Semantic encoding is a mental process that involves linking meanings or concepts to memories. It can be used to remember information, better comprehend the context of the text, and solve problems.
Semantic encoding allows individuals to recall information more effortlessly by attaching significance to data. Most of the time, its value is drawn from our personal encounters and perceptions.
A semantic encoding example is when you meet a new person and to remember their name, you tell yourself that this is the third person named “Ann” you’ve met this year – this simple story acts as an anecdote you can use during memory recall.
When using semantic encoding for problem-solving, people utilize their existing knowledge and logical connections between pieces of information to reach a solution quicker and more accurately than if they had just relied on rote learning.
Semantic Encoding Definition
Semantic encoding is the process of linking data and information to concepts and meanings to improve understanding and recall.
This mental process helps people associate unfamiliar words or facts with their existing knowledge base and build connections between seemingly unrelated pieces of data.
According to Bedford (2020),
“…semantic encoding is the processing and encoding of sensory input that has a particular meaning, or that can be applied to a context” (p. 122).
Bedford and Sanchez (2021) extend their definition and write that:
“…semantic encoding is a specific type of encoding in which the meaning of something (a word, phrase, picture, event, whatever) is encoded instead of the sound or vision” (p. 115).
Through semantic encoding, one can more easily remember learned material, better comprehend the context of a text, and have an increased capacity for problem-solving.
Simply, semantic encoding is a type of memory encoding that uses meaning and concepts instead of images and sounds to remember information.
Semantic Encoding Examples
- Remembering phone numbers: Many people employ semantic encoding when remembering phone numbers by attaching a meaning or story to the numbers to help make it easier for them to remember. For example, the phone number 333-444-5555 could be remembered by linking the digits to the phrase “3 blind mice” (3-3-4-4-5-5).
- Remembering birthdays and anniversaries: We often create stories or associations with dates when recalling them (such as associating a particular month with events or people). For example, if someone’s birthday is in May, they may remember it by linking the month to their mother or another family member who also has a birthday in May.
- Learning foreign language vocabulary: Associations are used widely in language learning to memorize words more quickly and effectively. For example, connecting pictures or stories to certain words can help us learn vocabulary more easily and remember it better over time.
- Understanding complex concepts: Semantic encoding is important for understanding complex topics like physics, mathematics, and economics. For instance, one can link these formulas to some picture or story to remember some formulas.
- Retaining facts and figures: By forming meaningful associations between data points, people can better retain numerical information like historical dates and populations of countries more reliably without having to refer back to textbooks all the time. So, one can associate the date of World War I with a specific event or picture.
- Memory palace technique: The memory palace technique is an effective method of using spatial encoding to remember larger amounts of data at once, such as details about characters from books, scenes from movies, or elements from scientific theories.
- Mnemonic devices: These strategies involve creating acronyms, phrases, or rhymes out of data points that need remembering. They may include chemical elements on the periodic table or US states capitals. For example, if one heard that “Rhode Island’s capital is Providence,” they might create a phrase like “Rachel in Providence.”
- Driving directions: Many drivers use techniques like street names or nearby landmarks as anchor points when recalling how to get from one place to another– instead of simply remembering every single turn or street name along the way. These reference points simplify navigation by giving you an idea of what’s coming up ahead without having to remember each detail individually beforehand.
- Remembering game rules: Board games, card games, and sports all involve rules that must be memorized to play. To help with this, players might create stories around specific rules or draw pictures representing the steps to take.
- Food recipes: Recipes often require us to combine different ingredients in precise amounts to obtain desired flavors, textures, etc. To remember the correct amounts of each ingredient, cooks may link them to items in their kitchen or other everyday objects. For example, if a recipe calls for “1/4 cup of sugar,” one might link it to a coffee mug or tea cup.
Collins and Quillian’s Network Model of Semantic Encoding
Collins and Quillian’s Network Model of semantic encoding is a framework that suggests that information is organized hierarchically within the brain.
According to this model, learning occurs when we encounter new concepts related to pre-existing knowledge in some way (Collins & Quillian, 1969).
The information stored in memory forms a network– with each node representing a concept and its connections signifying the relationship between that concept and other related topics.
For example, if you had to remember the word “dog,” you might break it down into more specific terms such as “animal,” “canine,” or even “labrador retriever.”
The nodes for these concepts would be connected by arrows representing their relationships (i.e., animal -> canine -> labrador retriever).
By understanding how these related items are linked, users can quickly recall complex ideas moments instead of starting from scratch each time they need it.
This model thus proposes that semantic encoding requires an understanding not only of individual words or phrases but also their relationships with one another within wider semantic networks (Collins & Quillian, 1969).
Collins and Quillian’s Network Model allows us to quickly recognize patterns without searching through multiple levels of detail every time something needs to be recalled.
Due to the interconnected nature of this structure, certain elements may become easier to access for being closer to already established facts (Collins & Quillian, 1969).
Benefits of Semantic Encoding
Semantic encoding provides many benefits for learning and memory formation, including improved retrieval, efficiency, understanding, transferability, and reduced interference.
Here is a brief overview of each benefit:
1. Improved Retrieval
By understanding how concepts are related and how they fit together in larger networks, we can more easily recall items from memory.
It is especially helpful for tasks that require retrieving information quickly or recalling multiple data pieces simultaneously.
2. Increased Efficiency
Semantic encoding centers around quickly recognizing patterns instead of wading through each element individually. So, users can save time by accessing relevant nodes without going through every node in between first (Kirchhoff et al., 2011).
3. Improved Understanding
By understanding the relationship between different concepts, users gain a better overall understanding of the material rather than simply memorizing facts and figures independently (Cherry et al., 2011).
In addition, it allows them to apply the material in new contexts more effectively.
