Memory can be a substitute for thinking in one obvious way – you know (remember) the answer instead of having to think about it.
But this substitution can happen in more ways than the above.
Consider this problem – Today is a Sunday. Every Sunday, there is a conference call of your organisation. Suppose today you are told that you have to make a presentation one month from now. Then how many Sunday’s later is that resentation going to be?
You can think. Something like say – One month period is 4 weeks i.e. 4 ‘week-intervals’. So (it strikes you) that there should be 5 Sundays, from the beginning of the one month period, uptill the end of the one month period. So counting today as the first Sunday, the 5th Sunday (since that signals the end of the one month period from today) is the presentation Sunday. So (5-1) i.e. 4 Sundays later, is the presentation day.
Now, you could have avoided the thinking, by mechanically writing down on a paper, say, vertical line markings for a Sunday, one after another, and labelling every new line as an ordered Sunday (1st, 2nd etc.) and kept an eye on when 4 ‘week intervals’ (gaps between 2 consecutive vertical lines) get over. That Sunday (its ordered number) is the presentation day.
Here, in the second method, thinking has been substituted by something mechanical. But what have you done by writing all this on the paper? You have unloaded the burden off your memory – having to remember and keep track of where you are in the process – since you are “seeing the status” of things as you are bringing them to mind. So basically, you have used (nothing but) memory, by writing all that on the paper, that approach!
This is how memory has played an actual, literal substitute for thinking (in the guise of a mechanical, literal method of solving the problem).
AI Essays 
“According to Daniel Kahneman’s theory, described in his book “Thinking, Fast and Slow” (Kahneman 2011), hu-mans’ decisions are supported and guided by the cooperation of two main kinds of capabilities, that, for sake of simplicity are called “systems”: system 1 provides tools for intuitive, imprecise, fast, and often unconscious decisions (“thinking fast”), while system 2 handles more complex situations where logical and rational thinking is needed to reach a complex decision (“thinking slow”).”
Also see https://arxiv.org/pdf/2010.06002.pdf
“This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI(for instance, adaptability, generalizability, common sense,and causal reasoning), we may obtain similar capabilities inan AI system by embedding these causal components. We hope that the high-level description of our vision includedin this paper, as well as the several research questions that we propose to consider, can stimulate the AI research community to define, try and evaluate new methodologies, frameworks,and evaluation metrics, in the spirit of achieving a better understanding of both human and machine intelligence”
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