Proaction Laboratory, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal.
Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal.
Cogn Neuropsychol. 2023 May-Jun;40(3-4):167-185. doi: 10.1080/02643294.2023.2279185. Epub 2024 Jan 12.
Feature generation tasks and feature databases are important for understanding how knowledge is organized in semantic memory, as they reflect not only the kinds of information that individuals hold about objects but also how objects are conceptually represented. Traditionally, semantic norms focus on a variety of object categories and, as a result, have a small number of concepts per semantic category. Here, our main goal is to create a more fine-grained feature database exclusively for one category of objects-manipulable objects. This database contributes to the understanding of within-category, content-specific processing. To achieve this, we asked 130 participants to freely generate features for 80 manipulable objects and another group of 32 participants to generate action features for the same objects. We then compared our databases with other published semantic norms and found high similarity between them. In our databases, we calculated the similarity between objects in terms of visual, functional, encyclopaedic, and action feature types using Spearman correlation, Baker's gamma index, and cophenetic correlation. We discovered that objects were grouped in a distinctive and meaningful way according to feature type. Finally, we tested the validity of our databases by asking three groups of participants to perform a feature verification experiment while manipulating production frequency. Our results demonstrate that participants can recognize and associate the features of our databases with specific manipulable objects. Participants were faster to verify high-frequency features than low-frequency features. Overall, our data provide important insights into how we process manipulable objects and can be used to further inform cognitive and neural theories of object processing and identification.
特征生成任务和特征数据库对于理解知识在语义记忆中的组织方式非常重要，因为它们不仅反映了个体对物体的信息种类，还反映了物体的概念表示方式。传统上，语义规范侧重于各种物体类别，因此每个语义类别只有少量的概念。在这里，我们的主要目标是为一个物体类别——可操作性物体创建一个更细粒度的特征数据库。该数据库有助于理解类别内的、特定于内容的处理。为了实现这一目标，我们要求 130 名参与者为 80 个可操作性物体自由生成特征，另一组 32 名参与者为相同物体生成动作特征。然后，我们将我们的数据库与其他已发表的语义规范进行比较，发现它们之间高度相似。在我们的数据库中，我们使用 Spearman 相关系数、Baker 的伽马指数和Cophenetic 相关系数，根据视觉、功能、百科全书和动作特征类型来计算物体之间的相似性。我们发现，根据特征类型，物体以独特而有意义的方式进行分组。最后，我们通过让三组参与者在操纵生产频率的同时执行特征验证实验，来测试我们数据库的有效性。我们的结果表明，参与者可以识别并将我们数据库的特征与特定的可操作性物体联系起来。参与者在验证高频特征时比低频特征更快。总的来说，我们的数据提供了对我们如何处理可操作性物体的重要见解，并可用于进一步为物体处理和识别的认知和神经理论提供信息。
