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Abstract Scene memory has known spatial biases. Boundary extension is a well-known bias whereby observers remember visual information beyond an image’s boundaries. While recent studies demonstrate that boundary contraction also reliably occurs based on intrinsic image properties, the specific properties that drive the effect are unknown. This study assesses the extent to which scene memory might have a fixed capacity for information. We assessed both visual and semantic information in a scene database using techniques from image processing and natural language processing, respectively. We then assessed how both types of information predicted memory errors for scene boundaries using a standard rapid serial visual presentation (RSVP) forced error paradigm. A linear regression model indicated that memories for scene boundaries were significantly predicted by semantic, but not visual, information and that this effect persisted when scene depth was considered. Boundary extension was observed for images with low semantic information, and contraction was observed for images with high semantic information. This suggests a cognitive process that normalizes the amount of semantic information held in memory. 

Automatic scene classification has applications ranging from urban planning to autonomous driving, yet little is known about how well these systems work across social differences. We investigate explicit and implicit biases in deep learning architectures, including deep convolutional neural networks (dCNNs) and multimodal large language models (MLLMs). We examined nearly one million images from user-submitted photographs and Airbnb listings from over 200 countries as well as all 3320 US counties. To isolate scene-specific biases, we ensured no people were in any of the photos. We found significant explicit socioeconomic biases across all models, including lower classification accuracy, higher classification uncertainty, and increased tendencies to assign labels that could be offensive when applied to homes (e.g., “slum”) in images from homes with lower socioeconomic status. We also found significant implicit biases, with pictures from lower socioeconomic conditions more aligned with word embeddings from negative concepts. All trends were consistent across countries and within the diverse economic and racial landscapes of the United States. This research thus demonstrates a novel bias in computer vision, emphasizing the need for more inclusive and representative training datasets. 

Simultaneous head and eye tracking has traditionally been confined to a laboratory setting and real-world motion tracking limited to measuring linear acceleration and angular velocity. Recently available mobile devices such as the Pupil Core eye tracker and the Intel RealSense T265 motion tracker promise to deliver accurate measurements outside the lab. Here, the researchers propose a hard- and software framework that combines both devices into a robust, usable, low-cost head and eye tracking system. The developed software is open source and the required hardware modifications can be 3D printed. The researchers demonstrate the system’s ability to measure head and eye movements in two tasks: an eyes-fixed head rotation task eliciting the vestibulo-ocular reflex inside the laboratory, and a natural locomotion task where a subject walks around a building outside of the laboratory. The resultant head and eye movements are discussed, as well as future implementations of this system. 

Simultaneous head and eye tracking has traditionally been confined to a laboratory setting and real-world motion tracking limited to measuring linear acceleration and angular velocity. Recently available mobile devices such as the Pupil Core eye tracker and the Intel RealSense T265 motion tracker promise to deliver accurate measurements outside the lab. Here, the researchers propose a hard- and software framework that combines both devices into a robust, usable, low-cost head and eye tracking system. The developed software is open source and the required hardware modifications can be 3D printed. The researchers demonstrate the system’s ability to measure head and eye movements in two tasks: an eyes-fixed head rotation task eliciting the vestibulo-ocular reflex inside the laboratory, and a natural locomotion task where a subject walks around a building outside of the laboratory. The resultant head and eye movements are discussed, as well as future implementations of this system.