Proceedings of the Annual Meeting of the Cognitive Science Society

Neural Probabilistic Learner and Sampler (NPLS) is an algorithm that has simulated children’s non-symbolic probability learning from visual stimuli such as collections of different colors of marbles. Although NPLS closely simulates the cognitive process of probability learning, the training of such learning algorithms often uses binary encoding of inputs that represent the perceived visual stimuli, avoiding simulation of the visual perception of the stimuli. Here, the computer vision technique You Only Look Once (YOLO) (Jocher et al., 2021; Redmon et al., 2016), is integrated into the workflow of an NPLS simulation probability learning experiments with children. YOLO is a convolutional neural network (CNN) designed to detect objects. The model’s performance on marble datasets is tested through an analysis of precision and recall. Results indicate that the YOLO model, when trained sufficiently, outputs predictions on marble image datasets with high accuracy and precision. We also analyze YOLO’s suitability as a biologically plausible model of visual processing, interfering with YOLO’s training process by shortening the training time to examine the effects of perceptual errors on simulated probabilistic reasoning.