Conceptual Hierarchies in a Flat Attractor Network: Dynamics of Learning and Computations

O’Connor, Christopher M; Cree, George S.; Masamune, Ken

doi:10.1111/j.1551-6709.2009.01024.x

Cited by 44 publications

(58 citation statements)

References 68 publications

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“…One way in which objects may be related is taxonomically, or within categories of things that share semantic features (e.g., Collins and Loftus, 1975; Rosch and Mervis, 1975; Rogers and McClelland, 2004; O'Connor et al, 2009). For example, taxonomically-related zebras and lions share visual features (e.g., eyes, four legs) and encyclopedic features (e.g., live on the savanna).…”

Section: Introductionmentioning

confidence: 99%

The role of action representations in thematic object relations

Tsagkaridis

Watson

Jax

et al. 2014

Front. Hum. Neurosci.

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A number of studies have explored the role of associative/event-based (thematic) and categorical (taxonomic) relations in the organization of object representations. Recent evidence suggests that thematic information may be particularly important in determining relationships between manipulable artifacts. However, although sensorimotor information is on many accounts an important component of manipulable artifact representations, little is known about the role that action may play during the processing of semantic relationships (particularly thematic relationships) between multiple objects. In this study, we assessed healthy and left hemisphere stroke participants to explore three questions relevant to object relationship processing. First, we assessed whether participants tended to favor thematic relations including action (Th+A, e.g., wine bottle—corkscrew), thematic relationships without action (Th-A, e.g., wine bottle—cheese), or taxonomic relationships (Tax, e.g., wine bottle—water bottle) when choosing between them in an association judgment task with manipulable artifacts. Second, we assessed whether the underlying constructs of event relatedness, action relatedness, and categorical relatedness determined the choices that participants made. Third, we assessed the hypothesis that degraded action knowledge and/or damage to temporo-parietal cortex, a region of the brain associated with the representation of action knowledge, would reduce the influence of action on the choice task. Experiment 1 showed that explicit ratings of event, action, and categorical relatedness were differentially predictive of healthy participants' choices, with action relatedness determining choices between Th+A and Th-A associations above and beyond event and categorical ratings. Experiment 2 focused more specifically on these Th+A vs. Th-A choices and demonstrated that participants with left temporo-parietal lesions, a brain region known to be involved in sensorimotor processing, were less likely than controls and tended to be less likely than patients with lesions sparing that region to use action relatedness in determining their choices. These data indicate that action knowledge plays a critical role in processing of thematic relations for manipulable artifacts.

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Section: Introductionmentioning

confidence: 99%

The role of action representations in thematic object relations

Tsagkaridis

Watson

Jax

et al. 2014

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…We used the model developed by Cree and colleagues (Cree et al, 1999; see also O’Connor, Cree, & McRae, 2009), and we would expect similar behavior from other attractor dynamical models of semantic processing (e.g., Plaut & Booth, 2000; Rogers & McClelland, 2004). The model architecture is shown in Figure 1.…”

Section: Attractor Model Simulationmentioning

confidence: 99%

“…Following O’Connor et al (2009), we set learning rate to 0.01 and added momentum (0.9) after the first 10 training epochs. The model was trained using continuous recurrent backpropagation through time (Pearlmutter, 1995) until it correctly activated over 95% of the appropriate semantic feature units (i.e., the model activated over 95% of features that were produced by participants in the feature norming study; by this point, the model also correctly deactivated over 99% of nonproduced features), which was approximately 40 training epochs.…”

Section: Attractor Model Simulationmentioning

confidence: 99%

Dynamics of activation of semantically similar concepts during spoken word recognition

Mirman

Magnuson

2009

Memory & Cognition

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Semantic similarity effects provide critical insight into the organization of semantic knowledge and the nature of semantic processing. In the present study, we examined the dynamics of semantic similarity effects by using the visual world eyetracking paradigm. Four objects were shown on a computer monitor, and participants were instructed to click on a named object, during which time their gaze position was recorded. The likelihood of fixating competitor objects was predicted by the degree of semantic similarity to the target concept. We found reliable, graded competition that depended on degree of target-competitor similarity, even for distantly related items for which priming has not been found in previous priming studies. Time course measures revealed a consistently earlier fixation peak for near semantic neighbors relative to targets. Computational investigations with an attractor dynamical model, a spreading activation model, and a decision model revealed that a combination of excitatory and inhibitory mechanisms is required to obtain such peak timing, providing new constraints on models of semantic processing.

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“…Our behavioral results were broadly consistent with the claim made by Cree et al (2006) that rare features play a privileged role in semantic processing. We examined whether their model (Cree et al, 2006; see also Cree, McRae, & McNorgan, 1999; O’Connor, Cree, & McRae, 2009) correctly predicts this outcome. A schematic depiction of the model architecture is shown in Figure 2.…”

Section: Simulationsmentioning

confidence: 99%

The effect of frequency of shared features on judgments of semantic similarity

Mirman

Magnuson

2009

Psychonomic Bulletin & Review

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Insight into the structure of conceptual knowledge can be gleaned by examining how statistical regularities in the semantic structure of concepts affect semantic processing. Two similarity judgment experiments revealed that pairs of concepts sharing relatively rare features were judged to be more similar than concepts sharing an equal number of relatively frequent features. Simulations confirmed that these results are consistent with a recurrent connectionist network model of semantic processing in which units corresponding to rare features are activated more quickly and accurately than units corresponding to frequent features. These results support the hypothesis that rare features play a privileged role in the processing of word meanings.The structure of conceptual representations is a critical and controversial issue in theories of language and cognitive processing. One important controversy centers on how feature-concept regularities influence processing. Sensitivity to statistical regularities is an important factor at all levels of language processing, including form (e.g., Saffran, 2003), meaning (e.g., Landauer & Dumais, 1997), and grammar (e.g., McClelland & Patterson, 2002). In general, frequently occurring elements are found to exert a stronger influence on processing and memory than are rarely occurring elements. In the domain of word meaning, one example is the finding that prime-target pairs that share features that frequently co-occur across concepts (such as CAT and BEAVER, which share the features has-fur and has-whiskers) exhibit larger priming effects than those that share features that do not frequently co-occur (such as PIN and COIN, which share the features is-small and is-made-of-metal; McRae, de Sa, & Seidenberg, 1997). McRae et al. (1997) found that this pattern, like other examples of sensitivity to statistical regularities, emerges in simulations of attractor-based connectionist networks. Indeed, coherent covariation -when groups of features are consistently present or absent across concepts-is a central aspect of learning and processing in connectionist models of semantic processing (e.g., McClelland & Rogers, 2003).Interestingly, there is also evidence that rare or idiosyncratic features play a particularly important role in semantic knowledge. The importance of idiosyncratic features has a long history in theories of semantic processing, going at least as far back as the foundational work of Rosch and Mervis (1975), who described the importance of cue validity for conceptual categorization. Cue validity is the conditional probability of concept c, given feature f: p(c | f); by Bayes's rule, this is equal to p(f | c) * p(c)/p(f). It is typical to make the simplifying assumption that the probability of a feature is either 0 or 1 (i.e., a concept either has the feature or does not), meaning that, for a present feature, p(f | c) = 1. Under the further simplifying assumption that concepts are equally likely, p(c) = 1/C, where C is the total number of concepts in the corpus, and p...

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Conceptual Hierarchies in a Flat Attractor Network: Dynamics of Learning and Computations

Cited by 44 publications

References 68 publications

The role of action representations in thematic object relations

The role of action representations in thematic object relations

Dynamics of activation of semantically similar concepts during spoken word recognition

The effect of frequency of shared features on judgments of semantic similarity

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