Comparing a computational model of visual problem solving with human vision on a difficult vision task.

ABSTRACT: Human vision is not merely a passive process of interpreting sensory input but can also function as a problem-solving process incorporating generative mechanisms to interpret ambiguous or noisy data. This synergy between the generative and discriminative components, often described as analysis-by-synthesis, enables robust perception and rapid adaptation to out-of-distribution inputs. In this work, we investigate a computational implementation of the analysis-by-synthesis paradigm using genetic search in a generative model, applied to a visual problem-solving task inspired by star constellations. The search is guided by low-level cues based on the structural fitness of candidate solutions compared to the test images. This dataset serves as a testbed for exploring how inferred signals can gu

SUBMITTER: Khajuria T 

PROVIDER: S-EPMC12707649 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature