Perceptual Categorization and Memory

知觉分类和记忆每当我们决定某个视觉呈现的物体是一只小猎犬而不是一只牧羊犬，是一个瓶子而不是一个罐子，或者是一棵树而不是一棵灌木时，我们都是通过将物体的知觉属性与先前通过经验获得的关于类别的信息进行比较来做出分类决定的。发展知觉分类的心理学理论需要理解知觉系统提供了什么信息，如何将这些信息与先前获得的类别信息进行比较，什么样的表征存储在记忆中，类别表征如何随经验而变化，以及如何根据各种类别的证据做出分类决策。感知范畴化形成了基本感知过程和更高层次认知之间的基本界面。通过比较各种形式模型的相对能力，以解释观察到的数据的定性和定量方面，这项理论工作将测试明确规定的假设的基本机制的感知分类。目前的工作重点是具体的记忆类别实例（称为范例）的作用，在知觉分类的形式化建议的基于范例的扩散模型（EBDM）。该模型结合了Nosofsky的广义语境范畴化模型、Nosofsky和Palmeri的基于范例的范畴化和自动性随机游走模型、Logan的自动性实例理论和拉特克利夫的扩散模型的元素。根据所提出的模型，类别表示的存储的样本，一个特定的类别的证据是一个函数的相对总和的相似性所呈现的项目存储的样本，和类别的反应是由一个连续时间的扩散过程所驱动的检索样本信息。初步的工作表明，该模型能够定性和定量的分类准确率和分类响应时间在各种条件下，相对较少的自由参数。一些实证研究计划对比EBDM的预测与其他竞争框架围绕基于原型，规则和决策边界的类别表示。新的理论进展也概述了如何感知信息可能会演变超时在一个特定的分类插曲。

AbstractPalmieri, ThomasBCS-9910756Perceptual Categorization and Memory Any time we decide that some visually presented object is a terrier ratherthan a collie, a bottle rather than a jar, or a tree rather than a shrub, we are making categorization decisions by comparing the perceptual attributes of an object with information about categories that have been acquired previously through experience. Developing psychological theories of perceptual categorization requires an understanding of what information is provided by the perceptual system, how that information is compared with category information that has been previously acquired, what kinds of representations are stored in memory, how category representations change with experience, and how classification decisions are made on the basis of evidence for various categories. Perceptual categorization forms a fundamental interface between basic perceptual processes and higher-level cognition. By comparing the relative abilities of various formal models to account for qualitative and quantitative aspects of observed data, this theoretical work will test well-specified hypotheses about the fundamental mechanisms of perceptual categorization. The present work focuses on the role of specific remembered category instances (referred to exemplars) in perceptual categorization as formalized by a proposed exemplar-based diffusion model (EBDM). This model combines elements of Nosofsky's generalized context model of categorization, Nosofsky and Palmeri's exemplar-based random walk model of categorization and automaticity, Logan's instance theory of automaticity, and Ratcliff's diffusion model under a single theoretical framework. According to the proposed model, categories are represented in terms of stored exemplars, evidence for a particular category is a function of the relative summed similarity of a presented item to stored exemplars, and category responses are determined by a continuous-time diffusion process driven by retrieved exemplar information. Preliminary work shows the model able to qualitatively and quantitatively account for both categorization accuracy and categorization response times under a variety of conditions with relatively few free parameters. Several empirical studies are planned to contrast the predictions of the EBDM with other competing frameworks centered around category representations based on prototypes, rules, and decision boundaries. New theoretical advancements are also outlined that specify how perceptual information might evolve overtime within a particular categorization episode.