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author: niplav, created: 2024-02-28, modified: 2024-03-31, language: english, status: in progress, importance: 6, confidence: certain

I examine the literature on transfer learning in humans.

Transfer Learning in Humans

When learning, one would like to progress faster, and learn things faster. So it makes sense to search for interventions that speed up learning (effective learning techniques), enable using knowledge and knowledge patterns from one learned domain in a new domain if appropriate (transfer learning), and make it easier to find further learning-accelerating techniques (meta-learning).

Summary of the Results

If you're already using spaced repetition a bunch,

  1. If you want to learn faster
    1. Do spaced repetition when possible
      1. In general, revisit basics of a field while you're learning
    2. Spend a lot of time on practice problems
    3. Explain why you're doing what you're doing, while you're doing it
    4. If errors are cheap and obvious, make and seek out errors during learning/training
  2. If you want to solve problems
    1. Try to get feedback on both the process and the outcomes of what you're doing
      1. Explicitly analyse errors after you've made them1
    2. If there are already experts at the problem you're trying to solve, interview them in a systematic fashion to extract their tacit knowledge
      1. With enough institutional support this can be turned into a training program
    3. If there are no experts in the domain where you're trying to solve a problem:
      1. Search for related domains and extract existing tacit knowledge there, or learn those domains—the closer the better
      2. Apply the Pólya method

If you think that these recommendations are kind of unsatisfying, I agree with you.

What I Am Looking For

Given a broad set of skills $S$, I was looking for an intervention/a set of interventions $I$ which has the following properties:

  1. After applying $I$, an average adult can now learn skills from $S$ is on average much faster counterfactually to not having applied $I$
  2. Applying $I$ and learning $S$ is easier than just learning all skills $S$
  3. $S$ is large (or actually encompasses all skills humans have)
  4. Optional: $I$ is relatively easy to apply, that is it doesn't need a lot of institutional setup
  5. Optional: $I$ can be applied to itself, and to find better interventions $I'$ that have the same properties as $I$

The question about transfer learning in humans isn't clearly differentiated from the research into effective learning techniques. Transfer learning and meta-learning are more focused on crossing the theory-practice gap and making progress in domains where we don't yet have detailed knowledge.

Therefore, I tried to find more information from well-performing institutions such as the military and large corporations, de-emphasizing research done in universities and schools (I found this difficult because those places tend to have more incentive to publish their techniques, and also strive to quantify their benefits).

Candidate Interventions

Straightforward Stuff

I found several studies from psychology, especially educational psychology.

Dunlosky et al. 2017 reviewed the evidence on ten proposed effective learning techniques, and singled out two interventions as having high utility and three interventions as having moderate utility:

  1. High utility:
    1. Practice testing: Testing oneself on the target domain in a low-stakes context, ideally repeatedly. Think spaced repetition with flashcards, or preparing for exams by doing exams from previous years. They mention that practice testing generalizes across formats (e.g. from simple recall to short answer inference tests). Can generate far transfer.
      1. p. 30: "practice testing a subset of information influences memory for related but untested information"
    2. Distributed practice: Practice that happens spread out over a longer amount of time, instead of cramming. This gain is also captured via spaced repetition. They do not mention any transfer benefits here.
  2. Moderate utility:
    1. Elaborative interrogation and Self-explanation2: Generating and saying3 an explanation for why an explicitly stated fact or concept is true. This most helps learners who already know a lot about the target domain, and works best if it is done during the learning process.
    2. Interleaved practice: When learning, repeat basic material while learning more advanced material. The advantages over distributed practice testing seems moderate, but (p. 38): "interleaved practice helped students to discriminate between various kinds of problems and to learn the appropriate formula to apply for each one". Works better on mathematics4.

"Far Transfer"

Summary: Far transfer occurs if one puts in a lot of effort, e.g. after doing semester- or year-long courses on decision-making and such. The effect sizes on general abilities tests are medium (d≈0.3).

Far transfer is:

improved performance at problems that are similar to but also substantially different from ones experienced during training (e.g., fault diagnosis in process control to fault diagnosis in telecommunication networks).

—Hoffman et al., “Accelerated Expertise”, 2014

The relevant papers are:

Error Management Theory

Summary: Making errors during training, if it is obvious an error has occurred, and errors are affordable, transfers the learned knowledge pretty well (d=0.8).

