connectionism
E899009
approach in artificial intelligence
computational paradigm
model of cognition
theoretical approach in cognitive science
Connectionism is a cognitive science and artificial intelligence approach that models mental processes using networks of simple, interconnected units whose learning and behavior emerge from patterns of activation and weight adjustment.
All labels observed (1)
| Label | Occurrences |
|---|---|
| connectionism canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11002826 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: connectionism Context triple: [Donald Hebb, influenced, connectionism]
-
A.
Hebbian learning
Hebbian learning is a neurobiological and computational learning principle often summarized as "cells that fire together wire together," where the connection between neurons is strengthened when they are activated simultaneously.
-
B.
Unified Theories of Cognition
Unified Theories of Cognition is a comprehensive cognitive science framework proposed by Allen Newell that seeks to explain diverse mental processes—such as problem solving, memory, and learning—within a single, unified theoretical architecture.
-
C.
cognitive science
Cognitive science is an interdisciplinary field that studies the mind and intelligence by integrating approaches from psychology, neuroscience, computer science, linguistics, philosophy, and related disciplines.
-
D.
Cognitivism
Cognitivism is a psychological and educational theory that explains learning and behavior in terms of internal mental processes such as thinking, memory, and problem-solving.
-
E.
Centre for Neural Computation
The Centre for Neural Computation is a research center focused on understanding the neural mechanisms underlying cognition and behavior through computational and systems neuroscience approaches.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: connectionism Target entity description: Connectionism is a cognitive science and artificial intelligence approach that models mental processes using networks of simple, interconnected units whose learning and behavior emerge from patterns of activation and weight adjustment.
-
A.
Hebbian learning
Hebbian learning is a neurobiological and computational learning principle often summarized as "cells that fire together wire together," where the connection between neurons is strengthened when they are activated simultaneously.
-
B.
Unified Theories of Cognition
Unified Theories of Cognition is a comprehensive cognitive science framework proposed by Allen Newell that seeks to explain diverse mental processes—such as problem solving, memory, and learning—within a single, unified theoretical architecture.
-
C.
cognitive science
Cognitive science is an interdisciplinary field that studies the mind and intelligence by integrating approaches from psychology, neuroscience, computer science, linguistics, philosophy, and related disciplines.
-
D.
Cognitivism
Cognitivism is a psychological and educational theory that explains learning and behavior in terms of internal mental processes such as thinking, memory, and problem-solving.
-
E.
Centre for Neural Computation
The Centre for Neural Computation is a research center focused on understanding the neural mechanisms underlying cognition and behavior through computational and systems neuroscience approaches.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
approach in artificial intelligence
ⓘ
computational paradigm ⓘ model of cognition ⓘ theoretical approach in cognitive science ⓘ |
| alsoKnownAs |
PDP
NERFINISHED
ⓘ
neural network approach ⓘ parallel distributed processing NERFINISHED ⓘ |
| associatedWork | Parallel Distributed Processing volumes NERFINISHED ⓘ |
| contrastsWith |
classical computationalism
ⓘ
rule-based models of cognition ⓘ symbolic AI ⓘ |
| coreIdea |
cognition arises from distributed representations
ⓘ
knowledge is stored in patterns of connection weights ⓘ mental processes are modeled as emergent from networks of simple units ⓘ processing occurs in parallel across many units ⓘ |
| emergedIn | 1980s ⓘ |
| fieldOfStudy |
artificial intelligence
ⓘ
cognitive science ⓘ computational neuroscience NERFINISHED ⓘ |
| influenced |
computational models of language
ⓘ
deep learning ⓘ models of memory ⓘ models of perception ⓘ modern neural network research ⓘ |
| influencedBy |
neuroscience
ⓘ
psychology ⓘ statistical learning theory ⓘ |
| learningMechanism |
Hebbian learning
ⓘ
backpropagation ⓘ error-driven learning ⓘ weight adjustment based on experience ⓘ |
| models |
language processing
ⓘ
learning and generalization ⓘ memory retrieval ⓘ pattern recognition ⓘ |
| notableProponent |
David E. Rumelhart
NERFINISHED
ⓘ
Geoffrey Hinton NERFINISHED ⓘ James L. McClelland NERFINISHED ⓘ |
| philosophicalIssue |
explanatory adequacy for higher-level cognition
ⓘ
relationship between distributed and symbolic representations ⓘ |
| typicalArchitecture |
autoassociative network
ⓘ
feedforward network ⓘ recurrent network ⓘ |
| typicalUnit | simple neuron-like processing unit ⓘ |
| usesConcept |
activation pattern
ⓘ
artificial neural network ⓘ connection weight ⓘ content-addressable memory ⓘ distributed representation ⓘ graceful degradation ⓘ learning rule ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: connectionism Description of subject: Connectionism is a cognitive science and artificial intelligence approach that models mental processes using networks of simple, interconnected units whose learning and behavior emerge from patterns of activation and weight adjustment.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.