Uncovering the Computational Mechanisms Underlying Many-Alternative Choice
How do we choose when confronted with many alternatives? There is surprisingly little decision modeling work with large choice sets, despite their prevalence in everyday life. Even further, there is an apparent disconnect between research in small choice sets, supporting a process of gaze-driven evidence accumulation, and research in larger choice sets, arguing for models of optimal choice, satisficing, and hybrids of the two. Here, we bridge this divide by developing and comparing different versions of these models in a many-alternative value-based choice experiment with 9, 16, 25, or 36 alternatives. We find that human choices are best explained by models incorporating an active effect of gaze on subjective value. A gaze-driven, probabilistic version of satisficing generally outperforms the other models, though gaze-driven evidence accumulation and comparison performs comparably well with 9 alternatives and is overall most accurate in capturing the relation between gaze allocation and choice.