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Investigate a computational implementation of visual problem solving using a generative model and genetic search.

December 9, 2025PLoS Computational BiologyOpen Access

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

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Investigate a computational implementation of visual problem solving using a generative model and genetic search.
Develop a generative search algorithm based on analysis-by-synthesis paradigm.
Apply the model to a visual problem-solving task inspired by star constellations.
Compare model performance with human performance based on accuracy and reaction time.
Generative model enhances adaptation in visual problem solving tasks.
Insights reveal mechanisms underlying human visual problem solving.

Authors

TKTarun KhajuriaUniversity of Tartu KTKadi TulverUniversity of Tartu JAJaan AruUniversity of Tartu

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Implication

Computational model shows improved adaptation in visual problem solving compared to human vision, indicating generative approaches may enhance interpretation.

Cite This Study

Khajuria et al. (2025) studied this question.

synapsesocial.com/papers/69401d732d562116f28f94bc https://doi.org/10.1371/journal.pcbi.1012968

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Implication

Computational model shows improved adaptation in visual problem solving compared to human vision, indicating generative approaches may enhance interpretation.

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

Key Points

Discussion

Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study

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