Modeling mean estimation tasks in within-trial and across-trial contexts

The mean estimation task, which explicitly asks observers to estimate the mean feature value of multiple stimuli, is a fundamental paradigm in research areas such as ensemble coding and cue integration. The current study uses computational models to formalize how observers summarize information in mean estimation tasks. We compare model predictions from our Fidelity-based Integration Model (FIM) and other models on their ability to simulate observed patterns in within-trial weight distribution, across-trial information integration, and set-size effects on mean estimation accuracy. Experiments show non-equal weighting within trials in both sequential and simultaneous mean estimation tasks. Observers implicitly overestimated trial means below the global mean and underestimated trial means above the global mean. Mean estimation performance declined and stabilized with increasing set sizes. FIM successfully simulated all observed patterns, while other models failed. FIM's information sampling structure provides a new way to interpret the capacity limit in visual working memory and sub-sampling strategies. As a model framework, FIM offers task-dependent modeling for various ensemble coding paradigms, facilitating research synthesis across different studies in the literature.