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Not Knowing What We Know

Context: Invited talk presented during the Workshop on Memory and Skill, Duke University, Durham, NC, April 2016.

Title: Not knowing what we know: A call for a theory-neutral database for empirical results in psychology.

Abstract: Theorists have proposed various memory distinctions based on duration, capacity, awareness, or the nature of the retrieved content. We argue that debates about whether such systems are functionally and structurally distinct cannot be resolved by empirical evidence and that they have no scientific value, because they do not advance our understanding about the processes involved in various mnemonic tasks. Instead, we argue for a framework based on investigating task-dependent processes. In this framework all mnemonic acts recruit different combinations of processes depending on the nature of the task. However, one major impediment to task-dependent analysis of cognitive processes is the lack of organization of empirical results in psychology. We argue that there are fundamental problems with the way we store and organize empirical knowledge that prevent empirical integration and theoretical advancement. We call for a theory-neutral database for empirical results in psychology that systematically maps task parameters and behavioral outcomes for every published study in the field. Task parameters would include descriptors for the nature of the stimuli, the details of the paradigm and the procedure, etc., based on a systematic hierarchical nomenclature. This would not only allow us to integrate currently available empirical data, but can also drive empirical research by identifying gaps in our knowledge. It might further shift the focus away from hypothesis testing to precise parameter estimation. Such a database can be integrated with the publishing process, where researchers would have to submit task-parameters and summary data, as well as raw data with a standardized format. This would allow for automatic meta-analyses, and large-scale parameter estimation. This empirical integration could lead to advancements in theoretical understanding by stimulating cross-talk between paradigms, standardizing task descriptors, identifying gaps in our current knowledge and providing a systematic set of results for building and testing computational models and cognitive architectures.

Presentation slides: PDF

Take-home message:

  • Theoretical advancement is hindered by the lack of organization of empirical results
  • Too many published empirical studies to integrate verbally
  • Models and architectures are useful, but do not solve the problem
  • We need a theory-neutral database for empirical results that systematically maps task parameters and behavioral outcomes for every published study in the field
  • Structured syntax-based description of task parameters that allows easy comparison, literature reviews, data extraction and automated data-analysis
  • Usefulness
    • Organization and integration of currently available knowledge
    • Automatic meta-analysis and large-scale parameter estimation
    • Identifying gaps in our knowledge
    • Model testing and comparison

Relevant publications:

The Mortar of Cognition

Context: Talk presented as part of the Perception, Action and Learning (PAL) series, Carnegie Mellon University, Pittsburgh, PA, Feb, 2017

Title: The Mortar of Cognition: Semantic relations play a crucial role in memory, language comprehension and analogical reasoning

Abstract: If entity concepts are the building blocks of cognition, then semantic relations are the mortar that holds them together. Relations provide an organizational structure for semantic knowledge and they grant us the ability to abstract information beyond a single learning episode. Relations are thus the very “fuel and fire of thinking” and are arguably what makes human cognition so special. In this talk I will discuss my recent efforts to understand (a) how semantic relations are represented in long-term memory, (b) how they are retrieved by different instances and exemplars, (c) how they influence associative recognition memory, language comprehension and analogical reasoning. Specifically, I will present findings from a series of experiments on relational and structural priming, as well as experiments that uncovered a novel relational luring effect (RLE) in associative recognition memory. The RLE shows that people are more likely to falsely believe that a novel word pair such as AIRPLANE PILOT has been previously seen, because they have studied a relationally similar pair such as SHIP CAPTAIN. The RLE presents fundamental challenges for models of semantic and episodic memory, and it has important implications for unitization in associative recognition, analogical reasoning and retrieval, as well as constructive memory research.

