Empirical modeling of an alcohol expectancy memory network using multidimensional scaling.
Empirical modeling of an alcohol expectancy memory network using multidimensional scaling.
复制标题
使用多维尺度对酒精预期记忆网络进行实证建模。
DOI：
10.1037//0021-843x.101.1.174
复制
发表时间：
1992
影响因子：
4.6
通讯作者：
Brannick,M
中科院分区：
文献类型：
--
作者：
Rather,BC;Goldman,MS;Roehrich,L;Brannick,M
风险相关的前因变量可以通过记忆过程与后来的酒精消费联系起来，酒精预期可能是一个相关的记忆内容。为了推进这一领域的研究，应用当前的记忆模型（如语义网络理论）来解释饮酒决策过程将是有用的。我们使用多维标度（MDS）的经验模型初步酒精预期语义网络，从理论上解释饮酒决策。亚组分析（PREFMAP）显示，具有不同饮酒史的个体在期望网络中可能有不同的关联途径。这些途径可能反过来影响了未来的饮酒水平和行为，而这个人是在酒精的影响下。所有人都将饮酒与积极/亲社会影响联系起来，但重度饮酒者表示唤起效应是他们最有可能的联系，而轻度饮酒者则预计会产生镇静作用。MDS建模过程中的一个重要的早期步骤是确定对应于理论网络节点的同义期望形容词组。
Risk-related antecedent variables can be linked to later alcohol consumption by memory processes, and alcohol expectancies may be one relevant memory content. To advance research in this area, it would be useful to apply current memory models such as semantic network theory to explain drinking decision processes. We used multidimensional scaling (MDS) to empirically model a preliminary alcohol expectancy semantic network, from which a theoretical account of drinking decision making was generated. Subanalyses (PREFMAP) showed how individuals with differing alcohol consumption histories may have had different association pathways within the expectancy network. These pathways may have, in turn influenced future drinking levels and behaviors while the person was under the influence of alcohol. All individuals associated positive/prosocial effects with drinking, but heavier drinkers indicated arousing effects as their highest probability associates, whereas light drinkers expected sedation. An important early step in this MDS modeling process is the determination of iso-meaning expectancy adjective groups, which correspond to theoretical network nodes.