A central problem in vision sciences is to understand how humans recognise objects under novel viewing conditions. Recently, statistical inference models such as Convolutional Neural Networks (CNNs) seem to have reproduced this ability by incorporating some architectural constraints of biological vision systems into machine learning models. This has led to the proposal that, like CNNs, humans solve the problem of object recognition by performing a statistical inference over their observations. This hypothesis remains difficult to test as models and humans learn in vastly different environments. Accordingly, any differences in performance could be attributed to the training environment rather than reflect any fundamental difference between statistical inference models and human vision. To overcome these limitations, we conducted a series of experiments and simulations where humans and models had no prior experience with the stimuli. The stimuli contained multiple features that varied in the extent to which they predicted category membership. We observed that human participants frequently ignored features that were highly predictive and clearly visible. Instead, they learned to rely on global features such as colour or shape, even when these features were not the most predictive. When these features were absent they failed to learn the task entirely. By contrast, ideal inference models as well as CNNs always learned to categorise objects based on the most predictive feature. This was the case even when the CNN was pre-trained to have a shape-bias and the convolutional backbone was frozen. These results highlight a fundamental difference between statistical inference models and humans: while statistical inference models such as CNNs learn most diagnostic features with little regard for the computational cost of learning these features, humans are highly constrained by their limited cognitive capacities which results in a qualitatively different approach to object recognition.
Reactive navigation in partially familiar planar environments using semantic perceptual feedback
