任务(项目管理)
背景(考古学)
计算机科学
机器学习
灵敏度(控制系统)
人工智能
过程(计算)
认知心理学
心理学
工程类
古生物学
电子工程
生物
系统工程
操作系统
作者
Ran Zhou,Jay I. Myung,Mark A. Pitt
标识
DOI:10.1016/j.cogpsych.2021.101407
摘要
The Balloon Analogue Risk Task (BART) is a sequential decision making paradigm that assesses risk-taking behavior. Several computational models have been proposed for the BART that characterize risk-taking propensity. An aspect of task performance that has proven challenging to model is the learning that develops from experiencing wins and losses across trials, which has the potential to provide further insight into risky decision making. We developed the Scaled Target Learning (STL) model for this purpose. STL describes learning as adjustments to an individual's strategy in reaction to outcomes in the task, with the size of adjustments reflecting an individual's sensitivity to wins and losses. STL is shown to be sensitive to the learning elicited by experimental manipulations. In addition, the model matches or bests the performance of three competing models in traditional model comparison tests (e.g., parameter recovery performance, predictive accuracy, sensitivity to risk-taking propensity). Findings are discussed in the context of the learning process involved in the task. By characterizing the extent to which people are willing to adapt their strategies based on past experience, STL is a step toward a complete depiction of the psychological processes underlying sequential risk-taking behavior.
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