Analyzing the factors underlying the structure and computation of the meaning of chipmunk, cherry, chisel, cheese, and cello (and many other such concrete nouns).

Cree, George S.; Masamune, Ken

doi:10.1037/0096-3445.132.2.163

Cited by 504 publications

(612 citation statements)

References 152 publications

(277 reference statements)

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“…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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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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“…Here we model the intermediate semantic knowledge with features from an independently performed feature norming study (Cree and McRae, 2003), where participants were explicitly asked to list features of 541 words. Our results suggest that (1) object features derived from a behavioral feature norming study can explain a significant portion of the systematic variance in the neural activity observed in our object-contemplation task.…”

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

“…Computational linguists have demonstrated that a word's meaning is captured to some extent by the distribution of words and phrases with which it commonly co-occurs (Church and Hanks, 1990). Psychologists have studied word meaning in many ways, one of which is through feature norming studies (Cree and McRae, 2003) in which human participants are asked to list the features they associate with various words. There are also approaches that treat the intermediate semantic representation as hidden (or latent) variables and use techniques like the traditional PCA and factor analysis, or the…”

Section: Introductionmentioning

confidence: 99%

Quantitative modeling of the neural representation of objects: How semantic feature norms can account for fMRI activation

2011

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Recent multivariate analyses of fMRI activation have shown that discriminative classifiers such as Support Vector Machines (SVM) are capable of decoding fMRI-sensed neural states associated with the visual presentation of categories of various objects. However, the lack of a generative model of neural activity limits the generality of these discriminative classifiers for understanding the underlying neural representation. In this study, we propose a generative classifier that models the hidden factors that underpin the neural representation of objects, using a multivariate multiple linear regression model. The results indicate that object features derived from an independent behavioral feature norming study can explain a significant portion of the systematic variance in the neural activity observed in an object-contemplation task. Furthermore, the resulting regression model is useful for classifying a previously unseen neural activation vector, indicating that the distributed pattern of neural activities encodes sufficient signal to discriminate differences among stimuli. More importantly, there appears to be a double dissociation between the two classifier approaches and within-versus between-participants generalization. Whereas an SVM-based discriminative classifier achieves the best classification accuracy in within-participants analysis, the generative classifier outperforms an SVM-based model which does not utilize such intermediate representations in between-participants analysis. This pattern of results suggests the SVM-based classifier may be picking up some idiosyncratic patterns that do not generalize well across participants and that good generalization across participants may require broad, large-scale patterns that are used in our set of intermediate semantic features. Finally, this intermediate representation allows us to extrapolate the model of the neural activity to previously unseen words, which cannot be done with a discriminative classifier.

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“…In such a task, it is beneficial to the participant to be able to recall or recognize a visual feature that is unique to the item to which the target word refers (e.g., a zebra's stripes). Furthermore, Cree and McRae (2003) found that the proportion of distinguishing features in a category predicted several major trends in the patterns of impairment observed in category-specific semantic deficits patients.Given the importance of distinctive features in explaining patient performance, they have a privileged status in many theories of semantic organization. For example, the sensory/ functional theory (Warrington & McCarthy, 1987) is based on the idea that sensory and functional information is particularly distinctive for living and nonliving things, respectively, and that category-specific impairments arise, therefore, from damage to either the sensory or functional knowledge processing pathways.…”

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confidence: 94%

“…In this article, we focus on the former definition (i.e., relative to all basic-level concepts). This dimension has been measured in various ways and given several names over the years, including cue validity (Bourne & Restle, 1959), distinguishingness (Cree & McRae, 2003), distinctiveness (Garrard, Lambon Ralph, Hodges, & Patterson, 2001), and informativeness (Devlin, Gonnerman, Andersen, & Seidenberg, 1998). To date, distinctive features have been especially useful in accounting for performance in concepts and categorization tasks, and in explaining the patterns of deficits observed in patients with category-specific semantic deficits.…”

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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

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214

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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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Analyzing the factors underlying the structure and computation of the meaning of chipmunk, cherry, chisel, cheese, and cello (and many other such concrete nouns).

Cited by 504 publications

References 152 publications

Exploring commonalities across participants in the neural representation of objects

Exploring commonalities across participants in the neural representation of objects

Quantitative modeling of the neural representation of objects: How semantic feature norms can account for fMRI activation

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

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