Rapid categorization paradigms have a long history in experimental psychology: Characterized by short presentation times and speeded behavioral responses, these tasks highlight the efficiency with which our visual system processes natural object categories. Previous studies have shown that feed-forward hierarchical models of the visual cortex provide a good fit to human visual decisions. At the same time, recent work in computer vision has demonstrated significant gains in object recognition accuracy with increasingly deep hierarchical architectures. But it is unclear how well these models account for human visual decisions and what they may reveal about the underlying brain processes. We have conducted a large-scale psychophysics study to assess the correlation between computational models and human behavioral responses on a rapid animal vs. non-animal categorization task. We considered visual representations of varying complexity by analyzing the output of different stages of processing in three state-of-the-art deep networks. We found that recognition accuracy increases with higher stages of visual processing (higher level stages indeed outperforming human participants on the same task) but that human decisions agree best with predictions from intermediate stages. Overall, these results suggest that human participants may rely on visual features of intermediate complexity and that the complexity of visual representations afforded by modern deep network models may exceed the complexity of those used by human participants during rapid categorization.

The ability to localize ourselves in the outdoor world based on visual input even in absence of prior positional information is an important skill of our daily lives that comes naturally to us. However, the underlying mechanisms of this ability are poorly understood. Here, we show how simple texture statistics can be sufficient to provide a strong prior for the self-localization tasks. We find that statistics of common outdoor features such as tree density, foliage type or road structure provide a stronger cue for self-localization than the matching and recognition of less common landmarks such as lamp posts. We encourage the use of such common feature vectors as priors for self-localization systems and hypothesize that humans may use similar priors to assess the location from an unknown image.