Which has been at least partly debunked as psychology's replication crisis went on, and has been called into question on the neuroscientific angle as well.

1) Replication failure, plain and simple.

2) Overfitting. There are dozens to hundreds of "cognitive biases" on lists: https://en.wikipedia.org/wiki/List_of_cognitive_biases. When you have hundreds of individual points, you really ought to draw some principles, and the principle should not be, "The system generating all this is rigid and inflexible."

3) Imprecision! Again, dozens to hundreds of cognitive biases. What possible behavior or cognitive performance can't be assimilated into the heuristics and biases theory? What can falsify it overall, even after so many of its individual supporting experiments and predictions have fallen down?

It looks like a mere taxonomy of observations, not a substantive theory.

How many meta-analyses have been conducted as of 2017 showing one result or the other? I don't think ego depletion itself has been thoroughly "debunked" yet. If it is a real effect, it's probably quite small - but I don't think that ego depletion has been thrown in the bin just yet.

2) Overfitting. There are dozens to hundreds of "cognitive biases" on lists: https://en.wikipedia.org/wiki/List_of_cognitive_biases. When you have hundreds of individual points, you really ought to draw some principles, and the principle should not be, "The system generating all this is rigid and inflexible."

3) Imprecision! Again, dozens to hundreds of cognitive biases. What possible behavior or cognitive performance can't be assimilated into the heuristics and biases theory? What can falsify it overall, even after so many of its individual supporting experiments and predictions have fallen down?

Wait a second - has anyone ever tried to explain the "IKEA Effect" using Dual Process Theory? What does a laundry-list of supposed cognitive biases have to do with the theory? Is anyone really trying to explain/predict all this almanac-of-cognitive-failings with Dual Process?

To my understanding, yes. That's basically what Dual Process theories exist for: to separate the brain into heuristic/bias processing as one process, and computationally expensive model-based cause-and-effect reasoning as another process. Various known cognitive processes or results are then sort of classified on one side of the line or another.

When you apply Dual Process paradigms to specific corners of cognition, they can be useful. For example, I've seen papers purporting to show that measured uncertainty allows model-free and model-based reinforcement learning algorithms to trade off decision-making "authority". This is less elegant than an explicitly precision-measuring free-energy counterpart, but it's still a viable hypothesis about how the brain can implement a form of bounded rationality when bounded in both sample data and compute power.

But when you scale Dual Processes up to a whole-brain theory, it's just too good at describing anything that involves dichotomizing into a "fast-and-frugal" form of processing and another expensive, reconstructive form of processing. One of the big issues here is that besides the potentially false original evidence for Dual Processes, we don't necessarily have reason to believe there exists any dichotomy, rather than a more continuous tradeoff between frugal heuristic processing and difficult reconstructive processing. The precision-weighting model-selection theory actually makes much more sense here.

Thanks! I've been doing a lot of amateur reading in cog-sci and theoretical neurosci. The subject enthuses me enough that I'm applying to PhD programs in it this upcoming season.

>So what do you think of the original article?

Thorough and accurate. I'll give a little expansion via my own thought. One thing taught in every theoretically-focused ML class is the No Free Lunch Theorem. In colloquial terms it says, "If you don't make some simplifying assumptions about the function you're trying to learn (and the distribution noising your data), you can't reliably learn."

I think experts learn this, appreciate it as a point of theory, and then often forget to really bring it back up and rethink it where it's applicable. All statistical learning takes place subject to assumptions of "niceness". Which assumptions, though?

Seems to me like:

* If you make certain "niceness" assumptions about the functions in your hypothesis space, but few to none about the distribution, you're a Machine Learner.

* If you make niceness assumptions about your distribution, but don't quite care about the generating function itself, you're an Applied Statistician.

* If you make niceness assumptions about your data, that it was generated from some family of distributions on which you can make inferences, you're a fully frequentist or Bayesian statistician.

* If you want to make almost no assumptions about the generating process yielding the data, but still want just enough assumptions to make reasoning possible, you may be working in the vicinity of any of cognitive science, neuroscience, or artificial intelligence.

The key thing you always have to remind yourself is: you are making assumptions. The question is: which ones? The original article reminds us of a whole lot of the assumptions behind current deep learning:

* The "layers" we care about are compositions of a continuous nonlinear function with a linear transform.

* The functions we care about are compositions of "layers".

* The transforms we care about are probably convolutions or just linear-and-rectified, or just linear-and-sigmoid.

* Composing layers enables gradient information to "fan out" from the loss function to wider and wider places in the early layers.

* The data spaces we care about are usually Euclidean.

These are things every expert knows, but which most people only question when it's time to look at the limitations of current methods. The author of the original article appears well-versed in everything, and I'm really excited to see what they've got for the next part.

It may be that distinctions like the one you're describing here are useful to make, but I don't think this claim refutes the possibility of ML "fitting a particular piece within a larger, yet unarticulated model."

I think the assertion is more that our current ways of representing elements of human experience are necessarily very lossy - or that there's some aspect of the situation that you can't describe/implement in terms of models of neural nets.

What neural nets need is to change their data format from plain tensors to object-relation graphs. The input of the network is represented as a set of objects that have relations among them, and the network has to be permute invariant to the order of presentation. An implementation is Graph Convolutional Nets. They learn to compose concepts in new ways and once they learned to count, compare, select by color, they can solve any combination of those concepts as well. That way the nets generalize better and transfer knowledge from a problem to the next.

Graphs are able to reduce the complexity of learning a neural net that can perform flexible tasks. But in order to get to even better results, it is necessary to add simulation to the mix. By equipping neural nets with simulators, we can simplify the learning problem (because the net doesn't have to learn the dynamics of the environment as well, just the task at hand). Examples of simulators used in DL are AlphaGo, the Reinforcement Learning applications on Atari Games, protein/drug property prediction, generative adversarial networks (in a way).

The interesting thing is that graphs are natural for simulation. They can represent objects as vertices and relations as edges, and by signal propagation the graph works like a circuit, a simulator, producing the answer. My bet is on graphs + simulators. That's how we get to the next level (abstraction and reasoning). DeepMind seems to be particularly focused on RL, games and recently, relation networks. There is also work on making dynamic routing in neural nets, in fact applying graphs implicitly inside the net, by multiple attention heads.

This doesn't necessarily address all the nuances of your post, but I do believe it's a step in the right direction. It pushes networks from

"learn how to solve this completely statically defined problem via sophisticated pattern matching"

to

"learn how to interpret a query, drawn from some restricted class of possible queries; accept variable-structure input to the query; strategize about techniques for answering the query; and finally compute the answer, possibly over multiple time-steps"