The Comparison of Human and Machine Performance in Object Recognition.

ABSTRACT: Deep learning models have advanced rapidly, leading to claims that they now match or exceed human performance. However, such claims are often based on closed-set conditions with fixed labels, extensive supervised training, and do not considering differences between the two systems. Recent findings also indicate that some models align more closely with human categorisation behaviour, whereas other studies argue that even highly accurate models diverge from human behaviour. Following principles from comparative psychology and imposing similar constraints on both systems, this study investigates whether these models can achieve human-level accuracy and human-like categorisation through three experiments using subsets of the ObjectNet dataset. Experiment 1 examined performance under varying pr

SUBMITTER: Kul G 

PROVIDER: S-EPMC12837923 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature