Lane Scott D, Yechiam Eldad, Busemeyer Jerome R
Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, 77030, USA.
Exp Clin Psychopharmacol. 2006 May;14(2):254-64. doi: 10.1037/1064-1297.14.2.254.
In 3 previous experiments, high doses of alcohol, marijuana, and alprazolam acutely increased risky decision making by adult humans in a 2-choice (risky vs. nonrisky) laboratory task. In this study, a computational modeling analysis known as the expectancy valence model (J. R. Busemeyer & J. C. Stout, 2002) was applied to individual-participant data from these studies, for the highest administered dose of all 3 drugs and corresponding placebo doses, to determine changes in decision-making processes that may be uniquely engendered by each drug. The model includes 3 parameters: responsiveness to rewards and losses (valence or motivation); the rate of updating expectancies about the value of risky alternatives (learning/memory); and the consistency with which trial-by-trial choices match expected outcomes (sensitivity). Parameter estimates revealed 3 key outcomes: Alcohol increased responsiveness to risky rewards and decreased responsiveness to risky losses (motivation) but did not alter expectancy updating (learning/memory); both marijuana and alprazolam produced increases in risk taking that were related to learning/memory but not motivation; and alcohol and marijuana (but not alprazolam) produced more random response patterns that were less consistently related to expected outcomes on the 2 choices. No significant main effects of gender or dose by gender interactions were obtained, but 2 dose by gender interactions approached significance. These outcomes underscore the utility of using a computational modeling approach to deconstruct decision-making processes and thus better understand drug effects on risky decision making in humans.
在之前的3项实验中，高剂量的酒精、大麻和阿普唑仑在一项二选一（风险与非风险）实验室任务中，会使成年人的风险决策急性增加。在本研究中，一种名为期望效价模型（J.R. 布塞迈尔和J.C. 斯托特，2002年）的计算建模分析被应用于这些研究中的个体参与者数据，针对所有3种药物的最高给药剂量以及相应的安慰剂剂量，以确定每种药物可能独特引发的决策过程变化。该模型包括3个参数：对奖励和损失的反应性（效价或动机）；更新对风险选择价值期望的速率（学习/记忆）；以及逐次试验选择与预期结果匹配的一致性（敏感性）。参数估计揭示了3个关键结果：酒精增加了对风险奖励的反应性，降低了对风险损失的反应性（动机），但没有改变期望更新（学习/记忆）；大麻和阿普唑仑都导致了与学习/记忆相关而非动机相关的冒险行为增加；酒精和大麻（但不是阿普唑仑）产生了更多随机反应模式，这些模式与二选一选择的预期结果的一致性较低。未获得性别或剂量与性别交互作用的显著主效应，但有2个剂量与性别交互作用接近显著。这些结果强调了使用计算建模方法来解构决策过程，从而更好地理解药物对人类风险决策影响的效用。
