Mengcun Gao, Brandon M. Turner, Vladimir M. Sloutsky
{"title":"The Role of Attention in Category Representation","authors":"Mengcun Gao, Brandon M. Turner, Vladimir M. Sloutsky","doi":"10.1111/cogs.13438","DOIUrl":null,"url":null,"abstract":"<p>Numerous studies have found that selective attention affects category learning. However, previous research did not distinguish between the contribution of focusing and filtering components of selective attention. This study addresses this issue by examining how components of selective attention affect category representation. Participants first learned a rule-plus-similarity category structure, and then were presented with category priming followed by categorization and recognition tests. Additionally, to evaluate the involvement of focusing and filtering, we fit models with different attentional mechanisms to the data. In Experiment 1, participants received rule-based category training, with specific emphasis on a single deterministic feature (D feature). Experiment 2 added a recognition test to examine participants’ memory for features. Both experiments indicated that participants categorized items based solely on the D feature, showed greater memory for the D feature, were primed exclusively by the D feature without interference from probabilistic features (P features), and were better fit by models with focusing and at least one type of filtering mechanism. The results indicated that selective attention distorted category representation by highlighting the D feature and attenuating P features. To examine whether the distorted representation was specific to rule-based training, Experiment 3 introduced training, emphasizing all features. Under such training, participants were no longer primed by the D feature, they remembered all features well, and they were better fit by the model assuming only focusing but no filtering process. The results coupled with modeling provide novel evidence that while both focusing and filtering contribute to category representation, filtering can also result in representational distortion.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cogs.13438","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"102","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/cogs.13438","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
Abstract
Numerous studies have found that selective attention affects category learning. However, previous research did not distinguish between the contribution of focusing and filtering components of selective attention. This study addresses this issue by examining how components of selective attention affect category representation. Participants first learned a rule-plus-similarity category structure, and then were presented with category priming followed by categorization and recognition tests. Additionally, to evaluate the involvement of focusing and filtering, we fit models with different attentional mechanisms to the data. In Experiment 1, participants received rule-based category training, with specific emphasis on a single deterministic feature (D feature). Experiment 2 added a recognition test to examine participants’ memory for features. Both experiments indicated that participants categorized items based solely on the D feature, showed greater memory for the D feature, were primed exclusively by the D feature without interference from probabilistic features (P features), and were better fit by models with focusing and at least one type of filtering mechanism. The results indicated that selective attention distorted category representation by highlighting the D feature and attenuating P features. To examine whether the distorted representation was specific to rule-based training, Experiment 3 introduced training, emphasizing all features. Under such training, participants were no longer primed by the D feature, they remembered all features well, and they were better fit by the model assuming only focusing but no filtering process. The results coupled with modeling provide novel evidence that while both focusing and filtering contribute to category representation, filtering can also result in representational distortion.
大量研究发现,选择性注意会影响类别学习。然而,以往的研究并未区分选择性注意中的聚焦和过滤成分的贡献。本研究通过考察选择性注意的成分如何影响类别表征来解决这一问题。被试首先学习了一个规则加相似度的类别结构,然后接受类别引物,接着进行分类和识别测试。此外,为了评估聚焦和过滤的参与情况,我们将不同的注意机制模型与数据进行了拟合。在实验 1 中,被试接受了基于规则的分类训练,重点是单一的确定性特征(D 特征)。实验 2 增加了识别测试,以考察参与者对特征的记忆。这两项实验都表明,被试只根据 D 特征对项目进行分类,对 D 特征表现出更强的记忆力,被试只受到 D 特征的启发而不受概率特征(P 特征)的干扰,而且被试更适合具有聚焦和至少一种过滤机制的模型。结果表明,选择性注意通过突出 D 特征和削弱 P 特征,扭曲了类别表征。为了研究扭曲的表征是否是基于规则的训练所特有的,实验 3 引入了强调所有特征的训练。在这种训练下,被试不再受到 D 特征的引诱,他们对所有特征的记忆都很好,而且在假设只有聚焦而没有过滤过程的模型中,被试对这些特征的拟合效果更好。这些结果与模型相结合,提供了新的证据,证明虽然聚焦和过滤都有助于类别表征,但过滤也会导致表征失真。