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

2020 ◽  
Author(s):  
Rahul Sharma ◽  
Bernardete Ribeiro ◽  
Alexandre Miguel Pinto ◽  
Amilcar F cardoso
Keyword(s):  
Training Data ◽  
Geometric Feature ◽  
Automatic Identification ◽  
Abstract Concept ◽  
Abstract Concepts ◽  
Concept Representation ◽  
Concrete Concepts ◽  
Hyper Space ◽  
Hierarchy Generation ◽  
Iris Data

The term Concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. The Concepts are also studied computationally 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, the IRIS data was used to demonstrate: the RAN's modeling; flexibility in concept identifier choice; and deep hierarchy generation. Data from IoT's Human Activity Recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with 8 UCI benchmarks and the comparisons with 5 Machine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RAN's 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 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 Abstract Concept Learning in Cognitive Robots

2021 ◽  
Author(s):  
Alessandro Di Nuovo ◽  
Angelo Cangelosi
Keyword(s):  
Concept Learning ◽  
Cognitive Robotics ◽  
Abstract Concept ◽  
Abstract Concepts ◽  
Knowledge Based ◽  
Abstract Knowledge ◽  
Benchmark Datasets ◽  
Artificial Learning ◽  
Concrete Concepts ◽  
Cognitive Robots

Abstract Purpose of Review Understanding and manipulating abstract concepts is a fundamental characteristic of human intelligence that is currently missing in artificial agents. Without it, the ability of these robots to interact socially with humans while performing their tasks would be hindered. However, what is needed to empower our robots with such a capability? In this article, we discuss some recent attempts on cognitive robot modeling of these concepts underpinned by some neurophysiological principles. Recent Findings For advanced learning of abstract concepts, an artificial agent needs a (robotic) body, because abstract and concrete concepts are considered a continuum, and abstract concepts can be learned by linking them to concrete embodied perceptions. Pioneering studies provided valuable information about the simulation of artificial learning and demonstrated the value of the cognitive robotics approach to study aspects of abstract cognition. Summary There are a few successful examples of cognitive models of abstract knowledge based on connectionist and probabilistic modeling techniques. However, the modeling of abstract concept learning in robots is currently limited at narrow tasks. To make further progress, we argue that closer collaboration among multiple disciplines is required to share expertise and co-design future studies. Particularly important is to create and share benchmark datasets of human learning behavior.

scholarly journals Situational systematicity: A role for schema in understanding the differences between abstract and concrete concepts

2019 ◽  
Author(s):  
Charles P. Davis ◽  
Gerry Altmann ◽  
Eiling Yee
Keyword(s):  
Concept Learning ◽  
Brain Regions ◽  
Control Network ◽  
Abstract Concept ◽  
Abstract Concepts ◽  
Critical Determinant ◽  
Per Se ◽  
Concrete Concepts ◽  
Insight Into ◽  
Concrete Concept

Abstract concepts differ from concrete concepts in a number of ways. Here, we focus on what we refer to as situational systematicity: The objects and relations that constitute an abstract concept (e.g., justice) are more dispersed through space and time than are the objects and relations that typically constitute a concrete concept (e.g., chair); a larger set of objects and relations might potentially constitute an abstract concept than a concrete one; and exactly which objects and relations constitute a concept is likely more context-dependent for abstract than for concrete concepts. We thus refer to abstract concepts as having low situational systematicity. We contend that situational systematicity, rather than abstractness per se, may be a critical determinant of the cognitive, behavioral, and neural phenomena typically associated with concepts. We also contend that investigating concepts through the lens of schema provides insight into the situation-based dynamics of concept learning and representation, and into the functional significance of the interactions between brain regions that make up the schema control network.

scholarly journals Abstract concepts and the suppression of arbitrary episodic context

2019 ◽  
Author(s):  
Charles P. Davis ◽  
Pedro M. Paz-Alonso ◽  
Gerry Altmann ◽  
Eiling Yee
Keyword(s):  
Source Memory ◽  
Abstract Concept ◽  
Abstract Concepts ◽  
Male And Female ◽  
Female Voices ◽  
Concrete Concepts ◽  
Concrete Words

Context is important for abstract concept processing, but a mechanism by which it is encoded and re-instantiated with concepts is unclear. We used a source-memory paradigm to determine whether episodic context is attended more when processing abstract concepts. Experiment 1 presented abstract and concrete words in colored boxes at encoding. At test, memory for the frame color was worse for abstract concepts, counter to our predictions. Experiment 2 showed the same pattern when colored boxes were replaced with male and female voices. Experiment 3 presented words from encoding in the same or different box color to determine whether a greater advantage is conferred by context retention in memory for abstract concepts. There was instead a disadvantage: abstract concepts were less likely to be identified when the encoding color was retained at test. Concrete concepts are more sensitive to simple episodic detail, and in abstract concepts, arbitrary context may be suppressed.

