H13-311_V3.5 · Question #351
In a neural network based on connectionism, each node can express a specific meaning.
The correct answer is B. FALSE. B is correct because connectionism is defined by distributed representations - meaning concepts and knowledge are encoded across many nodes simultaneously, not localized in any single node. No individual node "means" anything specific in isolation; meaning emerges from patterns…
Question
In a neural network based on connectionism, each node can express a specific meaning.
Options
- ATRUE
- BFALSE
How the community answered
(47 responses)- A30% (14)
- B70% (33)
Explanation
B is correct because connectionism is defined by distributed representations - meaning concepts and knowledge are encoded across many nodes simultaneously, not localized in any single node. No individual node "means" anything specific in isolation; meaning emerges from patterns of activation spread across the network.
Why A is wrong: Assigning specific meaning to individual nodes describes a symbolic AI or localist approach (think classic rule-based systems or grandmother cell models), not connectionist networks. Connectionism was specifically developed as an alternative to that node-per-concept architecture.
Memory tip: Think of connectionism as a crowd - just as no single person in a crowd represents "the event," no single node represents a concept. The meaning lives in the pattern of the whole group, not in any one member.
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