Presentation slides: PDF

Take-home message:

  • Semantic relations are represented abstractly in LTM
  • The same representation can be retrieved unintentionally by different exemplars
  • This affects associative recognition, lexical processing, language comprehension, etc
  • These abstract representations give rise to relational priming, structural priming and relational luring effects
  • Sometimes this happens unintentionally, without awareness and without involving executive wokring memory resources
  • We can gain a better understanding of memory by studying how its properties relate to its purpose in cognition and behavior

Relevant publications:

  • Popov, V., Hristova, P., & Anders, R. (2017). The Relational Luring Effect: Retrieval of relational information during associative recognition. Journal of Experimental Psychology: General. Advance online publication PDF
  • Popov, V. & Hristova, P. (2015). Unintentional and efficient relational priming. Memory & Cognition, 46(6), 866-878. PDF
  • Popov, V. & Hristova, P. (2014). Automatic analogical reasoning underlies structural priming in comprehension of ambiguous sentences. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 1192-1197). Austin, TX: Cognitive Science Society. PDF

Why are stories so powerful? Insights from the psychology of memory

After the amazing response my last post received, I couldn’t help but think about why this idea, the idea that academic writers can learn something from fiction writers, resonated so strongly with so many people. While this definitely deserves a discussion in its own right (Skip to the main discussion below by clicking here), there were also quite a few people who seemed to fundamentally disagree with my thesis. More than one person thought that “No, we academics do not need to and should no tell stories”. Some common notions were that requiring academics to be storytellers would:

1) Make publishing even harder

2) Distort science

3) Make no sense, because reading enjoyment does not matter for impact

These are just some of the examples, although the notions themselves were shared by other people as well.

First, let’s get this out of the way – the most important aspect of any scientific publication should be clarity. The purpose of scientific writing is to inform, not to entertain. Without a clear, succinct and accurate presentation, no story is worth publishing in science.

That being said, whether we like it or not, we are telling stories in our scientific writing. An academic paper is a story about data, about what we have learned from it. The only question is whether we are conscious of that and whether we are telling the story well.

More engaging articles are likely to be read and shared with peers, and are ultimately more influential as a result. While it is hopeful to think that once published, a paper is “out there” and that it should affect and influence other researchers, the simple truth is that more often than not a scientific paper dies a quiet death. “Sleeping beuaty” papers that present fundamentally important ideas, but lay dormant for decades before being rediscovered, is a more common scenario then people imagine. Finally, as this reddit user noted in response to my comment, most papers have very little impact (though he probably meant “the mode”, rather than the “median”, which is also not 0, but it is pretty close):

It is not outrageously unconceivable to think that papers centered around a good story are likely to be more influential. In fact, in climate science articles that have more narrative abstracts are more influential and are cited more often.

Why would having a good story make an article more impactful?

As someone who studies memory, I think that the key lies in the constructive nature of human memory (and despite the insistance of some of my friends that I am a robot, scientists are human too).

Most people imagine that memory works like a video camera – it dutifully records everything that you read, see and experience. We believe that when we close our eyes and mentally replay the things that happened to us, we are reexperiencing the past as it truly happened. Sure, our memories often fail us and we are prone to forget important details even about things we care about, but still, the prevailing notion is that, for the most part, what we do remember is an accurate portrayal of the world.

Unfortunately, psychological scientists have been busting that notion for years. Rather than being a playback of our true experiences, remembering is more akin to solving a puzzle without the picture on the box to guide us. It consists of innumerable pieces, patches of experiences, that get melted together and then get reconstructed, recombined, and repackaged on the spot as we need them. Memory researchers have observed these phenomena for years, both in the lab and in the real world, and they are often the topic of public discussion. In the words of Sir Frederick Bartlett, one of the pioneers of the field:

Remembering is not a completely independent function, entirely distinct from perceiving, imaging, or even from constructive thinking, but it has intimate relations with them all… One’s memory of an event reflects a blend of information contained in specific traces encoded at the time it occurred, plus inferences based on knowledge, expectations, beliefs, and attitudes derived from other sources.Remembering: A Study in Experimental and Social Psychology

What does all of this have to do with how stories affect scientific impact? Turns out, story structure is one of the most dominant organizational principles of our memories. Stories are the very scaffolding around which our memories are built. Stories are the backbone and the flesh of our experiences.

