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Darren Lott's avatar

Something is happening with this famous example that makes people fail so reliably. Essentially, it is using a “magician’s force.”

If the ball cost problem was presented mathematically like:

x + (x + 1.00) = 1.10

I doubt that the failure rate of University students would still be 50%. Would it be 5% or 0.5% or lower? Probably.

The wording of the problem is intended to engage a verbal attempt at solving it. Remember all the parsing needed to pull meaning from the scenario.

“A bat and ball cost $1.10”

(Two objects are presented in a specific order, and equated with a number also separated in order by two pieces by the decimal. The first object is bat and the first number segment is a dollar; the second object is ball and matches ten cents. Even if the problem is read aloud, “dollar ten” is still two ordered items. This results in:

{bat, dollar}, {ball, ten cents}

Next is the direct sentence further suggesting the cost of the bat is a dollar.

“The bat cost $1...”

Of course, left alone it would be a direct lie, but it continues “more than the ball.”

Since a dollar is more than 10 cents the bat would certainly cost more than the ball. A true statement. The lack of a comma means we are supposed to parse “more” in the sense of an inequality and some people recognize that and restructure the scenario mathematically. But most people will not jump off the initial word base association that was “forced” onto them.

“A Pickle and an Onion when purchased together cost 90 cents. The Onion costs twice as much as the Pickle. How much does each cost?”

How many of your friends would fail simple math problems when presented without the magician’s force?

Herry's avatar

It’s really interesting. I tested the problem on an older LLM (a local 2023 Mistral) and it behaved like a human, made the mistake, and only solved it after I pointed out the error. Current models, with their optimized reasoning algorithms, naturally don’t fail. I believe the brain works differently when it tries to understand a text compared to a mathematical reasoning task. In the first case, we search our memory for words to understand the meaning of sentences and produce replies. In the second, we use computational logic. When we realize we need to use both methods, we solve the problem—just like LLMs have learned to do.

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