AIMC Topic: Concept Formation

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Spatial relation categorization in infants and deep neural networks.

Cognition Feb 8, 2024
Spatial relations, such as above, below, between, and containment, are important mediators in children's understanding of the world (Piaget, 1954). The development of these relational categories in infancy has been extensively studied (Quinn, 2003) y...
Humans Infant Child Concept Formation Neural Networks, Computer Machine Learning
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Compositional diversity in visual concept learning.

Cognition Jan 14, 2024
Humans leverage compositionality to efficiently learn new concepts, understanding how familiar parts can combine together to form novel objects. In contrast, popular computer vision models struggle to make the same types of inferences, requiring more...
Spatial Learning Neural Networks, Computer Concept Formation Bayes Theorem Humans Generalization, Psychological
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Optimizing dense feed-forward neural networks.

Neural networks : the official journal of the International Neural Network Society Dec 11, 2023
Deep learning models have been widely used during the last decade due to their outstanding learning and abstraction capacities. However, one of the main challenges any scientist has to face using deep learning models is to establish the network's arc...
Concept Formation Knowledge Neural Networks, Computer Climate Change
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DHI-GAN: Improving Dental-Based Human Identification Using Generative Adversarial Networks.

IEEE transactions on neural networks and learning systems Nov 30, 2023
In this work, a novel semisupervised framework is proposed to tackle the small-sample problem of dental-based human identification (DHI), achieving enhanced performance via a "classifying while generating" paradigm. A generative adversarial network (...
Humans Neural Networks, Computer Learning Concept Formation Forensic Anthropology
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Choosing human over AI doctors? How comparative trust associations and knowledge relate to risk and benefit perceptions of AI in healthcare.

Risk analysis : an official publication of the Society for Risk Analysis Sep 18, 2023
The development of artificial intelligence (AI) in healthcare is accelerating rapidly. Beyond the urge for technological optimization, public perceptions and preferences regarding the application of such technologies remain poorly understood. Risk an...
Physicians Trust Humans Artificial Intelligence Cognition Concept Formation
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Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning.

PLoS computational biology Aug 25, 2023
The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Agents can be endowed with an inductive bias towards abstraction through...
Machine Learning Intelligence Concept Formation Knowledge Humans Neural Networks, Computer
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Resolving the associative learning paradox by category learning in pigeons.

Current biology : CB Feb 7, 2023
A wealth of evidence indicates that humans can engage two types of mechanisms to solve category-learning tasks: declarative mechanisms, which involve forming and testing verbalizable decision rules, and associative mechanisms, which involve gradually...
Columbidae Conditioning, Classical Artificial Intelligence Animals Concept Formation Learning
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Neural representational geometry underlies few-shot concept learning.

Proceedings of the National Academy of Sciences of the United States of America Oct 17, 2022
Understanding the neural basis of the remarkable human cognitive capacity to learn novel concepts from just one or a few sensory experiences constitutes a fundamental problem. We propose a simple, biologically plausible, mathematically tractable, and...
Humans Learning Animals Neural Networks, Computer Macaca Primates Visual Pathways Plastics Concept Formation
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The neural representation of abstract words may arise through grounding word meaning in language itself.

Human brain mapping Jul 15, 2021
In order to describe how humans represent meaning in the brain, one must be able to account for not just concrete words but, critically, also abstract words, which lack a physical referent. Hebbian formalism and optimization are basic principles of b...
Cerebral Cortex Adult Female Adolescent Concept Formation Male Humans Young Adult Models, Statistical Psycholinguistics Brain Mapping Machine Learning Reading Semantics Magnetoencephalography
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Incremental Concept Learning via Online Generative Memory Recall.

IEEE transactions on neural networks and learning systems Jul 6, 2021
The ability to learn more concepts from incrementally arriving data over time is essential for the development of a lifelong learning system. However, deep neural networks often suffer from forgetting previously learned concepts when continually lear...
Mental Recall Concept Formation Online Systems Machine Learning Algorithms Humans Neural Networks, Computer
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