One of Bartlett’s own studies is an execellent example of this principle in practice. How well do you think you would be able to remember a Native American folktale? Bartlett asked British participants to remember a story called “The War of the Ghosts”, a Native American folktale with an unusual for western fiction structure and narrative. Over the course of days, month and years, Bartlett asked them to repeatedly recall the story. The result? Every repetition made the story more and more distorted, but the distortion wasn’t random. People retold the tale in a way that was more and more consistent with their expectations and previous knowledge. Without realizing, they omitted details that did not seem important and distorted the structure to fit western expectations of narrative.

Aside from the ability to frame our memories, recognizing a familiar narrative helps us remember seemingly disjointed information. One of my favorite examples is a classic study done by Bransford and Johnson in 1972. Before I describe it, read the following passage and try to think, first, whether you have any idea what it is about, and second, whether you will be likely to remember it:

The procedure is actually quite simple. First you arrange things into different groups. Of course, one pile may be sufficient depending on how much there is to do. If you have to go somewhere else due to lack of facilities that is the next step, otherwise you are pretty well set. It is important not to overdo things. That is, it is better to do too few things at once than too many. In the short run this may not seem important bu complications can easily arise. A mistake can be expensive as well. At first the whole procedure will seem complicated. Soon, however, it will become just another facet of life. It is difficult to foresee any end to the necessity for this task in the immediate future, but then one never can tell, After the procedure is completed one arranges the materials into different groups again. Then they can be put into their appropriate places. Eventually they will be used once more and the whole cycle will then have to be repeated. However, that is part of life.

Are you completely lost? What if I told you that it describes the process of doing laundry and I ask you to read it again? How about now? Bransford and Johnson asked three groups of people to listen to the passage above, and to remember it for later. The first group was not told anything about it, and were completely lost and their memory of the passage was very poor. Another group was told beforehand, that the passage they will hear concerns laundry. They were able to remember twice as many details from the passage as the control group did! Even though both were given exactly the same informaiton in the passage itself, just knowing that it relates to something familiar such as laundry, allowed one group to bring that schema into mind, and use it to help them comprehend and remember what they were listening to.

The ability of familiar stories, schemas and narratives to distort and organize our memories, is not only a fascinating aspect of the workings of the human mind. It also likely plays a role in comprehending and remembering the articles we read, be they scientific or not. I would argue, that articles written with this in mind, with the goal of presenting a coherent story that fits into a narrative structure familiar to many people, would be easier to remember. And, after all, you cannot cite and be influenced by something you do not remember.

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All scientists should be storytellers

No one likes reading academic papers. Deep down even scientists hate reading academic prose. Aside from the rare gems now and again, academic prose is dry, terse and quite convoluted. This state of affairs is not surprising to anyone – it is so widespread that we have grown to expect bad prose and stumbling upon good writing in science feels like a slap in the face. It energizes and wakes us up. Whatever you may think of academics like Noam Chomsky, Jerry Fodor, Steven Pinker or Douglas Hofstadter, there is no denying that they have the uncanny ability to pierce with their words. Just look at some of the examples below and tell me they don’t intrigue the hell out of you.

Fodor, for example, entices the readers of his Precis to the Modularity of Mind (1985) with the following opening paragraph:

Everybody knows that something is wrong. But it is uniquely the achievement of contemporary philosophy – indeed, it is uniquely the achievement of contemporary analytical philosophy – to have figured out just what it is. What is wrong is that not enough distinctions are being made. If only we made all the distinctions that there are, then we should all be as happy as kings. (Kings are notoriously very happy).

Similarly, you can’t help but wonder what Hofstadter’s arguments will be when he makes a statement such as:

Analogy is the very blue of the sky of cognition. It is the talent so fundamental that it fuels our minds. [..] Is it irrational, subjective and concrete? Yes indeed, but it is also the underpinning of rationality, objectivity and abstraction. Analogy is not a rare luxury of thought or an exotic, remote corner of cognition. Analogy is the entire transport system of thought, including motorways, roads and trails; it pervades thinking, from throwaway remarks to deep scientific and artistic insights. All along the spectrum, analogy lets us see the new in terms of the familiar. It guides us in learning new concepts, solving mathematical problems, dealing with interpersonal conflicts and making political decisions.
– The Forgotten Fuel of our Minds, 2013

Why are examples such as these so few and far in between? Why are academics, who live and breathe writing, so notoriously bad at it?

