Optimal Allocation of Finite Sampling Capacity in Accumulator Models of Multialternative Decision Making

Autor: Jorge Ramírez‐Ruiz, Rubén Moreno‐Bote
Rok vydání: 2022
Předmět:
Zdroj: Cognitive Science. 46
ISSN: 1551-6709
0364-0213
DOI: 10.1111/cogs.13143
Popis: 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. This work is supported by the Howard Hughes Medical Institute (HHMI, ref 55008742), MINECO (Spain; BFU2017-85936-P), and ICREA Academia (2016) to R.M.-B, and MINECO/ESF (Spain; PRE2018-084757) to J.R.-R.
Databáze: OpenAIRE
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