On 22 Dec 2013, at 13:28, Craig Weinberg wrote:
http://medicalxpress.com/news/2013-12-odd-easy-feat-mind.html
Even scientists are fond of thinking of the human brain as a
computer, following sets of rules to communicate, make decisions and
find a meal.
I thought that only Dreyfus cofused computer and expert system. A
computer follows simple laws, but a brain, a priori, too. Sets of
rules is ambiguous, and quite misleading when discussing the
possibility or impossibility of computationalism.
Almost all adults understand that it's the last digit—and only the
last digit —that determines whether a number is even, including
participants in Lupyan's study. But that didn't keep them from
mistaking a number like 798 for odd.
A significant minority of people, regardless of their formal
education, believe 400 is a better even number than 798, according
to Lupyan, and also systematically mistake numbers like 798 for odd.
After all, it is mostly odd, right?
Well, that make sense if you say that a number is more "even" if it
has a bigger power of two factor.
400 -> 200 -> 100 -> 50 -> 25 (biggest power of two factor = 16 = 2^4)
798 -> 399 (biggest power of two factor = 2 = 2^1)
"Most of us would attribute an error like that to carelessness, or
not paying attention," says Lupyan, whose work was published
recently in the journal Cognition. "But some errors may appear more
often because our brains are not as well equipped to solve purely
rule-based problems."
Nor is a digital neuronal net.
Asked in experiments to sort numbers, shapes, and people into simple
categories like evens, triangles, and grandmothers, study subjects
often broke simple rules in favor of context.
For example, when asked to consider a contest open only to
grandmothers and in which every eligible contestant had an equal
chance of victory, people tended to think that a 68-year old woman
with 6 grandchildren was more likely to win than a 39-year old woman
with a newborn grandkid.
"Even though people can articulate the rules, they can't help but be
influenced by perceptual details," Lupyan says. "Thinking of
triangles tends to involve thinking of typical, equilateral sorts of
triangles. It is difficult to focus on just the rules that make a
shape a triangle, regardless of what it looks like exactly."
In many cases, eschewing rules is no big deal. In fact, it can be an
advantage in assessing the unfamiliar.
"This serves us quite well," Lupyan says. "If something looks and
walks like a duck, chances are it's a duck."
Unless it's a math test, where rules are absolutely necessary for
success. Thankfully, humans have learned to transcend their reliance
on similarity.
"After all, although some people may mistakenly think that 798 is an
odd number, not only can people follow such rules—though not always
perfectly—we are capable of building computers that can execute such
rules perfectly," Lupyan says. "That itself required very precise,
mathematical cognition. A big question is where this ability comes
from and why some people are better at formal rules than other
people."
That question may be important to educators, who spend a great deal
of time teaching rules-based systems of math and science.
"Students approach learning with biases shaped both by evolution and
day-to-day experience," Lupyan says. "Rather than treating errors as
reflecting lack of knowledge or as inattention, trying to understand
their source may lead to new ways of teaching rule-based systems
while making use of the flexibility and creative problem solving at
which humans excel."
Following, or not, rules, is a level dependent question. You can
simulate with prolog (which is a universal system with rules) a
neuronal nets (a universal system without rule), and vice versa.
Bruno
http://www.ncbi.nlm.nih.gov/pubmed/24156803
The difficulties of executing simple algorithms: why brains make
mistakes computers don't.
Lupyan G.
Abstract
It is shown that educated adults routinely make errors in placing
stimuli into familiar, well-defined categories such as triangle and
odd number. Scalene triangles are often rejected as instances of
triangles and 798 is categorized by some as an odd number. These
patterns are observed both in timed and untimed tasks, hold for
people who can fully express the necessary and sufficient conditions
for category membership, and for individuals with varying levels of
education. A sizeable minority of people believe that 400 is more
even than 798 and that an equilateral triangle is the most
"trianglest" of triangles. Such beliefs predict how people
instantiate other categories with necessary and sufficient
conditions, e.g., grandmother. I argue that the distributed and
graded nature of mental representations means that human algorithms,
unlike conventional computer algorithms, only approximate rule-based
classification and never fully abstract from the specifics of the
input. This input-sensitivity is critical to obtaining the kind of
cognitive flexibility at which humans excel, but comes at the cost
of generally poor abilities to perform context-free computations. If
human algorithms cannot be trusted to produce unfuzzy
representations of odd numbers, triangles, and grandmothers, the
idea that they can be trusted to do the heavy lifting of moment-to-
moment cognition that is inherent in the metaphor of mind as digital
computer still common in cognitive science, needs to be seriously
reconsidered.
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