scholarly journals Boundaries to grounding abstract concepts

2018 ◽  
Vol 373 (1752) ◽  
pp. 20170132 ◽  
Author(s):  
Diane Pecher ◽  
René Zeelenberg
Keyword(s):  
Abstract Concepts ◽  
Concept Representation ◽  
Motor Processing ◽  
Image Schemas ◽  
Sensory Motor ◽  
Concrete Concepts ◽  
The Brain ◽  
Concrete Representations ◽  
Processing Language

Grounded theories of cognition claim that concept representation relies on the systems for perception and action. The sensory-motor grounding of abstract concepts presents a challenge for these theories. Some accounts propose that abstract concepts are indirectly grounded via image schemas or situations. Recent research, however, indicates that the role of sensory-motor processing for concrete concepts may be limited, providing evidence against the idea that abstract concepts are grounded via concrete concepts. Hybrid models that combine language and sensory-motor experience may provide a more viable account of abstract and concrete representations. We propose that sensory-motor grounding is important during acquisition and provides structure to concepts. Later activation of concepts relies on this structure but does not necessarily involve sensory-motor processing. Language is needed to create coherent concepts from diverse sensory-motor experiences. This article is part of the theme issue ‘Varieties of abstract concepts: development, use and representation in the brain’.

Effects of Context on Processing Emotionally Neutral Abstract and Concrete Concepts

2016 ◽  
Vol 44 (7) ◽  
pp. 1191-1200 ◽  
Author(s):  
Liusheng Wang ◽  
Hongmei Qiu ◽  
Jianjun Yin
Keyword(s):  
College Students ◽  
Lexical Decision ◽  
Lexical Decision Task ◽  
Emotional Valence ◽  
Decision Task ◽  
Abstract Concepts ◽  
Context Condition ◽  
Word Context ◽  
Concrete Concepts ◽  
Concrete Words

The abstractness effect describes the phenomenon of individuals processing abstract concepts faster and more accurately than they process concrete concepts. In this study, we explored the effects of context on how 43 college students processed words, controlling for the emotional valence of the words. The participants performed a lexical decision task in which they were shown individual abstract and concrete words, or abstract and concrete words embedded in sentences. The results showed that in the word-context condition the participants' processing of concrete concepts improved, whereas in the sentence-context condition their processing of abstract concepts improved. These findings support the embodied cognition theory of concept processing.

scholarly journals How well do similarity measures predict priming in abstract and concrete concepts?

2018 ◽  
Author(s):  
Maria Montefinese ◽  
Erin Michelle Buchanan ◽  
David Vinson
Keyword(s):  
Word Association ◽  
Semantic Representation ◽  
Similarity Measures ◽  
Semantic Features ◽  
Association Strength ◽  
Abstract Concepts ◽  
Strategic Processes ◽  
Different Types ◽  
Two Measures ◽  
Concrete Concepts

Models of semantic representation predict that automatic priming is determined by associative and co-occurrence relations (i.e., spreading activation accounts), or to similarity in words' semantic features (i.e., featural models). Although, these three factors are correlated in characterizing semantic representation, they seem to tap different aspects of meaning. We designed two lexical decision experiments to dissociate these three different types of meaning similarity. For unmasked primes, we observed priming only due to association strength and not the other two measures; and no evidence for differences in priming for concrete and abstract concepts. For masked primes there was no priming regardless of the semantic relation. These results challenge theoretical accounts of automatic priming. Rather, they are in line with the idea that priming may be due to participants’ controlled strategic processes. These results provide important insight about the nature of priming and how association strength, as determined from word-association norms, relates to the nature of semantic representation.

scholarly journals Drawing with Metaphors. Mediating ideational content in drawing through metaphors

2013 ◽  
Vol 6 (3) ◽  
Author(s):  
Berit Ingebrethsen
Keyword(s):  
Abstract Concept ◽  
Abstract Concepts ◽  
Visual Metaphors ◽  
Linguistic Research ◽  
Cognitive Linguistic ◽  
Educational Issue ◽  
Metaphor Theory ◽  
Different Types ◽  
George Lakoff ◽  
The Subject

It is not easy to express abstract concepts, such as time and society, in a drawing. The subject of this article is rooted in the educational issue of visually expressing themes represented by abstract concepts. However, it is possible to find means and devices to express such ideas. This article shows how metaphors can be used to express such ideas visually. Cognitive linguistic research argues that metaphors are crucial in the verbal communication of abstract concepts. This article also attempts to show that metaphors are important in visual communication. The cognitive linguistic metaphor theory of George Lakoff and Mark Johnson is used here to investigate how metaphors are used to construct meaning in the drawings of cartoonist and illustrator Finn Graff and artist Saul Steinberg. The article presents a few examples of how visual devices structure the abstract concept of time. It then proceeds to explain how symbols function as metonymies and provides an overview of the different types of metaphors and how they are used to express meaning in drawings. The article concludes by attempting to provide new insights regarding the use of visual metaphors.