In his recent essay, Why Academics Stink at Writing, Steven Pinker suggests that it has nothing to do with a conscious desire to be obtuse in order to put meaningless content behind a veil of jargon. Or with the fact that academic journals and reviewers expect papers to be written in what he calls academese. Nor does academese spring from the fundamental abstractness and complexity of the subjects we study. At what then, can we point our fingers to, as the agent that poisons academic writing?

The trap of self-conscious writing.

One problem with academics is that when it comes to writing, they are like a ship-wreck survivor standing on two planks in the water that are moving in different directions. Academics are torn between satisfying two different purposes when they write – one is to inform their readers of novel data or ideas. At the same time, however, academics agonize over how they will be perceived in the eyes of their colleagues. Will they be thought of as careful, knowledgeable, creative and analytic? Or will they be dismissed as hacks who go beyond the data in making conclusions, who disregard their colleagues’ work, however remotely related, as people who are way too confident for their own good?

Pinker sees this trap of self-conscious writing as one of the fundamental causes of bad writing. It leads to metadiscourse (e.g. “This article is organized as follows”), to using vacuous qualifying phrases (e.g. almost, quite, in part), nominalizations where verbs would suffice (e.g. “The affirmation of the current hypothesis by the data…”), etc.

But even if one solves all of these local problems, academic prose might become easier to read, but wouldn’t necessarily become any more engaging. More often than not, academic papers lack spice, they lack a compelling story.

We should all be storytellers.

Why do people enjoy reading fiction? I’m sure there will be as many different answers as there are readers and writers, but I believe that the cornerstone of all is good storytelling. Even the most fascinating characters or the most beautiful prose can be empty shells when they are built around bad, misdirected, and unsatisfying stories. Lolita, for all its beauty and charm, is one unbearably boring book (don’t crucify me, please!).

Most articles in science lack a compelling story. Data by itself is meaningless, and yet, most academics, including me, often treat it as self-explanatory, as something that should enlighten readers by its sheer awesomeness. But we don’t care about data. We don’t care about numbers. Numbers are just tools to describe the world.

Scientist care, or at least they should, about what those numbers can tell us about the fundamental properties of reality, the reality of the natural world, of our minds, and of the culture our species has produced. And this is fundamentally a storytelling issue – what does the data tell us about that specific aspect of the world? How did it come to be the way it is, how does it work, and how can we fix it, if it is broken? How do I, as a scientist, share that story with my peers and with the public?

Jeffrey McDonnell, in a paper published in Nature a few days ago, deals with exactly this issue. While above I have treated it as a problem for readability, he aptly recognizes that the lack of storytelling ability is what fundamentally makes writing a challenge for many academics. He uses a storytelling approach to help him and his students write better, clearer and more engaging academic papers:

Each of my group’s papers now starts with a storyboard session at a whiteboard. I pretend to be a big-time Hollywood producer and ask the Ph.D. student or postdoc to play the role of would-be movie director pitching a new movie to me. Their pitch must answer three questions: What is the status quo? What is wrong with the status quo? How does this new paper go beyond the status quo?

This approach helps frame the story and place key figures and technical findings in context. Balancing each of the status quo elements is a great way to set up the introduction—often the toughest section for early-career scientists to write—and to lead the reader to the research questions or hypotheses.

I myself, realized that this is the way to go about three years ago, when I was struggling to write one of my first papers. I believed I had something important to say, but I found that I have no idea how to stay it – I became paralyzed every time I stared at the cursor blinking on the empty page.

My breakthrough occurred, similarly to McDonnell’s, when I realized I should start asking those precise questions – what is my data telling me? Not what have I found, but what does it meanWhat is the main conflict that my data resolves? How do I build up that conflict in a way that makes the reader ache for the resolution?

I picked up those techniques not from anyone I know, or from a course on academic writing, but from a book on writing fiction, where story is king and reigns supreme. I am far from being any good at it, and I often fall back on bad habits, but I’m constantly trying to improve.

