A Feature-based Generalizable Prediction Model for Both Perceptual and Abstract Reasoning

A hallmark of human intelligence is the ability to infer abstract rules from limited experience and apply these rules to unfamiliar situations. This capacity is widely studied in the visual domain using the Raven's Progressive Matrices. Recent advances in deep learning have led to multiple artificial neural network models matching or even surpassing human performance. However, while humans can identify and express the rule underlying these tasks with little to no exposure, contemporary neural networks often rely on massive pattern-based training and cannot express or extrapolate the rule inferred from the task. Furthermore, most Raven's Progressive Matrices or Raven-like tasks used for neural network training used symbolic representations, whereas humans can flexibly switch between symbolic and continuous perceptual representations. In this work, we present an algorithmic approach to rule detection and application using feature detection, affine transformation estimation and search. We applied our model to a simplified Raven's Progressive Matrices task, previously designed for behavioral testing and neuroimaging in humans. The model exhibited one-shot learning and achieved near human-level performance in the symbolic reasoning condition of the simplified task. Furthermore, the model can express the relationships discovered and generate multi-step predictions in accordance with the underlying rule. Finally, the model can reason using continuous patterns. We discuss our results and their relevance to studying abstract reasoning in humans, as well as their implications for improving intelligent machines.

BU-CVKit: Extendable Computer Vision Framework for Species Independent Tracking and Analysis

A major bottleneck of interdisciplinary computer vision (CV) research is the lack of a framework that eases the reuse and abstraction of state-of-the-art CV models by CV and non-CV researchers alike. We present here BU-CVKit, a computer vision framework that allows the creation of research pipelines with chainable Processors. The community can create plugins of their work for the framework, hence improving the re-usability, accessibility, and exposure of their work with minimal overhead. Furthermore, we provide MuSeqPose Kit, a user interface for the pose estimation package of BU-CVKit, which automatically scans for installed plugins and programmatically generates an interface for them based on the metadata provided by the user. It also provides software support for standard pose estimation features such as annotations, 3D reconstruction, reprojection, and camera calibration. Finally, we show examples of behavioral neuroscience pipelines created through the sample plugins created for our framework.