Perceptual Categorization and Memory

感知分类和记忆每当我们确定某些视觉呈现的物体是梗而不是牧羊犬，是瓶子而不是罐子，或者是树而不是灌木时，我们是通过将物体的感知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了解知觉系统提供了什么信息，这些信息如何与先前获得的类别信息进行比较，记忆中存储了哪些类型的表征，类别表征如何随着经验的变化而变化，以及分类决策是如何根据各种类别的证据做出的。知觉范畴构成了基本知觉过程和高级认知之间的基本接口。通过比较各种形式模型解释观察数据的定性和定量方面的相对能力，这项理论工作将检验关于知觉分类基本机制的明确假设。本文主要研究特定记忆类别实例(称为样本)在感知分类中的作用，该分类由一个提出的基于样本的扩散模型(EBDM)形式化。该模型将诺索夫斯基的广义语境范畴化模型、诺索夫斯基和帕尔梅里的基于范例的随机游走范畴化和自动机模型、洛根的自动机实例理论和拉特克利夫的扩散模型的元素结合在一个单一的理论框架下。根据所提出的模型，类别用存储的样本来表示，特定类别的证据是呈现项与存储的样本的相对总和相似度的函数，类别响应由检索到的样本信息驱动的连续时间扩散过程来确定。初步工作表明，该模型能够定性和定量地解释在自由参数相对较少的各种条件下的分类精度和分类响应时间。计划进行几项实证研究，将EBDM的预测与其他竞争框架进行比较，这些框架以基于原型、规则和决策边界的类别表示为中心。还概述了新的理论进步，具体说明了感知信息在特定分类情节中如何随着时间的推移而演变。

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.