Neural networks are computer models intended to reflect the organization of human brains and are often used in studies of...
GMAT Information and Ideas : (Ideas) Questions
Neural networks are computer models intended to reflect the organization of human brains and are often used in studies of brain function. According to an analysis of 11,000 such networks, Rylan Schaeffer and colleagues advise caution when drawing conclusions about brains from observations of neural networks. They found that when attempting to mimic grid cells (brain cells used in navigation), while 90% of the networks could accomplish navigation-related tasks, only about 10% of those exhibited any behaviors similar to those of grid cells. But even this approximation of grid-cell activity has less to do with similarity between the neural networks and biological brains than it does with the rules programmed into the networks.
Which finding, if true, would most directly support the claim in the underlined sentence?
The rules that allow for networks to exhibit behaviors like those of grid cells have no equivalent in the function of biological brains.
The networks that do not exhibit behaviors like those of grid cells were nonetheless programmed with rules that had proven useful in earlier neural-network studies.
Neural networks can often accomplish tasks that biological brains do, but they are typically programmed with rules to model multiple types of brain cells simultaneously.
Once a neural network is programmed, it is trained on certain tasks to see if it can independently arrive at processes that are similar to those performed by biological brains.
I'll solve this step by step, following the structured approach to help you understand exactly how to tackle Command of Evidence questions.
Step 1: Decode and Map the Passage
Create Passage Analysis Table
| Text from Passage | Analysis |
|---|---|
| Neural networks are computer models intended to reflect the organization of human brains and are often used in studies of brain function. |
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| According to an analysis of 11,000 such networks, Rylan Schaeffer and colleagues advise caution when drawing conclusions about brains from observations of neural networks. |
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| They found that when attempting to mimic grid cells (brain cells used in navigation), while 90% of the networks could accomplish navigation-related tasks, only about 10% of those exhibited any behaviors similar to those of grid cells. |
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| But even this approximation of grid-cell activity has less to do with similarity between the neural networks and biological brains than it does with the rules programmed into the networks. |
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Provide Passage Architecture & Core Elements
Main Point: Researchers warn that neural networks, despite being designed to model brains, may not provide reliable insights about actual brain function because their behaviors stem from programmed rules rather than genuine biological similarity.
Argument Flow: The passage establishes what neural networks are supposed to do, then presents a warning from researchers. It supports this warning with specific evidence from a grid cell study, showing that while networks can perform brain-like tasks, very few actually operate like real brain cells. Even when they do approximate brain cell behavior, this stems from artificial programming rather than true biological resemblance.
Step 2: Interpret the Question Precisely
What's being asked? Which finding would most directly support the underlined claim (that grid-cell approximation has more to do with programmed rules than biological similarity)
What type of answer do we need? Evidence that would strengthen/support this specific claim about programmed rules being more important than biological similarity
Any limiting keywords? Most directly support - we need the strongest, most direct evidence for this particular claim
Step 3: Prethink the Answer
- The underlined claim argues that when networks approximate grid-cell behavior, it's primarily due to their programmed rules rather than actual similarity to biological brains
- For evidence to support this claim, we'd need something showing that the programmed rules are artificial/different from how real brains work
- The similarity is superficial rather than genuine biological resemblance
- The programming is what creates the behavior, not brain-like processes
The rules that allow for networks to exhibit behaviors like those of grid cells have no equivalent in the function of biological brains.
✗ Incorrect
- This directly supports the claim by showing the programmed rules have no equivalent in the function of biological brains
- If the rules creating grid-cell behavior don't exist in real brains, then the behavior must be artificial rather than biologically similar
The networks that do not exhibit behaviors like those of grid cells were nonetheless programmed with rules that had proven useful in earlier neural-network studies.
✗ Incorrect
- This talks about networks that DON'T exhibit grid-cell behaviors, but our claim is about networks that DO exhibit these behaviors
- The claim is specifically about the 10% that do show grid-cell-like activity
Neural networks can often accomplish tasks that biological brains do, but they are typically programmed with rules to model multiple types of brain cells simultaneously.
✗ Incorrect
- This is about networks modeling multiple types of brain cells simultaneously - too broad and not specific to grid cells
- Doesn't address the key issue of whether grid-cell approximation comes from biological similarity or programmed rules
Once a neural network is programmed, it is trained on certain tasks to see if it can independently arrive at processes that are similar to those performed by biological brains.
✗ Incorrect
- This describes the general training process for neural networks but doesn't address the specific claim about grid-cell behavior
- Doesn't help us understand whether grid-cell approximations come from biological similarity or artificial programming