Encoding, converting sensory information to memory, is an essential process humans require for everyday tasks. Semantic encoding is one of the ways in which we associate meaning to the raw data around us, which can then be stored as memory and recalled later.
Semantic encoding is a cognitive process whereby we encode sensory input from our environment to give it meaning. Sensory information in our surroundings is converted into a meaningful form so that you can remember it. It is one of the first steps in memory encoding.
Semantic encoding is one of the best ways in which we remember things and can recall them later. Different proposed models explain how concepts are organized in the brain, and we use these structures to encode meaning.
What Is Semantic Encoding?
Semantic encoding is when the brain takes information from our senses and encodes this into meaningful information. In this way, the focus is not on the perceptual aspects of the data retrieved from our surroundings.
The word encoding also requires some explanation. Encoding refers to converting input from sensory stimuli into a form that can be processed and remembered. Regarding semantic encoding, sensory information is converted into meaning which can be applied to a context.
Semantic encoding allows us to make sense of the world around us. It is a type of cognitive encoding that provides the experience of understanding the meaning of things we come across daily. Sensory information can come from any of our senses – touch, hearing, taste, visual, or smell.
One example is information from word meanings. When someone says the word /cat/ to you, you immediately know what they are referring to and perhaps understand what they mean in a specific context. The context of the meaning of information is essential in semantic encoding. It can influence the way we remember certain concepts.
Another meaning can be reading written information. For instance, when you read the word /tea/, you can encode the meaning from it in the context that it appears in, in this case, a phrase or sentence. Is the tea referring to the context of drinking, pouring, making, etc.
What about a word with two different meanings, like a crane? It can refer to the bird or the machinery used in building. So if someone shouts out, "Look at the crane flying over there", you will certainly understand that they are talking about the bird. Semantic encoding helps us to encode which meaning is the one relevant, using the context at hand.
Semantic encoding can also be linked to deep encoding as it plays a massive role in memory encoding, especially long-term memory.
Role Of Semantic Encoding In Memory
Semantic encoding is one of the four types of encoding that fall under the primary term – memory encoding. Memory encoding is when input from our surroundings is converted into a form that can be stored in the brain and recalled in a short or longer time.
Semantic encoding plays a vital role in memory encoding as it is the process that attaches meaning to specific items. This meaning can be stored in the brain to be recalled whenever you need to use the information. The other three types of encoding for memory are visual, acoustic, and elaborative encoding.
Studies have shown that semantic encoding can make remembering information easier. Memory storage and retrieval occur by associating meaning to things more easily than if we didn't attach any meaning to them.
Imagine you read a text received from a friend that says, "see you later". In this context that you are aware of, you are meeting with your friend later for tea. Semantic encoding ensures that you remember the meaning of the written words in this context, rather than only remembering the words themselves.
Another example of semantic encoding in memory is remembering a phone number based on some attribute of the person you got it from, like their name. In other words, specific associations are made between the sensory input (the phone number) and the context of the meaning (the person's name).
Models Of Semantic Encoding
Researchers have proposed some models of semantic encoding to suggest how it works on a neuro-cognitive level. Collins and Quillian’s network model is a well-known model and can be seen as one of the better-understood ones.
Collins And Quillian’s Network Model
This model was developed around 1969, and the theory suggested a semantic network in the brain that looks pretty much like a spider’s web. This web roughly represents information in the form of different concepts.
In the web, various nodes refer to the main concepts (or meanings), and then between the nodes, some links make up the connections between the concepts. For instance, imagine the core node (the middle of the web) is a "mammal".
A mammal can have links to various other pieces of information and receive links from other nodes. These links are seen as arrows in the network, pointing from one concept to the next. For instance, the mammal node can connect to another node called "animal" where the link between the two represents "is an” (mammal is an animal).
Another link could be that a mammal has a vertebra. In this way, we can see that one node (mammal) can have several associations with other nodes (representing different meanings like animal or vertebra). As the web (the semantic network) continues to grow, more and more nodes are added, and more connections between these nodes appear.
The connections between the nodes are a way to organize the network and form associations between the different concepts. Simply put, this model proposes how to manage meaning and information in our brain.
Furthermore, Collin and Quillian said this semantic network works hierarchically based on linguistics (language). The central node (in this case, mammals) would be the most straightforward concept, moving outward in the web to form connections with more complex information (vertebra).
Comparing Semantic Features Model
Another model for semantic encoding was suggested a bit later in 1974. The semantic features model suggested that hierarchy is not so important when we want to organize concepts in the brain, but rather the semantic features of the concepts.
