Semantic feature production norms for a large set of living and nonliving things

By Ken McRae, George S. Cree, Mark S. Seidenberg, Chris McNorgan. 2005

1,118 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Developmental and Educational Psychology, Social Psychology, Cognitive Science & Psychology

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Citations in the corpus

Hypothesis Only Baselines in Natural Language Inference (2018, neutral): “Stud-8 These are “Nobody”, “alone”, “no”, and “empty”. ies in eliciting norming data are prone to repeated responses across subjects (McRae et al., 2005) (see discussion in § 2 of (Zhang et al., 2017)).”
Multi-Agent Cooperation and the Emergence of (Natural) Language (2016, methodology): “The objects in our images were categorized into 20 broader categories (such as weapon and mammal ) by McRae et al. (2005).”
Ordinal Common-sense Inference (2016, methodology): “re not likely to generate them from an initial prompt.6 The construction of SICK (Sentences Involving Compositional Knowledge) made use of existing paraphrastic sentence pairs (descriptions by differ6McRae et al. (2005): For example, features such as <is larger than a tulip>or <moves faster”
Multimodal Distributional Semantics (2013, methodology): “As just one piece of evidence for this claim, the widely used subject-generated semantic norms of McRae et al. (2005) contain 3,594 distinct perceptual features in total, and, of these, 3,099 (86”
Distributional Memory: A General Framework for Corpus-Based Semantics (2010, positive): “Similar property lists, collected from subjects in elicitation tasks, are widely used in cognitive science as surrogates of mental features (Garrard et al. 2001; McRae et al. 2005; Vinson and Vigliocco 2008).”
Are Distributional Representations Ready for the Real World? Evaluating Word Vectors for Grounded Perceptual Meaning (2017, positive): “The dataset groups features into several perceptual and non-perceptual categories: taxonomic, encyclo-pedic, function, visual-motion, visual-form and - surface, visual-colour, sound, tactile, and taste (McRae et al., 2005).”
See No Evil, Say No Evil: Description Generation from Densely Labeled Images (2014, methodology): “Labels gathered in this way are a type of feature norms (McRae et al., 2005), which have been used in the cognitive science literature to approximate human perception and were recently used as a visual proxy in distributional semantics (Silberer and Lapata, 2012).”
Do Neural Language Representations Learn Physical Commonsense? (2019, neutral): “Forty-three properties come from MR (McRae et al., 2005), and seven are new.”
Visual Commonsense in Pretrained Unimodal and Multimodal Models (2022, background): “There has also been earlier work on the McRae feature norms (McRae et al., 2005), in which human annotators wrote down attributes that describe the meaning of words.”
Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding (2012, background): “And indeed in feature norming exercises (Garrard et al., 2001; McRae et al., 2005; Vinson and Vigliocco, 2008) where participants are asked to list the properties of a word, the aggregate descriptions are typically limited to approximately 10-20 characteristics for a given concrete concept, with lim”
CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge (2021, background): “Our work relates to judging plausibility of events [Forbes and Choi, 2017, Wang et al., 2018], closely tied to inferences accessible from feature norms [McRae et al., 2005], but again these past efforts do not focus on judgments around speciﬁc entities.”
Grounded Models of Semantic Representation (2012, methodology): “In order to isolate the inﬂuence of the inference method from the resulting semantic representation we evaluated the inferred perceptual vectors on their own by computing their correlation with the original feature distributions in McRae et al.’s (2005) norms.”
Iconicity as a General Property of Language: Evidence from Spoken and Signed Languages (2010, background): “Based on English speaker-generated feature norms (McRae et al., 2005), Vinson et al.”
How to marry a star: Probabilistic constraints for meaning in context (2020, methodology): “In the literature, selectional constraints have sometimes been construed as hard constraints, for example in Katz & Fodor (1964) and recently in Asher (2011), and sometimes as soft preferences, for example in McRae et al. (1997), who view selectional constraints as verb-specific concepts that can ju”
Object-Specific Semantic Coding in Human Perirhinal Cortex (2014, methodology): “All objects were chosen to depict concepts from an anglicized version of the McRae production norms (McRae et al., 2005; Taylor et al., 2012) to calculate object-specific semantic similarity measures.”
Learning Grounded Meaning Representations with Autoencoders (2014, methodology): “The dataset is a taxonomy of 636 visual attributes (e.g., has wings , made of wood ) and nearly 700K images from ImageNet (Deng et al., 2009) describing more than 500 of McRae et al.’s (2005) nouns.”