Exemplars, prototypes, similarities, and rules in category representation: an example of hierarchical bayesian analysis

This article demonstrates the potential of using hierarchical Bayesian methods to relate models and data in the cognitive sciences. This is done using a worked example that considers an existing model of category representation, the Varying Abstraction Model (VAM), which attempts to infer the representations people use from their behavior in category learning tasks. The VAM allows for a wide variety of category representations to be inferred, but this article shows how a hierarchical Bayesian analysis can provide a unifying explanation of the representational possibilities using 2 parameters. One parameter controls the emphasis on abstraction in category representations, and the other controls the emphasis on similarity. Using 30 previously published data sets, this work shows how inferences about these parameters, and about the category representations they generate, can be used to evaluate data in terms of the ongoing exemplar versus prototype and similarity versus rules debates in the literature. Using this concrete example, this article emphasizes the advantages of hierarchical Bayesian models in converting model selection problems to parameter estimation problems, and providing one way of specifying theoretically based priors for competing models.     

Differential effects of regulatory fit on category learning

Motivation affects the degree to which people engage in tasks as well as the processes that they bring to bear. We explore the proposal that a fit between a person’s situationally induced self-regulatory focus and the reward structure of the task that they are pursuing supports greater flexibility in processing than does a mismatch between regulatory focus and reward structure. In two experiments, we prime regulatory focus and manipulate task reward structure. Our participants perform a rule-based learning task whose solution requires flexible strategy testing as well as an information-integration task for which flexible strategy use hinders learning. Across two experiments, we predict and obtain a three-way interaction between regulatory focus, reward structure, and task. Relative to a mismatch, a match leads to better rule-based task performance, but worse performance on the information-integration task. We relate these findings to other work on motivation and choking under pressure.     

Creating cluster‐specific purchase profiles from point‐of‐sale scanner data and geodemographic clusters: improving category management at a major US grocery chain

In the retail grocery industry, category management is the process of managing categories of products for greater profitability and customer value. Category management is a data‐driven process and, as a result, can benefit from point‐of‐sale (POS) scanner data. This paper describes the results of a one‐year project that shows how to use POS scanner data and geodemographic clusters to improve the practice of category management at Price Chopper, a large US grocery chain. The paper demonstrates how to merge POS scanner data with geodemographic clusters to create detailed purchase profiles that provide valuable information to category managers. It also discusses the trials and tribulations of using scanner data and provides several findings as implications (eg store‐specific promotions should be more effective than chain‐wide promotions for stores servicing a small number of geodemographic clusters with distinct shopping profiles) that supermarket managers can immediately use to improve existing promotional strategies. The paper's contents should be relevant to academicians and practitioners interested in improving the practice of category management in the UK, USA, Western Europe and Australia. Copyright © 2004 Henry Stewart Publications.     

View-invariant object category learning, recognition, and search: how spatial and object attention are coordinated using surface-based attentional shrouds

How does the brain learn to recognize an object from multiple viewpoints while scanning a scene with eye movements? How does the brain avoid the problem of erroneously classifying parts of different objects together? How are attention and eye movements intelligently coordinated to facilitate object learning? A neural model provides a unified mechanistic explanation of how spatial and object attention work together to search a scene and learn what is in it. The ARTSCAN model predicts how an object's surface representation generates a form-fitting distribution of spatial attention, or "attentional shroud". All surface representations dynamically compete for spatial attention to form a shroud. The winning shroud persists during active scanning of the object. The shroud maintains sustained activity of an emerging view-invariant category representation while multiple view-specific category representations are learned and are linked through associative learning to the view-invariant object category. The shroud also helps to restrict scanning eye movements to salient features on the attended object. Object attention plays a role in controlling and stabilizing the learning of view-specific object categories. Spatial attention hereby coordinates the deployment of object attention during object category learning. Shroud collapse releases a reset signal that inhibits the active view-invariant category in the What cortical processing stream. Then a new shroud, corresponding to a different object, forms in the Where cortical processing stream, and search using attention shifts and eye movements continues to learn new objects throughout a scene. The model mechanistically clarifies basic properties of attention shifts (engage, move, disengage) and inhibition of return. It simulates human reaction time data about object-based spatial attention shifts, and learns with 98.1% accuracy and a compression of 430 on a letter database whose letters vary in size, position, and orientation. The model provides a powerful framework for unifying many data about spatial and object attention, and their interactions during perception, cognition, and action.     