The model suggests that you compare these features and, in this way, establish the meaning of the information. In this model, different concepts are compared directly with one another instead of attributing many features to one concept.
In the semantic network proposed by Collins and Quillian, the concept of a cat would be linked to other concepts like fur, four legs, pet, and ears to understand that it is part of the core concept of "mammal". In this semantic features model, the concept of a cat would only be compared to other mammals, like an elephant, where the features may be four legs, wild animal, and ears.
Using this theory, we can use the similarities and differences between concepts to understand what they are and attribute meaning to them. There is no linguistic hierarchy where one concept is more complex than the other.
Criticism Of The Semantic Encoding Models
Although there have been other semantic encoding theories, these two have received a lot of attention and criticism. Firstly, the semantic network (Collins and Quillian) was criticized by cognitive psychologists for being too simple.
The suggestion was that semantic encoding is more complex in its' organization that having a hierarchical structure of concepts and associations. However, studies could show that the semantic network model was observed empirically.
A study presented the research participants with two different statements – “Dolphin is an animal” and “Dolphin is a fish” and timed the seconds it took for them to respond yes. The results indicated that it generally took people more time to respond to the more complex statement further away in the linguistic hierarchy.
So, in this example, participants took more time to respond to the statement “Dolphin is an animal”. The findings of this experiment showed that people responded differently to certain concepts, possibly suggesting a hierarchical system in place for concepts and links.
On the other hand, the main critique of the semantic features model is that it hasn't been shown empirically to the degree that the semantic networks have.
Ways To Optimize Semantic Encoding
We do semantic encoding every day several times a day. It is a natural way to remember information using meaningful associations. However, not all concepts are encoded in the same way. Some information you may find is straightforward to encode, but others may be more difficult.
There are two main strategies by which we can optimize semantic encoding. Why would we want to do this? If sensory input is not encoded correctly, remembering it later will be more difficult. These techniques can help you remember specific concepts, like studying a subject that you find challenging.
Mnemonics For Semantic Encoding
Mnemonics is a specific technique involving any device or strategy to help memory, like acronyms or associations. For instance, a popular one uses the acronym ROYGBIV to remember the rainbow colors.
Another popular system is the peg-word system. This system is when you associate (peg) a word you need to remember with easier-to-remember words. The words usually begin with the same letter. The following is an example of the peg-word system used to aid memory of the taxonomic categories of biology (kingdom, phylum, class, order, family, genus, species) – King Phillip Came Over For Good Soup.
Music mnemonics is a sub-category of mnemonics that uses music and song to remember concepts. Due to the overlap between music and language, music mnemonics is a great way to aid semantic encoding and memory.
During your life, you have probably used music mnemonics several times, perhaps without knowing. Children learn their alphabet letters and other concepts via song. Related to this technique is rhyming mnemonics.
As the name suggests, rhyme is used to remember information, similarly to music. Rhyme creates a pattern that can take on a sing-song structure, which helps solidify concepts into memory.
Chunking For Semantic Encoding
Chunking is another way to optimize our semantic encoding systems. It includes breaking large pieces of information into smaller parts (chunks). These smaller parts are easier to remember and remain meaningful.
Chunking is a versatile strategy where you can find patterns in the smaller sections that make up the whole while organizing the units into a system. A simple way to think about this is by making a grocery list. The list may have several items which is too much to remember.
However, if you break it into smaller sections and then organize it by grocery aisle or food category, it will become easier to remember.
A chunk can be seen as one of the puzzle pieces that contribute to making up the entire puzzle. For instance, remembering a phone number can use chunking as a technique. Break up the number into smaller chunks, and once you have memorized each one, it is easier to put them together.
Where Is Semantic Encoding In The Brain?
Different parts of the brain are responsible for various functions and encoding of information. Neuro-imaging studies have examined where specific encoding and processes occur in the brain. Research is still ongoing, but the prefrontal cortical regions appear to be activated during tasks that call for semantic encoding.
Specifically, the left inferior prefrontal cortex (including Brodmann's areas 45, 46, and 47) was activated during semantic encoding. Furthermore, the left temporal brain regions are also involved in semantic encoding.
The medial temporal lobe is involved in both semantic and perceptual encoding. Activation in the temporal regions applies to both verbal and non-verbal stimuli. Depending on the type of sensory input, other brain areas will also be activated before and during semantic encoding.
The hippocampus is another brain structure that can play a role in semantic encoding. It plays a role along the frontal cortex, helping to process and determine incoming sensory input.
Semantic encoding is our ability to take sensory information and convert it in the brain to give it meaning. It plays a significant role in memory, especially long-term memory. Different models have proposed how semantic encoding works in networks or by comparing semantic features.