How working memory (WM) resists perceptual distraction with its limited capacity is a fundamental question to understand its mechanism. To address this question, we used a continuous recall paradigm to directly compare the distraction effect during encoding and the delay periods. Across Experiments 1-3, we observed a substantial distraction-related cost on mnemonic fidelity when distractors presented during the delay (delaydistraction condition), but not if they were introduced at encoding (encoding-distraction condition) or across both periods (full-distraction condition). However, the distraction cost revived when we increased the difficulty to distinguish distractors from targets (Experiments 4 and 4S) and when we changed distractors' relevant features during the delay (Experiment 5). We also found that the robust distraction cost in the delay-distraction condition did not occupy extra spatial resources (Experiments 6a and 6b). These results suggested a dissociated distraction effect, which could be related to the dynamic resource allocation across two WM periods. Here, we proposed a Bayesian model and considered the task relevance and visual uncertainty as two main factors that determine the resource allocation principle at two different stages. This model successfully captured the main findings across all behavioral experiments and performed better than other alternative models. Taken together, the current work advanced our understanding of the distraction resistance of WM under the framework of limited resource allocation.

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Abstract-A new mine transient electromagnetic method based on U-shape spiral source is proposed. Firstly, the method is introduced. The character of new system is based on a U-shape spiral source which is made up of a U-shape support and the spiral power wire. Furthermore, we analyze the potential advantages of the new method. Finally, we present the observation data measured using the proposed method, which makes a foundation for the following study on data processing and interpretation.

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Handling imperfect information problems is fundamental to perception, learning, and decision-making. Ensemble perception may partially overcome imperfect information by providing global clues. However, if not all cluster elements are readily accessible, the observations required for computing statistics are incomplete. In this case, these elements' internal correlations (i.e., regularity) could serve as clues to elucidate the missing pieces. We thus investigated spatial regularity's role in ensemble perception under imperfect information situations created using partially occluded stimuli. In two experiments, we manipulated circle size (Experiment 1) and line orientation (Experiment 2) to linearly vary with its location; spatial regularity thus supplied clues for inferring information of the invisible parts. Participants estimated the mean of the targeted feature of the entire cluster, including visible and invisible parts. We observed robust biases toward the overall cluster in the estimations, implying the invisible parts were considered during ensemble perception. We proposed this effect could be understood as assessing evidence from visible parts to construct the missing parts. Experiment 3 employed a periodicity regularity to deter participants from using specific strategies, and consistent results were found. We then developed a generative model, the Regularity-Based Model, to simulate the inference process, which better captured the pattern of human outcomes than the comparative model. These findings indicate the visual system could use high-level structural information to infer scenes with incomplete information, thus producing more accurate ensemble representations. Public Significance StatementThe human visual system frequently encounters imperfect information problems when perceiving the external world. The current study offers insights into how regularity in a scene can help humans fill the gaps in visual information and form a complete representation of the world. Specifically, our results demonstrated that humans are capable of inferring the missing part of a partly-occluded ensemble by utilizing regularity clues, such as linearity or periodicity of the visible parts, to produce a complete representation of the whole ensemble. These findings highlight the visual system's ability to fill in missing information during ensemble perception.