The problem is, in STEM and in the social sciences, we rarely teach writing in undergrad or grad school. Rather, it is expected that writing is something students will pick up on their own as they go, as they read more and more. Given that the papers they read suffer from all the vices of academese, this expectation is unrealistic. We need to teach ourselves, our peers, and our students, how to write better stories, because stories do not tell themselves, and data is impotent when buried in bad writing.

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Getting into grad school the hard way

So, you have decided that you hate money and you embrace the thought of a 6o+ hour work week? Likely you are thinking about going to grad school, then. Does the process seems daunting?

It certainly is, and it is notoriously anxiety provoking. However, just remember that every tenured professor or current grad student (including me) was once in your shoes. The best way to deal with that anxiety is to get intimately familiar with the actual evaluation process and to be prepared. While the criteria might definitely vary across fields, here are some of the things I learned during my application process.

Start early

Above all, start planning early. If you are hopping to begin this fall, start planning now. If you are hopping to start next fall, start planning now – the sooner the better. Preparing all documents and taking all tests takes a lot of time. Spare yourself some of the anxiety by starting to prepare well in advance of the actual deadlines.

Understand the application process

The application processes goes roughly like this. Once you’ve chosen where to apply to, you have to submit an application with current academic transcripts, several letters of recommendation, a personal statement (statement of purpose), cv/resume, GRE scores. All schools have different deadlines, but most of those are in December/January in the year before you are hoping to begin your program.

What matters?

The factors that are weighted most importantly when they review your application: 1) Personal statement (to determine if you have the motivation, experience and research fit with the program), 2) Letters of recommendation (these should show that people who’ve supervised you think you will do great in a graduate program. They should speak about your qualities as a future academic/researcher, and give concrete examples about how you’ve excelled), 3) research experience

Research experience trumps all else.

Research experience trumps all else

Get first-hand research experience. The more the better. This is arguably the most important factor they will judge you on. Find a lab at your current university where they are studying phenomena that are of interest to you. Read a few papers by the person who leads the lab, think deeply about them and contact the PI of the lab. Say that you want to participate as a research assistant, why this area is interesting to you and ask to meet them to discuss it.

Labs are always looking for motivated research assistants. You should have at least an year of research experience in order to have any real chance of being admitted to a good program. Programs are looking to see that you know how to do research so that you won’t decide that you don’t like it and just give up one year through the program.

If you have the opportunity to choose among different labs, choose the one that studies things most closely related to what you want to study in grad school. In the beginning you would be doing menial work, but if you read and think enough, you can make creative suggestions and be given more responsibilities and do some creative work while you are there. If you manage to do that, the person who leads the lab can give you the strongest recommendation letter you will be able to get.

All research experience is not equal. Potential grad school mentors want to see that you have the capabilities to be a creative and productive member of their lab, who can contribute intellectually to their research. While being a research assistant, read papers vociferously and try to come up with alternative explanations, further testable hypothesis, etc, and convince your mentor that they are worth pursuing. In short – behave as a graduate student even before you become one.


While doing research, try get a publication, some conference presentations, the likes. It easier said than done, but this will show that you have taken an active part in the research process, no only assisted with menial tasks. While many people get accepted and possibly graduate without any publications, nothing speaks to your research skills and interests more than that.

Pick the schools you apply to carefully

Research your potential grad schools very carefully. Find a list of the strongest programs, and read the faculty profiles for each. Bring down the list to 5-15 school in which there are people working on the topics that interest you. Research fit with faculty is often even more important that qualifications (especially when there are many qualified candidates). Don’t apply to the best program just because it is the best, if you are not interested in what they are working on. You will be unhappy there and there is little chance to be admitted if you don’t show interest and knowledge of a specific area of research close to some of the faculty’s interests.

Contact potential advisers in advance

It is important to contact in advance each faculty member that you’ve chosen to potentially apply to. Introduce yourself and your interest briefly, ask whether they would be accepting new students in their lab next year. If they don’t, don’t bother applying. If they do, you can try to start a discussion about their work (you should have read it very carefully and have something meaningful to comment/ask. Do go bother them just for the sake of it). Some people disagree about this point, but two of the schools I applied to turned me down, because the professors I was interested in were not taking any new students that year. I could have saved myself the trouble and the money if only I had asked them beforehand.

