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

Masamune, Ken; Cree, George S.; Seidenberg, Mark S.; McNorgan, Chris

doi:10.3758/bf03192726

Cited by 881 publications

(1,384 citation statements)

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“…Semantic representations were taken from the McRae et al (2005) semantic feature production norms. In those norms, there are 2,526 unique features listed for 541 concepts.…”

Section: Resultsmentioning

confidence: 99%

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Distinctive features hold a privileged status in the computation of word meaning: Implications for theories of semantic memory.

Cree¹,

McNorgan²,

Masamune³

2006

Journal of Experimental Psychology: Learning, Memory, and Cogni

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The authors present data from 2 feature verification experiments designed to determine whether distinctive features have a privileged status in the computation of word meaning. They use an attractor-based connectionist model of semantic memory to derive predictions for the experiments. Contrary to central predictions of the conceptual structure account, but consistent with their own model, the authors present empirical evidence that distinctive features of both living and nonliving things do indeed have a privileged role in the computation of word meaning. The authors explain the mechanism through which these effects are produced in their model by presenting an analysis of the weight structure developed in the network during training.Keywords semantic memory; distinctive features; connectionist attractor network; conceptual structure theory The issue of how word meaning is stored in the mind, and computed rapidly when needed, is central both to theories of language comprehension and production and to understanding knowledge deficits in patients with neural impairments. Although we are far from a complete understanding, significant progress has been made since Tulving (1972) first introduced the term "semantic memory." Much of this progress can be attributed to the implementation and development of semantic memory models and the advancement of tools (e.g., semantic feature production norms and word co-occurrence statistics) that enable the rigorous testing of predictions generated from these models.The first models of semantic memory were hierarchical network theory (Collins & Quillian, 1969) and spreading activation theory (Collins & Loftus, 1975). Although both have been extremely influential and have provided predictions for numerous behavioral studies, both have lost favor owing to a number of well-understood and documented flaws (see Rogers & McClelland, 2004, for a recent review). A number of theories of semantic structure have been proposed as alternatives, including the sensory/functional theory (Warrington & Copyright 2006 by the American Psychological Association Correspondence concerning this article should be addressed to George S. Cree, Department of Life Sciences (Psychology), University of Toronto at Scarborough, Toronto, Ontario M1C 1A4, Canada. gcree@utsc.utoronto.ca. McCarthy, 1987), the conceptual structure account (Tyler & Moss, 2001), and the domainspecific hypothesis (Caramazza & Shelton, 1998). Theories of semantic processing have also been proposed, often framed in terms of connectionist networks, and include the work of Plaut and Shallice (1993), Rumelhart and Todd (1993), Rogers and McClelland (2004), and McRae, de Sa, and Seidenberg (1997). Current research focuses on discriminating among these models, integrating the structural and processing components, and developing them to account for behavioral data. NIH Public AccessOne way in which these models are being tested concerns whether concept-feature and feature-feature statistical relations are embedded in the semantic sys...

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“…Semantic representations were taken from the McRae et al (2005) semantic feature production norms. In those norms, there are 2,526 unique features listed for 541 concepts.…”

Section: Resultsmentioning

confidence: 99%

“…The items were chosen from McRae, Cree, Seidenberg, and McNorgan's (2005) feature production norms for 541 concepts. Participants were given features from McRae et al's norms and were asked to generate names of concepts that possess that feature.…”

Section: Concept Generation Normsmentioning

confidence: 99%

Distinctive features hold a privileged status in the computation of word meaning: Implications for theories of semantic memory.

Cree¹,

McNorgan²,

Masamune³

2006

Journal of Experimental Psychology: Learning, Memory, and Cogni

Self Cite

120

214

View full text Add to dashboard Cite

show abstract

“…Similarities based on the internal representation of objects derived from fMRI data can be compared with internal representations derived from empirically obtained judgment data or other models of semantic space, for example those based on feature norming studies [Cree and McRae, 2003;McRae et al, 2005], or lexical co-occurrence models [Andrews et al, 2009;Church and Hanks, 1990;Landauer and Dumais, 1997;Lund and Burgess, 1996] using representational similarity analysis [Kriegeskorte et al, 2008].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Exploring commonalities across participants in the neural representation of objects

Shinkareva

Malave

Just

et al. 2011

Human Brain Mapping

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The question of whether the neural encodings of objects are similar across different people is one of the key questions in cognitive neuroscience. This article examines the commonalities in the internal representation of objects, as measured with fMRI, across individuals in two complementary ways. First, we examine the commonalities in the internal representation of objects across people at the level of interobject distances, derived from whole brain fMRI data, and second, at the level of spatially localized anatomical brain regions that contain sufficient information for identification of object categories, without making the assumption that their voxel patterns are spatially matched in a common space. We examine the commonalities in internal representation of objects on 3T fMRI data collected while participants viewed line drawings depicting various tools and dwellings. This exploratory study revealed the extent to which the representation of individual concepts, and their mutual similarity, is shared across participants. Hum Brain Mapp 33:1375-1383,

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“…Feature-feature statistical co-occurrence was estimated using semantic feature production norms for 541 basic-level concrete concepts (McRae et al, 2005). In the norming task, participants listed features when given the names of approximately 20 concepts (30 participants listed features for each concept).…”

Section: Methodsmentioning

confidence: 99%

“…Thus, feature correlations obtained from feature production norms presumably reflect realworld statistical structure, although their magnitudes may also be influenced in part by causal knowledge. The features used in the present experiments were drawn from McRae et al's (2005) semantic feature norms; therefore, the statistical correlations reflect tendencies for people to list these features together for specific concepts. Murphy and Medin (1985) stated-we believe correctly-that causal relationships might influence what features are listed in such norms and in this way might affect calculations of statistical correlations between features.…”

Section: The Interplay Between Themmentioning

confidence: 99%

Feature-feature causal relations and statistical co-occurrences in object concepts

et al. 2007

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

Cited by 881 publications

References 44 publications

Distinctive features hold a privileged status in the computation of word meaning: Implications for theories of semantic memory.

Distinctive features hold a privileged status in the computation of word meaning: Implications for theories of semantic memory.

Exploring commonalities across participants in the neural representation of objects

Feature-feature causal relations and statistical co-occurrences in object concepts

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