Phillips Jeffrey S, McMillan Corey T, Smith Edward E, Grossman Murray
Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States.
Penn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States.
Neuropsychologia. 2017 Apr;98:98-110. doi: 10.1016/j.neuropsychologia.2016.07.003. Epub 2016 Jul 6.
Semantic category learning is dependent upon several factors, including the nature of the learning task, as well as individual differences in the quality and heterogeneity of exemplars that an individual encounters during learning. We trained healthy older adults (n=39) and individuals with a diagnosis of Alzheimer's disease or Mild Cognitive Impairment (n=44) to recognize instances of a fictitious animal, a "crutter". Each stimulus item contained 10 visual features (e.g., color, tail shape) which took one of two values for each feature (e.g., yellow/red, curly/straight tails). Participants were presented with a series of items (learning phase) and were either told the items belonged to a semantic category (explicit condition) or were told to think about the appearance of the items (implicit condition). Half of participants saw learning items with higher similarity to an unseen prototype (high typicality learning set), and thus lower between-item variability in their constituent features; the other half learned from items with lower typicality (low typicality learning set) and higher between-item feature variability. After the learning phase, participants were presented with test items one at a time that varied in the number of typical features from 0 (antitype) to 10 (prototype). We examined between-subjects factors of learning set (lower or higher typicality), instruction type (explicit or implicit), and group (patients vs. elderly control). Learning in controls was aided by higher learning set typicality: while controls in both learning set groups demonstrated significant learning, those exposed to a high-typicality learning set appeared to develop a prototype that helped guide their category membership judgments. Overall, patients demonstrated more difficulty with category learning than elderly controls. Patients exposed to the higher-typicality learning set were sensitive to the typical features of the category and discriminated between the most and least typical test items, although less reliably than controls. In contrast, patients exposed to the low-typicality learning set showed no evidence of learning. Analysis of structural imaging data indicated a positive association between left hippocampal grey matter density in elderly controls but a negative association in the patient group, suggesting differential reliance on hippocampal-mediated learning. Contrary to hypotheses, learning did not differ between explicit and implicit conditions for either group. Results demonstrate that category learning is improved when learning materials are highly similar to the prototype.
语义类别学习取决于几个因素,包括学习任务的性质,以及个体在学习过程中遇到的示例的质量和异质性方面的个体差异。我们训练了健康的老年人(n = 39)以及被诊断患有阿尔茨海默病或轻度认知障碍的个体(n = 44)来识别一种虚构动物“克鲁特”的实例。每个刺激项目包含10个视觉特征(例如,颜色、尾巴形状),每个特征有两种值之一(例如,黄色/红色、卷曲/直尾巴)。向参与者呈现一系列项目(学习阶段),并告知他们这些项目属于一个语义类别(明确条件),或者告知他们思考项目的外观(隐含条件)。一半的参与者看到与一个未见过的原型具有更高相似度的学习项目(高典型性学习集),因此其组成特征在项目之间的变异性较低;另一半从具有较低典型性(低典型性学习集)和较高项目间特征变异性的项目中学习。在学习阶段之后,一次向参与者呈现一个测试项目,这些项目的典型特征数量从0(反原型)到10(原型)不等。我们研究了学习集(较低或较高典型性)、指导类型(明确或隐含)和组(患者与老年对照组)这些受试者间因素。对照组的学习因学习集典型性较高而得到帮助:虽然两个学习集组中的对照组都表现出显著的学习,但接触高典型性学习集的那些人似乎形成了一个有助于指导他们进行类别归属判断的原型。总体而言,患者在类别学习上比老年对照组表现出更多困难。接触高典型性学习集的患者对类别的典型特征敏感,并能区分最典型和最不典型的测试项目,尽管不如对照组可靠。相比之下,接触低典型性学习集的患者没有学习的迹象。对结构成像数据的分析表明,老年对照组左侧海马灰质密度呈正相关,而患者组呈负相关,这表明对海马介导的学习存在不同程度的依赖。与假设相反,两组在明确条件和隐含条件下的学习没有差异。结果表明,当学习材料与原型高度相似时,类别学习会得到改善。