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1. Spatial Reasoning in Minecraft: An Exploratory Study of In-Game Spatial Practices

   Worsley, Marcelo; Bar-El, David (June 2020, Computersupported collaborative learning)

   Spatial reasoning is an important skillset that is malleable to training interventions. One possible context for intervention is the popular video game Minecraft. Minecraft encourages users to engage in spatial manipulation of 3D objects. However, few papers have chronicled any in-game practices that might evidence spatial reasoning, or how we might study its development through the game. In this paper, we report on 11 middle school students’ spatial reasoning practices while playing Minecraft. We use audio and video data of student gameplay to delineate five in-game practices that align with spatial reasoning. We expand on a student case study, to explicate these practices. The identified practices may be beneficial for studying spatial reasoning development in game-based environments and contribute to a growing body of research on ways games support development of important and transferable skills. 

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2. Mapping spatial gradients in spatial transcriptomics data with score matching

   https://doi.org/10.1101/2025.11.24.690257  

   Wang, An; Geman, Donald; Chitra, Uthsav; Younes, Laurent (November 2025, bioRxiv)

   Abstract Spatial transcriptomics (ST) technologies measure gene expression at thousands of locations within a two-dimensional tissue slice, enabling the study of spatial gene expression patterns. Spatial variation in gene expression is characterized byspatial gradients, or the collection of vector fields describing the direction and magnitude in which the expression of each gene increases. However, the few existing methods that learn spatial gradients from ST data either make restrictive and unrealistic assumptions on the structure of the spatial gradients or do not accurately model discrete transcript locations/counts. We introduce SLOPER (for Score-based Learning Of Poisson-modeled Expression Rates), a generative model for learning spatial gradients (vector fields) from ST data. SLOPER models the spatial distribution of mRNA transcripts with aninhomogeneous Poisson point process (IPPP)and usesscore matchingto learn spatial gradients for each gene. SLOPER utilizes the learned spatial gradients in a novel diffusion-based sampling approach to enhance the spatial coherence and specificity of the observed gene expression measurements. We demonstrate that the spatial gradients and enhanced gene expression representations learned by SLOPER leads to more accurate identification of tissue organization, spatially variable gene modules, and continuous axes of spatial variation (isodepth) compared to existing methods. Software availabilitySLOPER is available athttps://github.com/chitra-lab/SLOPER. 

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3. Spatial attention in natural tasks

   https://doi.org/10.12688/molpsychol.17488.1  

   Wohlgemuth, Melville; Salles, Angeles; Moss, Cynthia (January 2022, Molecular Psychology: Brain, Behavior, and Society)

   Little is known about fine scale neural dynamics that accompany rapid shifts in spatial attention in freely behaving animals, primarily because reliable indicators of attention are lacking in standard model organisms engaged in natural tasks.  The echolocating bat can serve to bridge this gap, as it exhibits robust dynamic behavioral indicators of overt spatial attention as it explores its environment.  In particular, the bat actively shifts the aim of its sonar beam to inspect objects in different directions, akin to eye movements and foveation in humans and other visually dominant animals. Further, the bat adjusts the temporal features of sonar calls to attend to objects at different distances, yielding a metric of acoustic gaze along the range axis. Thus, an echolocating bat’s call features not only convey the information it uses to probe its surroundings, but also provide fine scale metrics of auditory spatial attention in 3D natural tasks. These explicit metrics of overt spatial attention can be leveraged to uncover general principles of neural coding in the mammalian brain. 

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4. Spatial Models in Population Processes

   Molchanov, S.; Whitmeyer, J. (January 2017, Proceedings Volume “Selected Contributions in Honor of Valentin Konakov")
5. Spatial Task-Explicity Matters in Prompting Large Multimodal Models for Spatial Planning

   https://doi.org/10.1145/3687123.3698293  

   Majic, Ivan; Wang, Zhangyu; Janowicz, Krzysztof; Karimi, Mina (November 2024, ACM)