.90 on the 1,462-awakening open-access subset of the DREAM database (drawn from a full registry of 3,191 total awakenings from 263 participants across 20 independent laboratories). We further introduce a topology-conditioned rectified flow model for dream-state EEG synthesis-with a spectral-conditioned flow model of comparable feature dimensionality as an additional ablation baseline to isolate the value of topological conditioning specifically-and propose a set of candidate Betti transition archetypes linking topology to phenomenological dream report categories, presented as an exploratory hypothesis space pending empirical validation. If validated, this work represents a paradigm shift from spectral energy to phase-space geometry in neural rare-event detection, with potential future implications for wearable BCI dream monitoring.

In this work, we aim to address the challenge of long-range memory in panoramic world models by exploiting the rotation-equivariant property of omnidirectional representations, where rotation can be treated as an implicit geometric transformation.Building on this insight, we propose PanoWorld, which simplifies camera trajectories into translations via fixed headings for both current-action modeling and long-range memory through Dense Panoramic Ray-Conditioning (DPRC) and Geometry-aware Memory Augmentation (GMA).Then, a three-stage training pipeline is introduced to progressively optimize each component. To better evaluate physical consistency under large-scale spatial variations and diverse illumination conditions, where existing datasets are relatively stable, we construct World360, a large-scale dataset consisting of both real-world video clips collected via panoramic unmanned aerial vehicles and high-quality simulated clips generated by AirSim360.Extensive experiments on World360 demonstrate the effectiveness of PanoWorld, outperforming alternative methods by a large margin.Our models, training code, and dataset will be publicly available. More information can be found on our project page: https://lihaoy-ux.github.io/panoworld-page/.