School of Psychology, University of Tel Aviv, Israel.
School of Psychology, University of Tel Aviv, Israel; Sagol School of Neuroscience, University of Tel Aviv, Israel.
Cognition. 2019 Dec;193:104022. doi: 10.1016/j.cognition.2019.104022. Epub 2019 Jul 29.
Integration-to-boundary is a prominent normative principle used in evidence-based decisions to explain the speed-accuracy trade-off and determine the decision-time. Despite its prominence, however, the decision boundary is not directly observed, but rather is theoretically assumed, and there is still an ongoing debate regarding its form: fixed vs. collapsing. The aim of this study is to show that the integration-to-boundary process extends to decisions between rapid pairs of numerical sequences (2 Hz rate), and to determine the boundary type by directly monitoring the noisy accumulated evidence. In a set of two experiments (supplemented by computational modelling), we demonstrate that integration to a collapsing-boundary takes place in such tasks, ruling out non-integration heuristic strategies. Moreover, we show that participants can adaptively adjust their boundaries in response to reward contingencies. Finally, we discuss the implications to decision optimality and the nature of processes and representations in numerical cognition.
整合到边界是一个在循证决策中被广泛应用的规范原则，用于解释速度准确性权衡并确定决策时间。然而，尽管它很突出，但是决策边界并不是直接观察到的，而是理论上假设的，并且关于它的形式仍然存在争议：固定的还是崩溃的。本研究的目的是表明，整合到边界的过程扩展到快速数字序列对之间的决策（2Hz 速率），并通过直接监测嘈杂的累积证据来确定边界类型。在一系列两项实验中（辅以计算建模），我们证明了在这种任务中发生了崩溃边界的整合，排除了非整合启发式策略。此外，我们表明，参与者可以根据奖励的偶然性自适应地调整他们的边界。最后，我们讨论了这些发现对决策最优性以及数字认知过程和表示性质的影响。
