Behavioral differences between humans and machines arise early in visual processing.

ABSTRACT: It remains an open question to what extent current deep neural networks (DNNs) are suitable computational models of the human visual system. While DNNs have proven to be capable of predicting neural activations in primate visual cortex with great success, psychophysical experiments have shown behavioral differences between DNNs and human observers. One of these behavioral differences is which individual images DNNs and human observers find easy or difficult to recognize, as quantified by error consistency (EC). Hypothetically, the reported differences in EC could arise late in visual processing, even though the representations extracted by DNNs and human observers may have been more similar in the initial forward sweep: At the presentation and response times investigated in earlier work, o

SUBMITTER: Klein T 

PROVIDER: S-EPMC12922715 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature