Concept sharing between different concept representation robots

2009 ◽  
Author(s):  
Junji Takahashi ◽  
Kosuke Sekiyama ◽  
Toshio Fukuda
Keyword(s):  
Concept Representation

scholarly journals Commentary on "Congressional Informal Groups as Representative Responsiveness"

1993 ◽  
Vol 14 ◽  
pp. 375-379
Author(s):  
Susan Webb Hammond
Keyword(s):  
Collective Action ◽  
Policy Process ◽  
Theoretical Concept ◽  
Data Set ◽  
Concept Representation ◽  
Institutional Goals ◽  
Interesting Topic ◽  
Informal Groups ◽  
Formal Recognition

“Congressional Informal Groups as Representative Responsiveness” by Arturo Vega focuses on an interesting topic-congressional caucuses-and examines an important theoretical concept-representation-using a new data set that he has gathered. In the contemporary era, congressional caucuses-voluntary groups of members of Congress, without formal recognition in chamber rules or line-item appropriations that seek a role in the policy process-are increasingly salient congressional actors. The number of caucuses has increased dramatically during the 1980s; about 140 operated during the 102nd Congress (1991-1992). It is not surprising that caucuses have flourished during the 1970s and 1980s, two decades of structural decentralization during which members of Congress often pursued individual goals at the expense of collective action. I have argued elsewhere that caucuses, particularly in this environment, assist members in achieving individual goals and also help Congress achieve institutional goals (Hammond 1989). Vega’s focus on caucuses is useful.

scholarly journals Decoding the Information Structure Underlying the Neural Representation of Concepts

2021 ◽  
Author(s):  
Leonardo Fernandino ◽  
Lisa L. Conant ◽  
Colin J. Humphries ◽  
Jeffrey R. Binder
Keyword(s):  
Information Structure ◽  
Conceptual Knowledge ◽  
Neural Representation ◽  
Neural Activation ◽  
Activation Patterns ◽  
Concept Representation ◽  
Native Space ◽  
Distributional Information ◽  
Similarity Structure ◽  
Types Of Information

The nature of the neural code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. Three main types of information have been proposed as candidates for the neural representations of lexical concepts: taxonomic (i.e., information about category membership and inter-category relations), distributional (i.e., information about patterns of word co-occurrence in natural language use), and experiential (i.e., information about sensory-motor, affective, and other features of phenomenal experience engaged during concept acquisition). In two experiments, we investigated the extent to which these three types of information are encoded in the neural activation patterns associated with hundreds of English nouns from a wide variety of conceptual categories. Participants made familiarity judgments on the meaning of written nouns while undergoing functional MRI. A high-resolution, whole-brain activation map was generated for each noun in each participant′s native space. These word-specific activation maps were used to evaluate different representational spaces corresponding to the three types of information described above. In both studies, we found a striking advantage for experience-based models in most brain areas previously associated with concept representation. Partial correlation analyses revealed that only experiential information successfully predicted concept similarity structure when inter-model correlations were taken into account. This pattern of results was found independently for object concepts and event concepts. Our findings indicate that the neural representation of conceptual knowledge primarily encodes information about features of experience, and that - to the extent that it is represented in the brain - taxonomic and distributional information may rely on such an experience-based code.

scholarly journals Abstraction and context in concept representation

2003 ◽  
Vol 358 (1435) ◽  
pp. 1251-1259 ◽  
Author(s):  
James A. Hampton
Keyword(s):  
Cognitive Psychology ◽  
Knowledge Representation ◽  
Conceptual Knowledge ◽  
Context Dependence ◽  
Levels Of Abstraction ◽  
Concept Representation ◽  
Theoretical Dimension

This paper develops the notion of abstraction in the context of the psychology of concepts, and discusses its relation to context dependence in knowledge representation. Three general approaches to modelling conceptual knowledge from the domain of cognitive psychology are discussed, which serve to illustrate a theoretical dimension of increasing levels of abstraction.

scholarly journals Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts

2020 ◽  
Vol 10 (6) ◽  
pp. 1994 ◽  
Author(s):  
Rahul Sharma ◽  
Bernardete Ribeiro ◽  
Alexandre Miguel Pinto ◽  
F. Amílcar Cardoso
Keyword(s):  
Training Data ◽  
Geometric Feature ◽  
Automatic Identification ◽  
Computational Domain ◽  
Abstract Concept ◽  
Abstract Concepts ◽  
Concept Representation ◽  
Data Problem ◽  
Rans Modeling ◽  
Concrete Concepts

The term concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. Concepts are also studied in the computational domain through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of abstract concepts by exploiting the geometrical view of concepts, without supervision. In the article, first, a Toy-data problem was used to demonstrate the RANs modeling. Secondly, we demonstrate the liberty of concept identifier choice in RANs modeling and deep hierarchy generation using the IRIS dataset. Thirdly, data from the IoT’s human activity recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with eight UCI benchmarks and the comparisons with five Machine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RANs hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size.

scholarly journals A Modified Skip-Gram Algorithm for Extracting Drug-Drug Interactions from AERS Reports

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Li Wang ◽  
Wenjie Pan ◽  
QingHua Wang ◽  
Heming Bai ◽  
Wei Liu ◽  
...  
Keyword(s):  
Adverse Event ◽  
Drug Interactions ◽  
Large Scale ◽  
Characteristic Curve ◽  
Language Model ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Concept Representation ◽  
Average Accuracy ◽  
Tenfold Cross Validation

Drug-drug interactions (DDIs) are one of the indispensable factors leading to adverse event reactions. Considering the unique structure of AERS (Food and Drug Administration Adverse Event Reporting System (FDA AERS)) reports, we changed the scope of the window value in the original skip-gram algorithm, then propose a language concept representation model and extract features of drug name and reaction information from large-scale AERS reports. The validation of our scheme was tested and verified by comparing with vectors originated from the cooccurrence matrix in tenfold cross-validation. In the verification of description enrichment of the DrugBank DDI database, accuracy was calculated for measurement. The average area under the receiver operating characteristic curve of logistic regression classifiers based on the proposed language model is 6% higher than that of the cooccurrence matrix. At the same time, the average accuracy in five severe adverse event classes is 88%. These results indicate that our language model can be useful for extracting drug and reaction features from large-scale AERS reports.

Mutual access and mutual dependence of conceptual components

2001 ◽  
Vol 24 (3) ◽  
pp. 490-492 ◽  
Author(s):  
Friedemann Pulvermüller
Keyword(s):  
Information Exchange ◽  
Cognitive Representations ◽  
Cell Assemblies ◽  
Concept Representation ◽  
Mutual Dependence ◽  
Internal Connections

The HIT model comes close to a view suggested by Donald Hebb, that cognitive representations are organized as distributed neuron webs, cell assemblies, whose components are mutually connected and whose internal connections provide continuous information exchange among sub-components of the representation. Two questions are asked related to (1) the organization of internal connections of a concept representation and (2) the conditions under which information exchange between components are assumed in the HIT model.

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