The dynamics of scaling: a memory-based anchor model of category rating and absolute identification.
The dynamics of scaling: a memory-based anchor model of category rating and absolute identification.
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DOI：
10.1037/0033-295x.112.2.383
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发表时间：
2005-04
影响因子：
5.4
通讯作者：
A. Petrov;John R. Anderson
中科院分区：
文献类型：
--
作者：
A. Petrov;John R. Anderson
提出并验证了一种基于记忆的尺度模型--锚。将目标刺激的感知幅度与记忆中的一组锚点进行比较。锚点选择是概率性的，对相似性、基准强度和新近度敏感。获胜的锚在目标附近提供参考点，从而将全局缩放问题转换为局部比较。一个明确的纠正策略决定了最终的反应。两个增量学习机制更新锚的位置和基本激活。这就产生了顺序、语境、迁移、实践和其他动态效应。比例将展开为自适应贴图。一个层次的模型进行测试电池的定量措施，从2个实验中的绝对识别和类别评级。
A memory-based scaling model--ANCHOR--is proposed and tested. The perceived magnitude of the target stimulus is compared with a set of anchors in memory. Anchor selection is probabilistic and sensitive to similarity, base-level strength, and recency. The winning anchor provides a reference point near the target and thereby converts the global scaling problem into a local comparison. An explicit correction strategy determines the final response. Two incremental learning mechanisms update the locations and base-level activations of the anchors. This gives rise to sequential, context, transfer, practice, and other dynamic effects. The scale unfolds as an adaptive map. A hierarchy of models is tested on a battery of quantitative measures from 2 experiments in absolute identification and category rating.