The Gist of Hot and Cold Cognition in Adolescents Risky Decision Making

描述（由申请人提供）：青少年时期的冒险行为会导致大量的发病率和死亡率，而在此期间出现的不健康行为模式会影响一生。例如，在5547名12至19岁青少年的代表性样本中，没有人符合心血管健康标准。对奖励的敏感性（例如，美味的食物）会导致肥胖和其他不健康的行为，最近关于青少年冒险决策的研究综述集中在这个主题上。虽然有证据表明青少年比成年人对奖励更敏感，但并不是所有的研究都发现了这种模式：一些研究发现青少年对奖励的敏感度较低，这不能简单地用奖励阶段（期待与接受）来解释。其他研究并没有将奖励敏感性与风险态度、对结果的记忆或反馈诱导策略等已知的发展差异相混淆。此外，不同领域对奖励敏感性的定义也各不相同，对青少年决策的研究并没有区分这里所测试的4种不同假设。这些假设对青少年的冒险行为和动机对其行为的影响做出了截然不同的预测。此外，我们还研究了诸如对奖励和风险的敏感性等因素与情绪和驱动状态之间的相互作用。我们测试了令人惊讶的，但理论上有动机的预测，例如：(1)在青少年中，即使客观上低回报，驱动状态也会诱发反向框架（为更大的回报承担更大的风险，接受更大的肯定损失）。(2)诱导gist加工对奖励风险承担具有保护作用，降低了驱动状态的脆弱性。(3)尽管大多数理论预测青少年比成年人更容易受到强烈情绪的影响，并且不能准确地预测他们的风险决策，但从理论上讲，青少年比成年人更冷漠地对待这些风险是有道理的。青少年和成年人将提供奖励评级，并使用结果性和激励性激励相容程序（以平衡顺序）做出涉及这些相同奖励的决定。我们研究了常见的货币和特定领域对糖果和货币的影响，并使用神经成像来测试关于冒险的神经回路的假设。首席研究员和其他研究人员是非常熟练的数据分析和数学建模。分析将包括标准方差分析的析因设计（表1）与决策选择或奖励评级作为因变量。使用多元回归，个体差异测量（主成分分析将用于减少预测因子的数量；见Reyna， Estrada等人，2011），加上实验室决策和奖励评级，将用于预测青少年风险问卷中现实生活中的风险承担。因此，我们既操纵奖励水平，测量对奖励的敏感性作为个体差异，也操纵对认知控制的挑战（例如，驱动状态）和测量认知控制，包括抑制（行为抑制量表和去/不去任务）。

DESCRIPTION (provided by applicant): Risky behavior contributes to substantial morbidity and mortality during adolescence, and unhealthy patterns of behavior that debut during this period have consequences that play out over a lifetime. For example, in a representative sample of 5,547 12- to 19-year-olds, none met criteria for cardiovascular health. Sensitivity to rewards (e.g., tasty foods) contributes to obesity and other unhealthy behaviors, and recent reviews of research on risky decision making in adolescence have focused on this topic. Although there is evidence that adolescents are more sensitive than adults are to rewards, not all studies find this pattern: Some studies find less sensitivity to rewards among adolescents, which cannot be explained simply by reward stages (anticipation vs. receipt). Other studies were not designed to isolate reward sensitivity, and so confound it with known developmental differences in risk attitudes, memory for outcomes, or feedback-induced strategies. Also, definitions of reward sensitivity vary across fields, and research on adolescent decision making does not distinguish among 4 different hypotheses tested here. These hypotheses make starkly different predictions about adolescent risk taking and effects of incentives on their behavior. Moreover, we examine the interplay between such factors as sensitivity to reward and risk, on the one hand, with emotions and drive states on the other hand. We test surprising, but theoretically motivated predictions, for example: (1) Drive states will induce reverse framing (taking greater risks for greater rewards and accepting larger sure losses) among adolescents even for objectively low rewards. (2) Inducing gist processing will have a protective effect on ris taking for rewards, reducing vulnerability to drive states. (3) Although most theories anticipate that adolescents will be more vulnerable to strong emotion than adults, and less able to accurately forecast their risky decisions, there is theoretical justification for the prediction tht adolescents will approach such risks more coldly than adults. Adolescents and adults will provide reward ratings and make decisions involving these same rewards (in counterbalanced order) using consequential and motivating incentive-compatible procedures. We examine common currency and domain-specific effects for candy bars and money, and use neuroimaging to test hypotheses about neural circuitry of risk taking. The Principal Investigator and other investigators are highly proficient data analysts and mathematical modelers. Analyses will include standard ANOVA for factorial designs (Table 1) with either decision choices or reward ratings as dependent variables. Using multiple regressions, measures of individual differences (principal components analysis will be used to reduce the number of predictors; see Reyna, Estrada et al., 2011), plus laboratory decisions and reward ratings, will be used to predict real-life risk taking on the Adolescent Risk Questionnaire. Therefore, we both manipulate levels of reward and measure sensitivity to reward as an individual difference, as well as manipulate challenges to cognitive control (e.g., drive states) and measure cognitive control, including inhibition (Behavioral Inhibition Scale and go/no-go task).