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How humans learn new visual objects is a longstanding scientific problem. Previous work has led to a diverse collection of models for how object learning may be accomplished, but a current limitation in the field is a lack of empirical benchmarks that evaluate the predictive validity of specific, image-computable models and facilitate fair comparisons between competing models. Here, we used online psychophysics to measure human learning trajectories over a set of tasks involving novel 3D objects, then used those data to develop such benchmarks. We make all data and benchmarks publicly available, and, to our knowledge, they are currently the largest publicly-available collection of visual object learning psychophysical data in humans. Consistent with intuition, we found that humans generally require very few images ( |
