Abstract
How do we choose when confronted with many alternatives? There is surprisingly little decision modelling 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 provides slightly better fits to choices and response times, while the gaze-driven evidence accumulation and comparison model provides the best overall account of the data when also considering the empirical relation between gaze allocation and choice.
Highlights
In everyday life, we are constantly faced with value-based choice problems involving many possible alternatives
Based on the findings by Reutskaja et al, 2011, we considered a probabilistic version of satisficing, which combines elements from the optimal choice and hard satisficing models
We found that all subjects exhibited positive values on this measure in all set sizes (Figure 4E; with values ranging from 1.7% to 75%) and that it increased with set size (Figure 4E; b = 0.26%, 94% highest density intervals (HDI) = [0.15, 0.39] per item), indicating an overall positive association between gaze allocation and choice
Summary
We are constantly faced with value-based choice problems involving many possible alternatives. Prior work on 2AFC has indicated that simple value-based choices are made through a process of gaze-driven evidence accumulation and comparison, as captured by the attentional drift diffusion model (Krajbich et al, 2010; Krajbich and Rangel, 2011; Smith and Krajbich, 2019) and the gazeweighted linear accumulator model (GLAM; Thomas et al, 2019). These models assume that noisy evidence in favour of each alternative is compared and accumulated over time.
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