A Subtype of Ultrasonic Vocalizations during Palatable Food Consumption in Rats Identified by Machine Learning-Assisted Classification.

Spatial cognition, the ability to acquire, organize, exploit, and update knowledge about external space, is fundamental to both human beings and artificial intelligence (AI). It underlies not only sensorimotor skills such as navigation and manipulation but also supports higher-level cognitive functions, including abstraction, planning, and reasoning. In humans, generalizable spatial representations support interpretation of sensory input, anticipation of future events, and flexible adaptation to changing environments, from brewing coffee in a familiar kitchen to navigating an unfamiliar city. Given its broad relevance in embodied interaction with the real physical world, spatial cognition has emerged as a central theme across disciplines, driving advances in robotics, urban simulation, and planetary-scale modeling, while increasingly recognized as a foundational component of artificial general intelligence (AGI). Despite the rapid progress in multi-modal large language models (MLLMs) and growing efforts to equip embodied agents with visual-language reasoning, current artificial systems remain fundamentally limited in large-scale spatial cognition, especially in tasks requiring long-horizon navigation and mobile manipulation. A central bottleneck lies in the lack of structured spatial memory, a mechanism for persistently encoding, organizing, and retrieving spatial knowledge about the environment. Most existing methods, whether based on end-to-end reinforcement learning or modular pipelines with powerful MLLMs, process observations in a reactive and stateless manner. Without durable internal models of external space, agents struggle to consolidate coherent spatial representations or perform reasoning beyond immediate stimuli, resulting in fragmented knowledge, short-sighted planning, and poor generalization. Addressing these limitations requires a paradigm shift from reactive processing to memory-centric spatial cognition, which supports persistent representations and compositional reasoning over time and space. Compared to artificial systems, biological spatial cognition offers a robust and compelling template. Decades of neuroscience research revealed that organisms consolidate spatial knowledge into three distinct yet interconnected forms (Fig. ): landmarks, which encode stable associations of salient environmental cues to support localization and contextual understanding; route knowledge, which captures egocentric movement trajectories between landmarks for habitual navigation and path integration; and survey knowledge, which integrates multiple routes into allocentric, map-like representations that support flexible inference, shortcut discovery, and detour planning. These spatial representations are accessed and coordinated via working memory, especially visual-spatial working memory, enabling adaptive retrieval, composition, and generalization based on task demands and environmental familiarity.