Semantic feature production norms for manipulable objects

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 have about objects but also how objects are conceptually parse. 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 finely-tuned 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 structural similarity between them. In our databases, we calculated the similarity between visual, functional, encyclopedic, and action feature types. 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 task 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.