Applying machine learning to satellite imagery and vessel-tracking data to detect chronic oil pollution from ships at sea and identify the polluters

2021 ◽  
Author(s):  
Jona Raphael ◽  
Ben Eggleston ◽  
Ryan Covington ◽  
Tatianna Evanisko ◽  
Sasha Bylsma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Oil Spills ◽  
Oil Pollution ◽  
Satellite Image ◽  
Petroleum Products ◽  
Training Data ◽  
Automatic Identification ◽  
Identification System ◽  
Sar Imagery ◽  
Pollution Events

<p><strong>Operational oil discharges from ships</strong>, also known as “bilge dumping,” have been identified as a major source of petroleum products entering our oceans, cumulatively exceeding the largest oil spills, such as the Exxon Valdez and Deepwater Horizon spills, even when considered over short time spans. However, we still don’t have a good estimate of</p><ul><li>How much oil is being discharged;</li> <li>Where the discharge is happening;</li> <li>Who the responsible vessels are.</li> </ul><p>This makes it difficult to prevent and effectively respond to oil pollution that can damage our marine and coastal environments and economies that depend on them.</p><p> </p><p>In this presentation we will share SkyTruth’s recent work to address these gaps using machine learning tools to detect oil pollution events and identify the responsible vessels when possible. We use a convolutional neural network (CNN) in a ResNet-34 architecture to perform <strong>pixel segmentation</strong> on all incoming <strong>Sentinel-1 synthetic aperture radar</strong> (SAR) imagery to classify slicks. Despite the satellites’ incomplete oceanic coverage, we have been detecting an average of <strong>135 vessel slicks per month</strong>, and have identified several geographic hotspots where oily discharges are occurring regularly. For the images that capture a vessel in the act of discharging oil, we rely on an <strong>Automatic Identification System</strong> (AIS) database to extract details about the ships, including vessel type and flag state. We will share our experience</p><ul><li>Making sufficient training data from inherently sparse satellite image datasets;</li> <li>Building a computer vision model using PyTorch and fastai;</li> <li>Fully automating the process in the Amazon Web Services (AWS) cloud.</li> </ul><p>The application has been running continuously since August 2020, has processed over 380,000 Sentinel-1 images, and has populated a database with more than 1100 high-confidence slicks from vessels. We will be discussing <strong>preliminary results</strong> from this dataset and remaining challenges to be overcome.</p><p> </p><p>Our objective in making this information and the underlying code, models, and training data <strong>freely available to the public</strong> and governments around the world is to enable public pressure campaigns to improve the prevention of and response to pollution events. Learn more at https://skytruth.org/bilge-dumping/</p>

EXPRESS: Articulatory suppression delays processing of abstract words: the role of inner speech

2021 ◽  
pp. 174702182110536
Author(s):  
Chiara Fini ◽  
Gian Daniele Zannino ◽  
Matteo Orsoni ◽  
Giovanni Augusto Carlesimo ◽  
Mariagrazia Benassi ◽  
...  
Keyword(s):  
Articulatory Suppression ◽  
Judgment Task ◽  
Inner Speech ◽  
Dominant Hand ◽  
Conceptual Representation ◽  
Abstract Concepts ◽  
Single Object ◽  
Abstract Words ◽  
Concrete Concepts

Compared to concrete concepts, like “book”, abstract concepts expressed by words like “justice” are more detached from sensorial experiences, even though they are also grounded in sensorial modalities. Abstract concepts lack a single object as referent and are characterized by higher variability both within and across participants. According to the Word as Social Tool (WAT) proposal, owing to their complexity, abstract concepts need to be processed with the help of inner language. Inner language can namely help participants to re-explain to themselves the meaning of the word, to keep information active in working memory, and to prepare themselves to ask information from more competent people. While previous studies have demonstrated that the mouth is involved during abstract concepts’ processing, both the functional role and the mechanisms underlying this involvement still need to be clarified. We report an experiment in which participants were required to evaluate whether 78 words were abstract or concrete by pressing two different pedals. During the judgment task, they were submitted, in different blocks, to a baseline, an articulatory suppression, and a manipulation condition. In the last two conditions, they had to repeat a syllable continually and to manipulate a softball with their dominant hand. Results showed that articulatory suppression slowed down the processing of abstract more than that of concrete words. Overall results confirm the WAT proposal’s hypothesis that abstract concepts processing involves the mouth motor system and specifically inner speech. We discuss the implications for current theories of conceptual representation.

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