Children's suggestibility for an instance of a repeated event versus a unique event: the effect of degree of association between variable details

Are children who experience an event repeatedly more suggestible about an instance of the event than children who experience it once? Researchers have answered this question both in the affirmative and in the negative. In this study, we hypothesized that the degree of association between details that changed across instantiations of the event would help to explain the discrepancy. Preschoolers (4- and 5-year-olds) and first graders (6- and 7-year-olds) participated in either a single play session or four repeated play sessions, each of which contained 16 critical details. Across play sessions in the repeat-event condition, half of the critical details were associated and half were not associated. During a biasing interview 2 weeks later, children were misinformed about half of the critical details. The next day, children answered free and cued recall questions about the target play session. Among older children, repeat-event participants were more suggestible than single-event participants, especially for high-association details. Among younger children, repeat-event participants were more suggestible than single-event participants for low-association details. Consistent with some current theories of children's memory, older children were more suggestible than younger children.     

What's in a name? Typicality and relatedness effects in children

We studied how category typicality and out-of-category relatedness affect speeded category verification (vote "yes" if pictured object is clothing) in typically developing 4- to 14-year-olds and adults. Stimuli were typical and atypical category objects (e.g., pants, glove) and related and unrelated out-of-category objects (e.g., necklace, soup). Typical and unrelated out-of-category objects exhibited preferential processing (faster reaction times and fewer errors). Variations in typicality and relatedness disproportionately influenced children's performance, with developmental improvement associated with both verbal and nonverbal factors. Underextension versus overextension errors seemed to be associated with independent factors, namely multifaceted maturational factors versus receptive vocabulary skill, respectively. Errors were infrequent, suggesting spontaneous taxonomic classification by all participants. An experiment with printed words in adults replicated results, indicating that typicality and relatedness effects reflected organizational principles of the semantic system, not picture-related processes. This research establishes the viability of an online approach to assessing automatic components of semantic organization in children.     

图画与中、英文字词识别加工的比较

通过对不同材料进行加工的比较来探讨人脑的信息加工机制是认知心理学采用的主要研究方法之一。本研究采用了两个实验。实验一比较了图画、中文和英文字词在命名和分类任务中的差异。结果发现:中文是介于图画和英文之间的一种符号系统,在命名任务中,英文反应最快,图画反应最慢,中文居中;而在分类任务中,反应的快慢趋势却截然相反。实验二进一步探讨了言语及非言语信息的表征结构及加工特性,使用了图-图、图-字、字-图、字-字此四种组合材料进行命名一致性和语义分类两种任务,结果发现:字特征组(字-字,字-图)在命名一致性判断任务中的反应优于图特征组(图-图、图-字),而在语义一致性判断任务中则相反,这一结果很难用双编码理论和共同编码理论来解释。语词信息对语音码的接通是直接的,而对语义码的接通却是间接的,图画信息反之,而且由于某种策略产生了图特征组与字特征组的区别。     

SC-IAT范式下大学生感恩的内隐性

目的：研究探讨了单类内隐联想测验（SC-IAT）测量大学生感恩的可行性,并分析内隐感恩与外显感恩的关系。方法：采用inquisit3.0.2.0编制SC-IAT感恩测量程序,测量了大学生的内隐感恩;采用青少年感恩量表（AGS）测量了大学生的外显感恩,并运用相关技术分析两者之间的关系。结果：采用SC-IAT测量大学生感恩的内隐效应显著,内隐感恩不存在性别、生源地和独生与否等人口统计学指标上的显著差异,大学生的内隐感恩与外显感恩之间相关不显著。结论：（1）大学生整体上存在着积极的内隐感恩;（2）内隐感恩与外显感恩相互分离,是两个不同的建构。     

Category theory,logic and formal linguistics: Some connections,old and new

We seize the opportunity of the publication of selected papers from the Logic, categories, semantics workshop to survey some current trends in logic, namely intuitionistic and linear type theories, that interweave categorical, geometrical and computational considerations. We thereafter present how these rich logical frameworks can model the way language conveys meaning.