Teaching Collective Memory

Aimed at integrating cutting-edge psychological science into the classroom, columns about teaching Current Directions in Psychological Science offer advice and how-to guidance about teaching a particular area of research or topic in psychological science that has been the focus of an article in the APS journal Current Directions in Psychological Science.

Rajaram, S., Greeley, G. D., & Jin, J. (2026). Collective memory is more than what we remember: Quantifying interconnections. Current Directions in Psychological Science.

Invite your students to spend a few minutes recalling their last exam in your class. They can write down specific questions or topics covered, fluctuations in their emotional state (e.g., dread, relief), and any sensory experiences they remember (e.g., noises). Even though they’ll be answering individually, many students will recall the same things: the most challenging questions on the test, feelings of stress and relief, and a couple of distractions. These shared memories are one example of collective memory: a remembered history that a group of people hold in common (Halbwachs, 1925/1992).

Students tend to picture collective memory as a kind of group photograph: a fixed image that many people happen to hold in common. What they usually don’t picture is that the photograph has an internal structure, that is, how all the specific pieces hang together. APS Fellow Suparna Rajaram and colleagues Garrett Greeley and Jingwen Jin make the case that this internal structure, or memory organization, can also be shared across people.

Typically, research on collective memory focuses on the overlap in content, such as whether two people remember the same details (Roediger, 2021). Far less attention has been given to overlap in organization: Do two people connect those details in memory to each other in the same way, and do they recall those details in the same sequence? Rajaram and colleagues argue that this organizational overlap is a distinct and underappreciated dimension of collective memory.

Consider the analogy of building a LEGO set. You open the LEGO box and use the instructions to successfully construct a replica of the Millennium Falcon. Your friend receives the identical set, but without a box or instructions. Though your friend has seen the Star Wars movies and has the exact same bricks as you, their construction looks quite different. To state the obvious, it’s not just the content that matters, but also how those contents are connected and arranged.

Now imagine what would happen if you and your friend could collaborate on building a new LEGO Millenium Falcon, based entirely on your memories of your individual builds. You would see a shift toward shared arrangement, incorporating past interconnections from each individual. What is particularly fascinating is that these new, shared arrangements “stick”—if you later built yet another LEGO Millenium Falcon, it would look more like the one you built collaboratively than the one you built individually. That, at least, is what occurs in laboratory studies: When people collaboratively recall the past, that information gets reorganized together (Rajaram et al., 2026).

To investigate how collaborative memories are organized and reorganized, psychological scientists have drawn on computational approaches. One of these approaches, which has historically been used in brain-imaging research, is representational similarity analysis. The idea is to have participants learn the same information—such as a list of 12 words (apple, banana, chair, etc.)—and then examine the sequence by which they recall those words. Knowing the recall sequence allows researchers to compute the “distance” between each item in the list. For example, if “apple” was recalled first and “banana” second, then their distance would be 1; if “chair” wasn’t recalled until position 12, then its distance from “apple” would be 11. This calculation produces a full matrix for each person, which can be used to determine whether different people’s memories are similarly organized.

Using this method, Jin et al. (2026) studied memory organization in individual (solo) recall attempts as well as collaborative group recall attempts in which individuals in the group contributed responses at any point in a free-flowing manner. They found that after a collaborative group recall, individuals’ subsequent solo recalls drifted closer to the group’s pattern, suggesting that collaborative recall reshapes the internal structure of what people remember.

But what about collective semantic memories, which can form the body of knowledge for societies (Roediger, 2021)? To study the organization of collective semantic memory, Greeley et al. (2025) used another computational approach called network analysis. Participants were asked to name as many U.S. cities as they could. Network analysis treats each remembered city as a node and draws a connection between cities that are recalled back-to-back. With just one participant, this analysis produces a simple chain; with a larger sample, it produces a broad network of cities with some connections being visibly thicker than others. Greeley et al. identified not only that most people recall New York City and Los Angeles, but that these cities are usually recalled together, despite being on opposite sides of the country. Other cities were commonly grouped geographically—when a person recalled Dallas, they often subsequently recalled Houston and Austin.

These computational approaches could also be used to address how shared global experiences affect the organization of semantic memories. Before 2026, network analysis would not suggest that people’s memories collectively linked Mexico City to Atlanta or Vancouver. However, after millions of people watched the exciting soccer matches in these three cities on the same days during the 2026 World Cup, these three cities may be intertwined in our collective memories despite differing in geography, country, and culture.

Computational approaches to collective memory are unique to psychological science research, but they have bearing on other fields including sociology, business, and political science. Rajaram et al. (2026) point out that sports fans, political parties, and other groups can share substantial memory content while still telling meaningfully different stories, because of the way in which those memories are organized. This fundamental knowledge helps to explain how survey respondents in the United States, United Kingdom, and Russia each estimated that their own country was responsible for more than half of the Allied effort in ending World War II (Roediger & Zerr, 2022). These citizens shared the same broad contents of memory, but with different organization.

So, let’s return to the initial example. Students will likely agree on the “snapshot”—the stressful anticipation before starting the test, the hardest questions, and a distracting phone ring. But listen closely to how their recollections are sequenced. Do they recall test items in the same order, even if that order does not fit the blueprint of your test? And, if the students first discuss the test among themselves, does that trigger a change in how they later individually recall the test? If so, then that’s collective memory in action, right there in your classroom.

Researcher Teacher-Feature

Almost all university researchers are teachers, too. That’s why we’ve started a new feature of Teaching with Current Directions. We invite the scientists featured in our column to answer questions about their own teaching. We hope that this new feature inspires you and sparks your curiosity! The interviews may be summarized and edited for clarity.

Dr. Rajaram, what courses do you teach the most? At SUNY Stony Brook, I teach graduate Human Memory (for doctoral and master’s students) and, for undergraduates, Human Memory and Research Methods plus lab.

What’s one of your favorite courses, topics, or skills to teach? My favorite is to teach students how to think as a scientist and a scholar; more specifically, how to think about the content of the course and not just what to memorize. I also teach them why it is important to learn about the content and the topics that are not related to your own research, and how this process expands your ability to think about your own research.

What aspect of teaching do you like the best? I most enjoy interacting with students and engaging with their questions. I also like learning from what worked and what did not work in my lectures, and why. This helps me recalibrate my lectures, a process I enjoy and find to facilitate deeper learning for me.

Do you discuss your own research findings in class? If so, what do you want your students to know about it? On occasion. I do this when my own research is relevant to the course content, for example, in the graduate-level memory course but not so much in the undergraduate research methods course. I want students to know how the assigned research studies by my lab group—and the research topic itself—are relevant to the course content, and why these studies are important to learn as a part of the course content.

What class or lesson was particularly memorable from your own undergraduate years? The lessons where I learned that experiments open the path to investigating causal relationships between variables that interest us.

What comes to mind when you reflect on the intersection of teaching and research? For me, teaching is one of the best ways to learn. This sometimes turns out to be true even for the material that I think I already know well. This is because teaching opens for me fresh perspectives, new ideas, and greater clarity when I explain concepts, theories, and experiments and listen to the students’ reactions and ideas. These are important moments that bring clarity and novelty in thinking about research ideas and questions just as much for the students as for me. Sometimes, students in my class become so engaged in the ideas and experiments I share with them that they join research labs to pursue hands-on research experience. To me, research and teaching inform each other.

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