Error Management Training (EMT) is a type of training in which making errors during exploration while learning is actively encouraged. Trainers encourage learners during learning to make errors and reflect on those errors while learning, but don't give much guidance beyond that.

Keith & Frese 2008 perform a meta-analysis analysing studies training participants to use software tools or learn programming languages (n=2183), comparing EMT to training that encourages error-avoidance, and find that EMT has a medium-sized advantage over error-avoiding training methods (d=0.44).

EMT shows larger effect sizes over error-avoiding methods with more demanding transfer: d=0.56 for performance after training, and d=0.8 for transfer that requires modifying learned procedures to fit news contexts (adaptive transfer). However, Keith & Frese also provide evidence that this advantage only occurs if there is clear feedback on whether an error has occurred or not.

One is reminded of Umeshisms: If you never fail, you're underperforming. (Also, you're not going to be able to use it.)

When I tried tutoring someone in programming for fun, I tried to give the person assignments that they would only be able to solve 50% of the time. I don't know whether this is optimal, but mumble mumble entropy mumble dense reward mumble.

Pólya Method

Summary: Evidence is not great, but one paper looks suspiciously good. Worth investigating, especially since it's often recommended by research mathematicians.

Another interesting-seeming strand of research were tests of the Pólya method. The Pólya method is a four-step problem-solving method, with the four steps being

  1. Understand the problem
  2. Devise a plan
    1. The book "How to Solve It" also has a list of problem solving strategies
  3. Carry out the plan
  4. Look back

This is a variant of the OODA loop, with the difference that a lessened time pressure allows forming a whole plan (not just a decision) and for reflection after carrying out the plan.

For some weird reason, the only scientists who have investigated the Pólya method experimentally are Indonesian. I have no idea why.

The relevant papers all test on learning basic mathematical problem solving skills in plane geometry and fractions:

  1. Nasir & Syartina 2021: n=32 Indonesian high-school students, non-RCT, only observational. Effect size d=0.71, but that's not super impressive given it's not an RCT.
  2. Widiana et al. 2018: n=138 elementary school children, RCT. I'm not entirely sure about this, but based on their Table 1 and this calculator I get d=2.4, which I find really hard to believe. I think I'm making a mistake$_{60\%}$ or the paper is fraudulent$_{40\%}$.
  3. Hayati et al. 2022: n=40 Indonesian high-school children. This paper is so confusingly written I can't extract any meaning from it.

Accelerated Expertise

Summary: With a lot of institutional support, one can extract knowledge from experts and use it to create better training programs. This requires a large institution to be worth it.

Accelerated Expertise (Hoffman et al., 2014) was motivated by getting military recruits up to speed quickly before moving them to deployment. It focuses on the case in which there are already experts for a given domain, and one aims to move the skills from domain experts into the mind of new recruits as quickly as possible. Chin 2024 summarizes the goals of the research project that lead to the book as attempting to speed up the time from being a beginner at a specific task or set of tasks to being proficient at that task (hence the name "Accelerated Expertise").

They are skeptical that any training can make trainees dramatically better at the domain than experts with a lot of training.

For this, Hoffman et al. have developed a series of multiple steps for creating training programs for new recruits.

  1. Identify domain experts
  2. Use Cognitive Task Analysis to extract expert knowledge
  3. Build a case library of difficult cases
  4. Turn case library into a set of training simulations
  5. Optional: Include introspection & reflection in the program
  6. Optional: Teach abstract/generalized principles
  7. Test the program

The book contains a literature review on transfer in chapter 5, which I didn't manage to completely read, but which afaik is the best collected resource on transfer learning in humans. They summarize the chapter by remarking that not artificially "dumbing down" a domain when a beginner tries to learn it can delay learning in the beginning, but speed up learning in the long run because it prevents misunderstandings from becoming entrenched.

Inducing Transfer

They also summarize the methods for inducing transfer:

Transferring a skill to new situations is often difficult but can be promoted by following a number of training principles: employing deliberate practice, increasing the variability of practice, adding sources of contextual interference, using a mixed practice schedule, distributing practice in time, and providing process and outcome feedback in an explicit analysis of errors.

—Hoffman et al., “Accelerated Expertise” p. 176, 2014

I'd also have liked to dive deeper on extracting expert knowledge, which looks important especially in novel domains like AI alignment.

Dual N-Back

Summary: Increases working memory, but probably not IQ.

