Why do we decide before we have all the information?

Before choosing a product, voting in an election, or making an important decision, we may seek additional information or decide based on what we already know. But how does the brain determine the right moment to stop gathering information and commit to a choice? This question lies at the heart of many everyday decisions and was the starting point for a study that sought to understand why people often decide before all available information has been collected.

The research was conducted by Christina Dimitriadou and Nicholas Furl from the Royal Holloway University of London. The study involved 40 healthy adults who took part in a computerized decision-making task. Participants observed a sequence of blue and green beads drawn from one of two containers with different proportions of each colour. As new beads were presented, they had to infer which container was being used. After each bead, they could choose either to view another bead or to stop and make a decision. Because each additional observation carried out a cost, the task required participants to decide when enough evidence had been gathered to justify a response. While completing the task, their brain activity was recorded using electroencephalography (EEG). The results were also compared with the predictions of mathematical models simulating an ideal decision-making strategy.

The researchers found that participants tended to make decisions earlier than would be expected according to optimal models. In particular, they gathered less information than was necessary to maximize the probability of making a correct choice, especially in more uncertain situations where the available evidence was less conclusive. Nevertheless, computational models showed that this behaviour could be explained by the way people appear to assign a subjective cost to the effort involved in continuing to search for information.

Brain recordings also revealed distinct signals associated with the moment of decision-making. When participants stopped seeking further information and committed to a choice, there was a marked increase in a brain response known as the P300, which is often associated with the processing of relevant information and decision-making. In addition, the researchers observed increased beta oscillatory activity, a type of brain rhythm linked to preparing for action and committing to a choice. These signals became stronger as participants approached the point of making a decision.

The authors conclude that decision-making results from a balance between the benefit of obtaining more information and the perceived cost of continuing to search for it. The findings suggest that the brain has specific mechanisms that signal when the accumulated information appears sufficient to support a choice, even if that decision is not mathematically optimal. Understanding these mechanisms may help explain why some people make hasty decisions and may contribute to the study of disorders in which the evaluation of information and uncertainty is altered.

Published in the journal Neuropsychologia, the study From sampling to stopping: The P300 ERP component and beta power contribute to reward-related decision commitments resulted from project 34/20 – What mechanisms and computations does the brain use to solve complex decision problems? supported by the Bial Foundation.

ABSTRACT

Optimal stopping problems provide a framework for studying decision-making under uncertainty, balancing the trade-off between information sampling and decision commitment. We investigated deviations from normative strategies in human decision-making and examined the neural mechanisms underlying decision commitment versus sampling using electroencephalography (EEG). Forty participants viewed sequences of beads drawn from fictitious urns and attempted to infer the majority bead colour in each urn, while their EEG activity was recorded. After viewing each bead, participants could choose to sample more by drawing another bead (draw choice) or to stop sampling and infer the contents of the urn (urn choice). A Bayesian ideal observer model and parametrised models were used to predict participant behaviour. Participants undersampled relative to the ideal observer, particularly in the more uncertain condition where the proportion of bead colours was close to chance (60/40 or 0.6), with the model better capturing behaviour in the easier 80/20 (0.8) condition. Stimulus-locked P300 amplitudes showed larger responses for urn choices and a gradient of increasing amplitude as draws approached commitment. Larger frontal ERP responses were also observed under higher uncertainty (0.6 condition). Beta oscillatory activity was stronger for urn choices in the high uncertainty condition, with fast beta (20–30 Hz) activity driving final commitment to a decision. Beta power was further predicted by model-derived action values. By integrating behavioural, computational, and neurophysiological data, this study advances our understanding of active information sampling versus decision commitment in reward-related probabilistic environments.