Abstract
Visual search is crucial in daily human interaction with the environment. Hybrid search extends this by requiring observers to find any item from a given set. Recently, a few models were proposed to simulate human eye movements in visual search tasks within natural scenes, but none were implemented for Hybrid search under similar conditions. We present an enhanced neural network Entropy Limit Minimization (nnELM) model, grounded in a Bayesian framework and signal detection theory, and the Hybrid Search Eye Movements (HSEM) Dataset, containing thousands of human eye movements during hybrid tasks. A key Hybrid search challenge is that participants have to look for different objects at the same time. To address this, we developed several strategies involving the posterior probability distributions after each fixation. Adjusting peripheral visibility improved early-stage efficiency, aligning it with human behavior. Limiting the model's memory reduced success in longer searches, mirroring human performance. We validated these improvements by comparing our model with a held-out set within the HSEM and with other models in a separate visual search benchmark. Overall, the new nnELM model not only handles Hybrid search in natural scenes but also closely replicates human behavior, advancing our understanding of search processes while maintaining interpretability.