In decision making, we often focus on only a few options out of many. This behavior may seem hasty, but it can actually be the smartest move when we have limited cognitive resources. Researchers have explored how to best allocate our limited sampling time to determine the most profitable option. They used accumulator models, which simulate how evidence accumulates for different choices. The study found that sampling capacity increases with available time and the distinguishability of options. The optimal allocation of time undergoes a dramatic shift based on capacity. For small capacities, evenly allocating time to five options while ignoring others is ideal, regardless of the rewards distribution. On the other hand, for larger capacities, the number of sampled accumulators grows in a sublinear fashion, following a power law. Interestingly, allocating equal time to each sampled accumulator is more effective than uneven allocations. This research sheds light on the tradeoffs involved in multialternative decision making and emphasizes the importance of limited resources and environmental variability. So next time you’re faced with numerous options, take a cue from science and focus on a handful that offers the most promise! To dive deeper, check out the full article.

Abstract
When facing many options, we narrow down our focus to very few of them. Although behaviors like this can be a sign of heuristics, they can actually be optimal under limited cognitive resources. Here, we study the problem of how to optimally allocate limited sampling time to multiple options, modeled as accumulators of noisy evidence, to determine the most profitable one. We show that the effective sampling capacity of an agent increases with both available time and the discriminability of the options, and optimal policies undergo a sharp transition as a function of it. For small capacity, it is best to allocate time evenly to exactly five options and to ignore all the others, regardless of the prior distribution of rewards. For large capacities, the optimal number of sampled accumulators grows sublinearly, closely following a power law as a function of capacity for a wide variety of priors. We find that allocating equal times to the sampled accumulators is better than using uneven time allocations. Our work highlights that multialternative decisions are endowed with breadth–depth tradeoffs, demonstrates how their optimal solutions depend on the amount of limited resources and the variability of the environment, and shows that narrowing down to a handful of options is always optimal for small capacities.

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