The Centre for Speech, Language and the Brain (CSLB) concept property norms

By Barry Devereux, Lorraine K. Tyler, Jeroen Geertzen, Billi Randall. 2013

172 citations recorded by the source corpus.

Theories of the representation and processing of concepts have been greatly enhanced by models based on information available in semantic property norms. This information relates both to the identity of the features produced in the norms and to their statistical properties. In this article, we introduce a new and large set of property norms that are designed to be a more flexible tool to meet the demands of many different disciplines interested in conceptual knowledge representation, from cognitive psychology to computational linguistics. As well as providing all features listed by 2 or more participants, we also show the considerable linguistic variation that underlies each normalized feature label and the number of participants who generated each variant. Our norms are highly comparable with the largest extant set (McRae, Cree, Seidenberg, & McNorgan, 2005) in terms of the number and distribution of features. In addition, we show how the norms give rise to a coherent category structure. We provide these norms in the hope that the greater detail available in the Centre for Speech, Language and the Brain norms should further promote the development of models of conceptual knowledge. The norms can be downloaded at www.csl.psychol.cam.ac.uk/propertynorms.

Subfields: Cognitive Neuroscience, Developmental and Educational Psychology

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Citations in the corpus

On the Binding Problem in Artificial Neural Networks (2020, background): “(Devereux et al., 2014).”
Are Distributional Representations Ready for the Real World? Evaluating Word Vectors for Grounded Perceptual Meaning (2017, positive): “CSLB We reproduce and extend our results on a second semantic norm dataset collected by the Cambridge Centre for Speech, Language and the Brain (CSLB; Devereux et al., 2014).”
Probing Neural Language Models for Human Tacit Assumptions (2020, methodology): “Designing Probes We construct prompts for evaluating STAs in LMs by leveraging the CSLB Concept Property Norms (Devereux, Tyler, Geertzen, & Randall, 2013), a large extension of the McRae data, containing 638 concepts each linked with roughly 34 associated properties.”
Object-Specific Semantic Coding in Human Perirhinal Cortex (2014, methodology): “One type of representational scheme is provided by models in which the semantic similarity between objects is captured with semantic features (e.g., has legs, made of metal) (Tyler and Moss, 2001; Taylor et al., 2007, 2011; Tyler et al., 2013; Devereux et al., 2013).”
THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images (2019, background): “A wider range and more standardized set of concepts to choose from is provided by category norms [9, 10] or object property norms [71, 72], which can be useful for improving comparability between studies.”
Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations (2019, methodology): “We present a method of analyzing commonsense knowledge in word representations through attribute classification on the semantic norm dataset (Devereux et al., 2014), and compare a contextual model to a traditional word type representation.”