Navigating daily tasks relies on working memory to recall past information, manage current goals, and plan future actions. A key factor influencing the dynamic allocation and updating of cognitive resources is an individual's state relative to their goals. For instance, when following a mental shopping list, decisions about which item to prioritize depend largely on the person's location. In real-world scenarios, agent states are dynamic and often disrupted by urgent distractions or important interruptions. How people adapt to such perturbations remains unclear because studies of distraction resilience in working memory typically fix agent states. To address this, we developed a working memory paradigm inspired by the arcade game Snake, simulating agent movements in a dynamic environment. Participants (N = 50) controlled a snake in a rectangular field to locate memorized targets (apples) and earn points. Each trial required encoding 1, 2, or 4 apple locations, followed by memory-guided navigation to capture all apples. In half of the trials, distractions (the sudden appearance of additional target grapes) required participants to deviate from their initial plans and collect grapes before resuming the search for hidden apples. Without distractions, participants prioritized nearby targets, using proximity as a cue for memory allocation. When distractions perturbed the agent's position, participants flexibly redistributed resources to prioritize targets nearer the updated position. This flexibility declined with higher memory loads, and critically, reliance on working memory following distraction was limited to a single item regardless of load. These findings reveal dynamic working memory redistribution as a mechanism that enables flexible but constrained resilience to distraction in dynamic environments.
Navigating daily tasks requires working memory to retain information, formulate plans, and execute goal-directed actions. While frequent distractions may momentarily disrupt planned actions, individuals typically exhibit the cognitive resilience necessary to regroup and resume goal-directed behavior. How people adapt t...
Ziyao Zhang, Jarrod A. Lewis-Peacock· bioRxiv· 0 citations
Mainstream robotic policies often adopt a Markovian formulation, but many complex real-world manipulation tasks are inherently non-Markovian, requiring long-horizon memory beyond the current observation. Existing memory mechanisms often rely on language summaries, growing visual windows, or their combinations, and may...
Si-Zhe Zhao, Hao-Zhe Xie, Wei-Yu Zhao et al.· 0 citations
PMCoder is presented, an issue-resolution agent that couples a hierarchical phase planner with episodic memory that outperforms either component alone and reduces repeated failed actions, empty-patch exits, and context-window exhaustion.
Robot policies deployed in the wild should have the capability to continually learn new tasks without forgetting existing behaviors. A common approach to combat such catastrophic forgetting is to train on new task data with a replay buffer of previously learned task data. Although this buffer is commonly sampled random...
Maximilian Du, Zhanyi Sun, Chen Xu et al.· 0 citations
Mimir is introduced, a neuro-symbolic memory that separates world memory from task memory and dynamically grounds them before each action, substantially outperforming current closed-source models.
Haoming Xu, Zhen-Lin He, Heng-Yi Wang et al.· 0 citations
Effortless object finding by humans, even in cluttered or unseen environments, relies on the seamless integration of perception, memory, and contextual inference. In contrast, embodied robots operating under egocentric perception and partial observability frequently struggle with dynamic spatial relations and long-te...