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Semantic networks and spreading activation

Semantic networks store information in our brains as connected ideas. Concepts are represented as nodes linked by their relatedness. The first model was hierarchical, from general to specific categories. Collins and Loftus proposed a modified version based on individual experience. Activating one concept also activates related ones, a process called spreading activation. Created by Carole Yue.

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Video transcript

- [Instructor] In order to solve problems and make decisions, you often have to access information already stored in your brain. But how is that information stored? There are a lot of possibilities, but the one we're going to focus on in this video is the semantic network approach, which argues that concepts are organized in your mind in terms of connected ideas. You can kind of think of this as parallel to how information might be stored in a computer. You have different nodes, which represent your concepts, and those nodes are connected by links. Depending on how connected the nodes are, the links might be shorter, for closely related ideas, or longer, for less related ideas. Let me show you an example to make this a little more concrete. The first semantic network model was hierarchical, meaning that they thought concepts were organized from higher order categories down to lower order categories and their exemplars. So let's start with a general category: animal. So that's this node here. Animal might be linked to other nodes, such as bird or fish. And bird might be further linked to canary, bluebird, or more distantly, ostrich. Ostrich is probably not the first thing you think of when you think of a bird, so that's why its link is longer. But simple labels aren't the only type of knowledge we store. We can store characteristics and properties of concepts at each node. According to the principle of cognitive economy, which just means that our brain is efficient, we store these properties at the highest possible node. For example then, instead of storing can breathe at each animal's node, we store that property just at the animal node. More specific characteristics such as sings, or long legs, would be stored at lower level nodes. One pretty interesting piece of supporting evidence for this type of hierarchical organization comes from how long it takes people to verify certain statements. In this kind of test, you say a statement, and ask people to tell you if it's true or not. As you can imagine, people verify a canary is a canary pretty quickly. It takes them a little longer to verify a canary is a bird, and even longer to verify that a canary is an animal. So this is some support that we store things in a hierarchical manner, because the longer it takes us to verify a connection between two nodes, then the longer those links are, or the more nodes we have to go through to make that link. However, this isn't true for all types of animals, or even all types of categories. For example, people tend to verify that a pig is an animal faster than a pig is a mammal. Because of this issue and some other problems, Collins and Loftus proposed a modified version of the semantic network. Rather than a hierarchical organization, this model says that every individual semantic network develops based on their experience and knowledge. So, some links might be longer or shorter for different individuals, and there may be direct links from higher order categories to their exemplars. One pretty cool thing about semantic networks is that it means all the ideas in your head are connected together. So when you activate one concept, you're pulling up related concepts along with it. This general elevation and availability is called spreading activation. For example, if I say "fire engine," not only do you think of a fire engine, but related concepts such as trucks, fire, even the color red become activated, making it easier for you to retrieve or identify those items.