Semantic memory, organized knowledge networks, so let's use simple example to clarify this and explain
this.
So what is going on up in our minds when we see this type of situation?
So we have a kid sitting on a bench eating an ice cream together with a puppy.
Right.
First, the picture is such or the situation is such is encoded into iconic memory in this case, because
it's mostly a visual thing.
Of course, there are audio elements in this as well.
But for this example, let's focus on the iconic stuff.
So we see this situation.
We hold that for, as I said, roughly approximately half a second and we will start processing it directly
in the working memory, of course.
So when it comes into working memory, then we are trying to utilize the long term memory to try to
understand it semantically.
So we are retrieving kind of notes of information, notes and knowledge from the semantic memory.
So we are starting to getting a picture about this mental picture saying this is a kid and a puppy sitting
on a bench eating an ice cream together.
Right.
So we have them activated for different notes in this analysts network.
So we have activated a notion of a bench.
We know what a bench is.
We know what a kid is.
We know what an ice cream is.
We know when a puppies.
And I should say that I've been using a picture of a kid here just to exemplify that.
Of course, this knowledge is not just encoded as words in the long term memory.
They could be encoded in a number of different ways.
And I don't know if we actually know exactly how the information is encoded, but we retrieve the specific
notes.
But the notes such are not separated in semantic memory.
That's the whole point of the semantic memory.
So the notes are actually connected to larger network of other notes.
So we know, for example, that kids like ice cream.
White Puppis, which is an animal and puppy, is also a pet.
And animals, they actually like food and ice cream to some point is food.
Maybe a parent wouldn't say that, but a kid would probably say that.
So a puppy and a cat are both pets.
Pets actually could be a part of a family.
So that could be a family member.
A kid is also a family member, family member or containing them both pets, for example.
But usually people people can sit on benches.
And so the semantic memory contained this complete network of nodes and links between them.
And when we start working on them, we start retrieving the notes up and working memory.
Of course, we are also using the other long term memories.
So we are, for example, getting information from the episodic memory and the episodic memory.
Hold specific situations that I've been part of that I remember.
So I remember exactly maybe my dog, but I have myself when I was a kid or I remember being out more
or less being pulled by a larger dog, a specific situation.
So they are not semantic and on contextualised, they are actually contextualized.
So the semantic memory is kind of you don't know exactly why you know it, you just know it.
Whereas the episodic memory, you have specific knowledge about specific episodes from your life.
And then we have the procedural memory.
So, you know, things about, for example, how you approach the dog so you won't get angry.
Are you the famous ones or how you ride a bicycle or how you tie your shoelaces and so on.
So they are procedures or how you play the violin or play the piano.
You cannot probably see verbally how you do it.
You just know.
But the interesting thing then after this is that once you have started activating the notes that you
saw and retrieved from your sensory image, you start activating the links.
So let's say now that we have activated those four notes, the puppy, the ice cream, the kid and the
bench, what happens now is that a working memory automatically start retrieving.
The other notes there are linked to those notes.
And that is called the that the activation spreads activation spreading.
Another thing which is very interesting here.
So this is when we are trying to retrieve existing knowledge from the working from the long term memory
into the working memory.
But what happens when we are trying to encode knowledge into the long term memory from the working memory?
The interesting thing here is that knowledge is not automatically encoded just because we think of them
or have them in working memory.
We are.
He intentionally had to try to acquire this knowledge, and that could be done in different ways and
the date and the way we do it will actually affect how well it is organized and persistent and being
persistent in that way.
So let's take this very simple example again.
This was research done again by Gleitzman and Craig and Tubbing and Bransford in the 70s.
So they were giving a group a complex material to read or to learn.
And directly after they had done that and they said that they were done with it, basically they were
tested.
So they were proud.
They were giving trick questions that should reveal whether they actually know that material that we
just to read or tried to learn.
And then they were proved again sometimes later, a couple of weeks, a couple of months after this
occasion.
And the interesting thing that they saw was the ones that got the meaning from the beginning had much
better memory when they were probed again.
Hence, the ones that have integrated the knowledge from the very beginning, from the first time they
try to learn this, they record better.
That means that you if you have the working memory and you're working on your knowledge and your working
memory, you both retrieve information from semantic memory and you actually create new memory and semantic
memory.
You could do that in different ways.
So if you do it in a very shallow way, for example, route learning or mechanical rehearsal and just
laying there, five plus five is 10, five plus five is 10, popular as high as 10, five plus five,
10 and so on.
That's the kind of rote learning, mechanical learning.
You can do it a little bit more intermediate.
They're trying to pay attention to the concrete situations with the sounds and appearances.
Exactly how it sounds when you do this and when you when you after this specific five plus five is 10
and you could do it in a deep processing way.
That means that you pay attention to the semantics.
You pay attention to what addition is about.
Do you pay attention to arithmetic?
And then you can determine in all situations where you need that five plus five is actually ten.
So those kind of three very different ways of doing this.
And the interesting thing that they found out was that if we do it in a shallow way, we get poor persistence.
We cannot recall that knowledge in the long after a long time period have passed.
Whereas if we do it using deep processing and trying to link this intuitive semantic memory, then it
gets stuck.
So we're creating new links to new knowledge and creating new nodes and also new links.
Then we have the best persistence.
And this is very interesting that also this or that it isn't enough just to search around for meaning.
You have to find the meaning.
You have to actually link in the new knowledge into the existing memory and find the meaning of that
new knowledge in context of your existing knowledge.
Then it stucks.