Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Computación, Buenos Aires, Argentina.
CONICET-Universidad de Buenos Aires, Instituto de Investigación en Ciencias de la Computación (ICC), Buenos Aires, Argentina.
PLoS Comput Biol. 2018 Mar 2;14(3):e1005961. doi: 10.1371/journal.pcbi.1005961. eCollection 2018 Mar.
We present a theory of decision-making in the presence of multiple choices that departs from traditional approaches by explicitly incorporating entropic barriers in a stochastic search process. We analyze response time data from an on-line repository of 15 million blitz chess games, and show that our model fits not just the mean and variance, but the entire response time distribution (over several response-time orders of magnitude) at every stage of the game. We apply the model to show that (a) higher cognitive expertise corresponds to the exploration of more complex solution spaces, and (b) reaction times of users at an on-line buying website can be similarly explained. Our model can be seen as a synergy between diffusion models used to model simple two-choice decision-making and planning agents in complex problem solving.
我们提出了一种在存在多种选择情况下的决策理论,该理论通过在随机搜索过程中明确纳入熵障碍,从而偏离了传统方法。我们分析了来自 1500 万闪电战游戏在线存储库的反应时间数据,并表明我们的模型不仅适用于平均值和方差,而且适用于游戏每个阶段的整个反应时间分布(跨越几个反应时间数量级)。我们应用该模型表明:(a)更高的认知专业知识对应于更复杂的解决方案空间的探索;(b)在线购买网站的用户的反应时间也可以得到类似的解释。我们的模型可以被视为用于模拟简单二择一决策的扩散模型和用于复杂问题解决的规划代理之间的协同作用。