Between heuristics and optimality: Flexible integration of cost and evidence during information sampling

Abstract

To guide effective decision making in an uncertain world, humans must balance seeking relevant information with the costs of delaying choice. Optimal information sampling requires computationally expensive value estimations. Information sampling heuristics are computationally simple, but rigid. Efficient and flexible information sampling, therefore, must leverage the advantages offered from both approaches. In the present study, human participants completed an information sampling task, in which they sampled sequences of images (e.g. indoor and outdoor scenes) and attempted to infer the majority category (e.g. indoor or outdoor) under two reward conditions. We examined how behavior maps onto potential information sampling strategies. We found that choices were best described by a flexible function that lay between optimality and heuristics; integrating the magnitude of evidence favoring each category and the number of samples collected thus far. Integration of these criteria resulted in a trade-off between evidence and samples collected, in which the strength of evidence needed to stop sampling decreased linearly as the number of samples accumulated over the course of a trial. This non-optimal trade-off best accounted for choice behavior even under high reward contexts. Our results demonstrate that unlike the optimal strategy, humans are performing simple accumulations instead of computing expected values, and that unlike a simple heuristic strategy, humans are dynamically integrating multiple sources of information in lieu of using only one source. This evidence-by-costs tradeoff illustrates a computationally efficient strategy that balances competing motivations for accuracy and cost minimization.

Optimal Allocation of Time in Risky Choices under Opportunity Costs

“…Otherwise, one endures the opportunity costs of reduced time for making other potentially rewarding choices or for activities to increase well-being more generally. Consequently, for adaptive decision making it is important to determine how much time to spend on a choice problem (Hsiung et al, 2022;Payne et al, 1992).…”