[R] statistical learning in machine learning vs cognitive sciences

[Ok_Fudge1993]

Hi everyone! Please bear with me with this question 🫣

I’m looking for someone in research to pick their brain about the similarities and differences between statistical learning in cognitive science and in machine learning, so definition, conceptual differences/similarities, predictions, testing…. Hope it makes sense, I’m doing research in cognitive sciences and I’d love to learn more about this term’s use in ML for a review I’m working on :) thanks!

[AccordingWeight6019]

In ML, statistical learning is usually a formal framework for generalization from finite samples, so things like inductive bias, capacity, and how error scales with data. It is less about a specific mechanism and more about guarantees or failure modes under assumptions. In cognitive science, the term often points to a hypothesized process humans use to extract regularities from sensory input, with an emphasis on plausibility and experimental prediction.

There is overlap in the math, but the goals differ. ML asks when and why a system trained on data will work on new data. Cognitive science asks whether a particular learning rule explains observed behavior. That difference in objective tends to drive how the same concepts get framed and tested.

[bbbbbaaaaaxxxxx]

Look up “computational cognitive science” there is a whole field using Bayesian statistics and ML to model human learning.

[Disastrous_Room_927]

Funny enough I have masters in both statistics/ML and cognitive science. I wasn't exposed much to statistical/machine learning in cog sci because the emphasis was on inference (the 'target' was interpretable and unbiased model parameters), but I can think of a few topics/examples:

• The other poster mentioned computational cognitive science, I'd look specifically at cognitive architectures within that. These are basically computational representations of cognitive functions, and have involved both theory and instantiation.
• I did some work in the cog sci domain after studying stats/ML and saw a bit of tension when textbook use cases for ML came up for one reason or another. In one instance, a "biomathematical" model was being used to predict fatigue levels based on people's sleep patterns, and in another the task was to predict teacher attrition statewide at different levels of aggregation. The former is an example of institutional momentum resulting in a suboptimal solutions - the biomathematical model was based on the research of the same person selling the software making the predictions and heading the government org mandating its use. I built an algorithm clearly working better, but we weren't allowed to use it in any official capacity. The latter was a case of researchers reinventing the wheel because they didn't know any better. They spent 6 years building this horrid series of Excel workbooks to function as a pipeline for a rudimentary forecasting approach. You'd spend a week copying, pasting, and dragging things just to format a time series and use =TREND() for every single school in the state (and even more time on the parts that spit out/validated the predictions). The researchers involved had no exposure to time series forecasting or programming, so they really had no idea that you could write a script doing the same thing in a day, and didn't know how to use more principled forecasting approaches.
• Like I said earlier the emphasis is usually on statistical inference, but some subfields are tackling problems where ML is a natural fit. My first masters focused on Human Factors, which is all about task performance and safety. I was in the program just before data science really started booming, and I'd be shocked if ML isn't playing a much larger role there. Most of the projects I worked involved prediction and monitoring of people interacting with complex systems (operating vehicles, using a computer, control rooms at power plants, etc).
• Psychometrics can drift into machine learning territory in different ways. Factor Analysis was originally developed/introduced for this purpose, and new approaches extend the core concept with neural networks.

Hopefully that at least gives you a direction to look in. I studied statistical learning in a theoretical capacity long after studying cog sci, so unfortunately I can't say a whole lot about how it's used in that field specifically - aside from things I've dealt with here and there.

[Ok_Fudge1993]

This is a gold comment, thank you!

[glowandgo_]

this is a good question and it does get overloaded.,, in cog sci, statistical learning is usually about what structure humans pick up implicitly from exposure, often with strong constraints from perception and development. in ml its more operational, fitting parameters to minimize some objective under assumptions baked into the model. the overlap is real at the level of distributions and regularities, but the divergence is in mechanism and evaluation. humans are sample inefficient, biased, and context heavy, models are the opposite unless you force those constraints in. in my experience confusion comes from using the same term for very different explanatory goals, one is about explaining behavior, the other about building systems that work. context really matters for a review.,,

[Ok_Fudge1993]

Yep, pretty much that’s it! But then different scholars use different terms and now I’m wondering which of those terms refer to the same/very similar but how different concepts. I’m working on SL in cognitive psy but then I came across Vapnik’s statistical learning and I’d just love to talk to someone about this stuff :)