4. Enhanced Transferability
The ability to relate new information to existing knowledge improves retention rates and increases transferability rates. This situation arises because users have internalized more details about the concept than just reciting rote facts from memory.
5. Reduced Interference
By hierarchically organizing knowledge, users reduce interference from newer material that may conflict with older knowledge (Kirchhoff et al., 2011).
Thus, they can spend less time reviewing previously learned information and focus instead on newly encountered topics.
Limitations of Semantic Encoding
Some limitations associated with semantic encoding include dependency on knowledge structure, difficulty in referencing new information, overly complex networks, insensitivity to contextual changes, and limited capacity for deep learning.
Here is a brief overview of each limitation:
1. Dependency on Knowledge Structure
The success of the network model relies heavily upon an existing knowledge structure to properly organize new information (Kumar, 2020).
If a user does not have an adequate understanding of the topics being studied or a lack thereof, it may become difficult to pattern new items within the network accurately.
2. Difficulty in Referencing New Information
When referencing new concepts within the existing framework, it can be difficult for users to identify the exact link between each element– particularly if the connection is not obvious.
In addition, it makes it challenging to organize and recall details properly when necessary.
3. Overly Complex Networks
If networks become too complex, users risk becoming overwhelmed and unable to make sense of all the details they’re presented with. Such a situation leads them to forget important pieces instead of having retained them successfully.
4. Insensitivity to Contextual Changes
Semantic encoding focuses primarily on recognizing patterns rather than analyzing contextual changes, which could affect how items interact with one another within larger networks (Merrill et al., 1981).
So, it is quite difficult for users to identify subtle differences between concepts or apply their knowledge appropriately in new contexts.
5. Limited Capacity for Deep Learning
Semantic encoding is more concerned with recognizing existing links between concepts as opposed to forming entirely novel ones.
So, the information easily stagnates if users do not challenge themselves beyond simply repeating processes that have already been established.
Strategies to Improve Semantic Encoding
From incorporating multiple sources of information to breaking down topics into smaller components, there are several strategies to help improve users’ semantic encoding skills (Hofmann & Asmundson, 2017).
Here is a brief list of some techniques that can be used:
- Incorporating Multiple Sources of Information: By using multiple sources of information and combining different techniques, such as visual and verbal modes, users can create a more complete and diverse knowledge network, enabling them to recall information better when necessary.
- Drawing Connections Between Concepts: By creating connections between concepts, users can increase their understanding by connecting related items in a way that improves their ability to recall the material when needed.
- Practice Recalling Details from Memory: Repeatedly testing one’s ability to recall details from memory encourages neural pathways to be formed and reinforced, leading to improved storage capabilities for later use.
- Utilizing Mnemonic Devices: Using mnemonic devices or other forms of assistance can make it easier for users to remember details regarding specific topics, allowing them to reinforce their understanding of the material quicker than they otherwise would have been able to do so on their own.
- Breaking Down Topics into Smaller Components: Expressing complex topics in terms of smaller components enables users to focus on specific areas rather than trying to commit too much at once– thus aiding in memorization while also enabling learners to make associations with previously learned sections to create larger networks of interconnected knowledge.
Other Types of Encoding
Additional types of encoding include:
|Type of Encoding
|Involves using visual cues to store information and acoustic means using sound or language to store information.
|Involves using meaning or context to store information. We store the meaning along with the term, date, or concept to make it more memorable.
|Involves using auditory cues to store information. Includes linking sound characteristics such as pitch and frequency to the data being stored.
|Involves connecting new information to prior knowledge to remember it. Contrasted to rote learning where facts are remembered in isolation.
|Refers to using physical sensations and touch to store information.
|Involves organizing information into groups or categories.
Semantic encoding is a cognitive process that involves linking meanings or concepts to memories to improve understanding, recall, and problem-solving.
It enables individuals to associate unfamiliar words or facts with their existing knowledge base and build connections between seemingly unrelated pieces of data.
Although it is an important skill to possess, semantic encoding can be challenging due to its reliance on recognizing patterns and connections, which may not always be obvious.
Therefore, to help improve semantic encoding skills, people can utilize various strategies, from drawing connections between concepts to breaking down topics into smaller components.
With the right tools and techniques, people can improve their semantic encoding abilities and become better equipped to remember information for longer periods after successfully retaining them.
Bedford, D. (2020). Knowledge architectures. New York: Routledge.
Bedford, D., & Sanchez, T. W. (2021). Knowledge networks. New York: Emerald Group Publishing.
Cherry, K. E., Silva Brown, J., Jackson Walker, E., Smitherman, E. A., Boudreaux, E. O., Volaufova, J., & Michal Jazwinski, S. (2012). Semantic encoding enhances the pictorial superiority effect in the oldest-old. Aging, Neuropsychology, and Cognition, 19(1-2), 319–337. https://doi.org/10.1080/13825585.2011.619645
Hofmann, S. G., & Asmundson, G. J. G. (2017). The science of cognitive behavioral therapy. London: Academic Press.
Kirchhoff, B. A., Anderson, B. A., Barch, D. M., & Jacoby, L. L. (2011). Cognitive and neural effects of semantic encoding strategy training in older adults. Cerebral Cortex, 22(4), 788–799. https://doi.org/10.1093/cercor/bhr129
Kumar, A. A. (2020). Semantic memory: A review of methods, models, and current challenges. Psychonomic Bulletin & Review. https://doi.org/10.3758/s13423-020-01792-x
Merrill, E. C., Sperber, R. D., & McCauley, C. (1981). Differences in semantic encoding as a function of reading comprehension skill. Memory & Cognition, 9(6), 618–624. https://doi.org/10.3758/bf03202356
Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]