Semantic feature production norms for manipulable objects

ABSTRACT

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.

KEYWORDS:

• Semantic norms
• features
• manipulable objects
• semantic similarity
• within-category

Acknowledgements

We would like to thank Ema Leitão, Catarina Senra, and Adriana Martins for their help in data collection and Arthur Pilacinski for his help with OpenSesame.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Although potentially using multi-level modelling would be an interesting approach to our data, our current design does not allow for using such an approach. The standard recommendation for a multi-level sample size is to have 50 groups with 30 observations in each group. In our case, however, we have more groups (80 objects) but significantly fewer observations (two or four depending on whether it is the General or Action databases).

Additional information

Funding

This work was supported by an European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [Starting grant number 802553]; “ContentMAP” to J.A.. D.V. was supported by a Foundation for Science and Technology of Portugal Doctoral grant [SFRH/BD/137737/2018]. A.H. was supported by the European Research Council Starting Grant “ContentMAP” [802553].