I re-read parts of Gwern 2019 and Gwern 2018, and come away with believing that if one is bottlenecked by working memory, n-back is worth it, but it doesn't work well for increasing intelligence.

Judgmental Forecasting

Summary: I didn't find anything on whether learned forecasting ability transfers across domains. I now want to analyze some data to find out whether it does.

The evidence from the judgmental forecasting research is confusing. On the one hand, it's widely known that domain-level experts are not very good at making predictions about their own domain, and are outcompeted by superforecasters who are just generally good at predicting.

On the other hand, the vibe given by forecasters and forecasting researchers is similar to the following statement:

By the way, there are no shortcuts. Bridge players may develop well-calibrated judgment when it comes to bidding on tricks, but research shows that judgment calibrated in one context transfers poorly, if at all, to another. So if you were thinking of becoming a better political or business forecaster by playing bridge, forget it.

—Philip E. Tetlock & Dan Gardner, “Superforecasting” p. 179, 2015

I tried to find the research this paragraph is talking about by asking in a couple of discord servers and messaging the Forecasting Research Institute, but I didn't get any responses that were satisfying to me.

I now want to analyze my own judgmental forecasting datasets to figure out how much forecasting ability generalizes across domains.

Creating Self-Improving Institutions

Summary: Organizations can become organizations that improve their governing variables. Inducing this is very tricky. Events that can induce double-loop learning in an organization include a change to leaders which value reflection and dialogue, and the introduction of software tools, such as systems which are used for prediction, which then provide feedback.

Double-loop learning is a method to improve learning of organizations, taking into account the learning process itself.

Auqui-Caceres & Furlan 2023 review the evidence on double-loop learning.

They report on several interventions:

[…] these studies maintain that the most prominent barrier to generate DLL is defensive reasoning and routines (Bochman & Kroth, 2010; Clarke, 2006; Kwon & Nicolaides, 2017; Sisaye & Birnberg, 2010; Stavropoulou et al., 2015; Sterman, 1994; Wong, 2005), which are produced by participants in DLL processes, whenever assumptions underlying taken-for-granted procedures, practices, or policies are challenged. Although people are aware that they should not use defensive reasoning to deal with daily work difficulties and challenges (Thornhill & Amit, 2003), they still use them to avoid losing control and dealing with embarrassment (Mordaunt, 2006).

—Auqui-Caceres & Furlan, “Revitalizing double-loop learning in organizational contexts: A systematic review and research agenda” p. 14, 2023

Questions

  1. Is it better to perform elaborative interrogation verbally, or is it as good to write things down?
  2. What is the optimal amount of "going back to the basics" to deepen understanding over time?
    1. Spaced repetition schedules are one suggestion, but they're only geared towards remembering, not deepening understanding.
  3. Do people generalize within judgmental forecasting, across question asking domains?
  4. Why do all papers I've found to gravitate to the "better learning techniques" bucket?
  5. Which techniques do really successful consultancies or investment firms use for problem-solving ability?

My Impression of the Literature

After spending a dozen hours researching this area, my current impression is that this is something that too many different fields are interested in; among them are business people, military psychologists, education researchers, neuroscientists, cognitive psychologists…

This results in a wild outgrowth of terminology: "transfer of learning", "learning to learn", "deutero-learning", "double-loop learning", "design thinking", "adaptive learning" &c. In my research I don't think I've encountered a paper being cited by two different papers, which suggests there's more than a thousand papers grasping at the same question of transfer learning.

I've created an (incomplete) spreadsheet with the relevant papers from the literature that I could find.

See Also


  1. Since everything is judgmental-forecasting-shaped, one could test this by letting forecasters elaborate on their forecasts and at resolution time analyse their elaborations. I've tried doing this but it fell off for other projects. 

  2. These two techniques are treated separately in the paper, but as far as I can tell mostly for historical reasons. 

  3. Judging from Dunlosky et al. 2017 the participants in the various studies were asked to verbally explain their reasoning. It's not said how writing the explanation instead of saying it compares. 

  4. This is supported by the theory of transfer-appropriate processing, which puts an especially strong emphasis on the encoding and retrieval of learned information. As far as I understand, the recapitulation of basic knowledge in the context of more advanced knowledge allows for a more accurate re-encoding of the basic knowledge. This also tracks with my experience of learning mathematics: I've gotten more mileage out of understanding basic concepts deeply (e.g. how probabilities, logits and bits fit together), than understanding more advanced concepts shallowly.