>So yeah, I think human thinking may have severe flaws that are rather similar to the ones discussed in the article, but that doesn't prove that current AI has all the components needed to match humans.

I adamantly agree, there is probably a lot of generally applicable (i.e. not domain-specific) "tricks" or "implicit insights" that mammal brains use which we haven't discovered yet.

Another good example is, how it's often stated that humans have no trouble learning things "in one shot", but how on closer inspection that may or may not be true and is very hard to verify:

Consider how when we have a clear negative or positive experience (say being thrown out of class or standing in a corner facing the wall versus getting a compliment on your work etc), we afterwards typically replay the sequence of events and how it led to the current situation. It is unclear if this replaying occurs only for the highest-level abstract thoughts possibly centralized into a couple of regions recording and replaying episodic memory, or if this is actually happening in a more decentralized way throughout the brain while we are only subjectively aware of this episodic aspect at the conscious level. If this self replaying of the last lessons that apply locally is spread and operating independently throughout the brain, could this be an explanation of the brain wave patterns? Is this origin of the "aha" signals or is that just a fantastic concoction of the reproducibility crisis?

Local feedback signals (numbering on the order of number of synapses) vs global feedback signals (numbering on the order of kinds of hormones, diffusing neurotransmitters, blood sugar etc)
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We still know very little of the feedback mechanisms in the brain, is it a low number of global feedback signals, or a large number of local feedback signals?

A) global feedback: the hardcoded feedback mechanism is primitive wetware (listening to the low number of chemical signals) while the high-speed feedback is learned by neurons influencing each other in the prograde direction as emergent behaviour resulting from this primitive wetware (the only feedback being through axons literally feeding back as opposed to feedforward networks); or

B) local feedback: if the high-speed feedback is hardcoded wetware too, i.e. retrograde signalling of adjoint derivatives across the synapse, which would mean that the reverse accumulation automatic differentiation we use is closer to biology than currently accepted.

For example in A) suppose there is a low bandwidth (low frequency) feedback reward signal, say blood sugar rising after eating a given sweet for the first few times, then the shortest path between the taste buds and the chewing and swallowing motor neurons would improve their weights for intake, a first level anticipation, but then later seeing or touching the sweet or actively seizing one might cause the previously trained neurons to train the newly recruited signal paths by some other neurotransmitter reward by anticipation.

Fast-approach-but-imprecise adaptation vs Slow-approach-but-precise fine-tuning
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Another facet is that we currently have "1 training regime" for digital neural networks, with which I mean: we use gradient descent both to 1) adapt the weights from a totally randomized initial state to a somewhat acceptable state, and then after a nonexistent pause, 2) to continue improving the weights near the top of the hill (or bottom of valley...) of the scoring landscape. We have no guarantee that nature uses a single regime, let me concoct a hypothetical (and thus improbable) guess: perhaps it uses gradient descent to get the weights approximately where they should be, but then a per-synapse lock-in amplifier correlates (multiplies over time 2 signals while summing) the positive / negative feedback with its own positive or negative variation on the weight, smoothes this product-sum correlation signal (low pass filtering) to get a very high precision feedback signal which has higher precision than a single instantaneous "sample" of feedback (local or global). Throughout the brain synapes some would be closer to the instantaneous feedback signal regime (in response to new lessons. or for short term memory), while others would be closer to the LIA regime, for longer term memory and / or precision. That would be trillions of lock-in amplifiers in a single human brain...

EDIT: in fact SGD stochastic gradient descent can already be seen as a global LIA mechanism, but with all weights / synapses using the same low pass filter. In theory we could give each synapse / weight not only a weight but also each their own timescale (or example count scale, the tau in (alpha) and (1-alpha) multiplier factors when expressed as a filter), and adapt the timescale depending on the local feedback signal (adjoint derivative) in the backpropagation algorithm. To vary the weight just use weight times (1 + 0.001 times the randombit bit r), and multiply it with the feedback signal (the component of the usual gradient, i.e. the derivative that corresponds to this synapse / weight). A similar trick could be used to vary the timescale. One could also hardcode the timescale for certain synapses as a hyperparameter to forcibly locate short term vs long term memory pathways.

EDIT2: As an example of the function of weights that would settle (or were forced) at long vs short timescales at low or high levels:

low level, short term: adapting to reverb entering or leaving a church, adapting to noise when entering or leaving a party,

low level, long term: associating spectral peaks among frequency bins (the fundamental and higher harmonics for sounds of different timbres would remain in roughly the same place, regardless of background noise)

high level, short term: unimportant small talk, or important but quickly dealt with information (did I already pay this drink or can I walk away?)

high level, long term: what is my pin code?