Potential advisers want to read about your research ideas, not your cutesie childhood inspiration story.

Your statement of purpose should be about your research ideas

Start writing your statement of purpose early. Tailor it for each school, but most importantly tailor it to the faculty member you’ve chosen at each school. Briefly discuss you profile, experience and motivation, but spend the majority of the text on your ideas and interests and how they relate to what they are working on.

Edit it multiple times. Give it to other people to read, ask for their honest comments. Give it to faculty at your current university with whom you have a relationship and ask them for suggestions.

Your GRE score doesn’t have to be perfect – is just a cut-off

Take the GRE in the summer before you apply. The GRE is usually taken as an initial cut-off – it is important that you score well, but above the 80th percentile it doesn’t really matter where you score.

Be prepared to wait

It is a long processes and it costs many people a lot of stress and anxiety. Remember, that life outside of academia exists and that your worth as a human being is not dependent on getting into a grad program. Any faculty member will tell you that there is a lot of noise in the system, and that many good people don’t make it on their first try.

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A brain trying to understand itself

Imagine if we could bridge the gap between minds and computers. Imagine a world where the whole of humanity’s knowledge will no longer be available just at our fingertips, but instead it will be directly available to us in the same intimate and immediate way as we access information stored in our own brains. This will take away the need to spend countless precious years training every young mind to imbibe an ever expanding wealth of facts that quickly go obsolete. Instead, it will bring the focus during those critical years on reasoning, critical thinking, creativity and the acquisition of skills and abilities.

But in order to bring this future to fruition we first need to understand the specific neural code with which semantic information is stored in the brain and the precise mechanisms through which it is retrieved on demand. We need to understand how these two aspects of memory work, so that later on we can engineer a system capable of taking advantage of those mechanisms. Whether I will see this future realized during my lifetime is anybody’s best guess, but I have decided to dedicate my work and efforts to help make it a reality.

I am currently a PhD student at Carnegie Mellon University and the Center for the Neural Basis of Cognition. I work with Prof. Lynne Reder in exploring the neural and mental mechanisms of memory formation, knowledge representation and retrieval.

And because who doesn’t need another reason to procrastinate, let’s give this blogging thing a go.

Neuromusings will be an opinionated view of cognitive science, research and academia. I plan to post reviews of recent and classical articles in the field of cognitive psychology and cognitive neuroscience, as well as general thoughts and ideas about cognition and philosophy of science, open data, data analysis, research practices, academic workflows, etc.

Hopefully, I will find a distinct voice with which to express my ideas.

What other academic/neuroscientific blogs do you follow? What topics interest you? What are your thoughts on the technological future I have outlined above? Let me know in the comments.

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Imagine if we could bridge the gap between minds and computers. Imagine a world where the whole of humanity’s knowledge will no longer be available just at our fingertips, but instead it will be directly available to us in the same intimate and immediate way as we access information stored in our own brains. This will take away the need to spend countless precious years training every young mind to imbibe an ever expanding wealth of facts that quickly go obsolete. Instead, it will bring the focus during those critical years on reasoning, critical thinking, creativity and the acquisition of skills and abilities.

But in order to bring this future to fruition we first need to understand the specific neural code with which semantic information is stored in the brain and the precise mechanisms through which it is retrieved on demand so that later on we can engineer a system that can take advantage of those mechanisms. Whether I will see this future realized during my lifetime is anybody’s best guess, but I have decided to dedicate my work and efforts to help make it a reality.

I am currently a PhD student at Carnegie Mellon University and the Center for the Neural Basis of Cognition. I work with Prof. Lynne Reder in exploring the neural and mental mechanisms of memory formation, knowledge representation and retrieval.

I publish under my full name, Vencislav Popov, but Ven is easier to say and remember, and as people have noted, “Oh, Ven, like a venn diagram!”

Check out my blog, Neuromusings (quite sparse for now).


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