Just a Reflection of a Reflection: Hierarchical Retrieval

When I started this blog, my goal was to publish one post a week for a full year. Since I am officially halfway to my goal, I thought this would be a good time to pause and reflect on what we've learned so far. If you've been following along, almost all of my posts thus far have been about two things: how knowledge is acquired and how it gets represented. These two themes were further broken down into smaller pieces so that they could be understood in isolation. In so doing, however, it is likely that we miss the forest for the trees. So let's take a step back and see where we are and how far we've come. 

To motivate this reflection, I would like to introduce a new concept. As we discussed previously, working memory only holds a limited amount of information. We also learned a couple of different strategies to expand our working-memory capacity. One of them was called chunking. The concept for today's reflection is called Hierarchical Retrieval, and it is a way to chunk a lot of information by exploiting the semantic connections between the ideas.

Hierarchical Retrieval Applied to Cognitive Science

As stated previously, the mind is a big fan of both meaning and order. Things that go together tend to get represented together. For example, it is very natural to link together the concept of "cheese" and "mouse" because we believe that mice like to eat cheese. In other words, these two concepts are linked because of the meaning that they have, and we can generate a causal connection that links the two. However, the concept "cheese" and "clown fish" is a less natural connection. It would take a pretty active imagination to link these two concepts. If we are able to tell a story about how two concepts are linked, then it is more likely that we will be able to remember them. Thus, retrieval can be based on meaning (i.e., semantics).

The mind also craves order. Concepts are connected to each other, as we saw in the associative and semantic network representations of knowledge. This network representation was useful because it helped explain cognitive phenomena like priming and how we generate creative ideas. When laid out as a network, there didn't seem to be any discernible order. However, we can explicitly reorganize these network structures so that they are hierarchical. Here's an example that might look familiar.

There are eleven concepts represented in the hierarchical diagram. That means that we are beyond the typical working memory capacity. However, I think it would be trivial for you to memorize this list because you can chunk together the items that are nested under the "parent" concepts (i.e., the concept "Birds" is the parent concept to "Finches" and "Sparrows").

The STEM Connection

Textbooks use the power of a hierarchy all the time. In fact, you might think about the table of contents as a way to hierarchically organize the information in the book. In addition, it might help students to create their own hierarchical diagram of the information they have covered in class because it forces the student to think about the connections between the ideas. Also, it might be helpful, when introducing a new topic, to lay out all of the information so the student can see where the class currently is and where it is going. In educational psychology, they call this an advanced organizer.

If we were to apply that same process to this blog, it might look something like this:

My goal is to keep expanding this diagram until all of the various concepts and topics have been discussed. For example, we talked about the limitations of the attentional system in that we can only process one stream of information at a time. The concept "Attention" does not have a node in the network diagram above. Thus, this representation is incomplete. Moreover, it fails to capture the inner-connections between various concepts. For example, while talking about solving problems, it became evident that we needed to consider the representation that was used during problem solving, and the different effects on working-memory load. Thus, a hierarchical retrieval structure, such as this, fails to capture all of the information. But it does give us a nice, bird's eye view of all the topics we've covered so far. It also points to some of the concepts that are missing, which I can't wait to discuss in future blog posts! 


Share and Enjoy! 

Dr. Bob

For More Information

[1] I was introduced to the lovely idea of a hierarchical retrieval structure in the following paper: Bower, G. H., Clark, M. C., Lesgold, A. M., & Winzenz, D. (1969). Hierarchical retrieval schemes in recall of categorized word lists. Journal of Verbal Learning and Verbal Behavior, 8(3), 323-343.