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If you've been building LR drills or looking over your analytics lately, you might have noticed some new tags showing up:
![](https://uploads.7sage.com/wp-content/uploads/2024/07/Screenshot-2024-07-23-at-1.43.09%E2%80%AFPM-2048x950.png)
That's because we've introduced fifteen new LR tags, which we're calling stimulus tags. These new tags cover many of the common patterns we see in the stimuli of LR questions, as well as some of the most important techniques we use to grapple with those stimuli.
Note: To access the new tags, you must be using the current 3-digit PrepTest format. The new tags will not work under the obsolete 2-digit format. To switch to the 3-digit format, navigate to the Drills, PrepTests, or Explanation Videos page and choose the "Current format" button at the top. (You only need to change to the current format on any one of these pages, and the change will be applied across the site.)
![](https://uploads.7sage.com/wp-content/uploads/2024/07/Screenshot-2024-07-26-at-7.33.48%E2%80%AFAM.png)
Why the New Tags?
Until recently, LR tags were only based on the question stem. You know: NA, MSS, Weaken, and all the rest. But that’s a pretty limited way to think about LR. There are so many patterns that bleed across different question types, and improving your ability to recognize those patterns can help you improve your score.
Think about causal reasoning, or the use of “some/most” quantifiers, or the difficulties posed by math-y questions involving percentages and ratios. Any of those elements can show up on any question type. So why should you be limited to searching by question type? Wouldn't it be nice if you could pull up, say, every single question to ever involve conditional reasoning?
Well, now you can. Choose the Conditional Reasoning tag when building a drill, and you'll see every conditional reasoning question out there.
Below is an introduction to each of the new tags, along with links to some of the curriculum concepts represented by these tags.
We hope you find the new tags useful! Have questions or ideas? Leave a comment!
Tag Definitions
Analogy (An)
You'll see this tag when the stimulus involves reasoning by analogy by claiming that two (or more) things are similar in important ways—or that they’re dissimilar in important ways.
This can also include temporal analogies between past, present, and future. (E.g., something was true in the past so it will also be true in the future.)
Causal Reasoning (CausR)
The stimulus features causal reasoning in one or more of the following ways:
- The stimulus connects causes to effects.
- The author observes some phenomenon and forms a hypothesis about its causes and/or effects.
- The author notes that two things are correlated and concludes that one of those things causes the other.
- Cause-and-effect relationships are central to the author’s premises.
To learn more about this form of reasoning, see Logic of Causation.
Conditional Reasoning (CondR)
Understanding conditional relationships is important to understanding the stimulus. This tag covers any question where conditional reasoning is used to reach the conclusion, as well as any MBT, Inf, and MSS question where understanding conditional relationships is important to making inferences.
Elements of conditional reasoning can include the following:
- Triggering a sufficient condition to trigger the necessary condition
- Negating a necessary condition to negate the sufficient condition (i.e., triggering the contrapositive)
- Biconditional relationships (i.e., both conditions are sufficient and necessary)
- Negating conditional relationships (i.e., claiming that one condition does not trigger another)
- Chaining multiple conditional relationships together
To learn more about this form of reasoning, see Conditional and Set Logic.
Eliminating Options (ElimOpt)
The author’s line of reasoning presents two or more possibilities and proceeds to eliminate one or more of those possibilities. The author might eliminate options explicitly or the stimulus might present false dichotomies, in which the author assumes some options don’t exist or don’t apply without having explicitly ruled out those options.
Fact v. Belief v. Knowledge (FvBvK)
There is an important distinction between what is claimed as fact on one hand, and whether people are aware of that fact or believe it to be true on the other hand. This often takes the form of the author stating a fact and then assuming (without warrant) that some other party agrees with that fact.
To learn more about this distinction and how to recognize it, see this lesson.
Kick It Up (KIU)
The stimulus features one of the following situations where the “kick it up” technique is useful for understanding the stimulus or predicting the correct answer:
- A sufficient condition in the premises can be kicked up into the domain.
- A sufficient condition in the conclusion can be kicked up into the premises.
To learn more about this technique, see Kick It Up to the Domain and Kick Up Conditional Conclusions.
Lack of Support v. False Conclusion (LSvFC)
Weak support, or a lack of evidence, for some conclusion is taken as evidence against that conclusion.
To learn more about this reasoning flaw, see this lesson.
Link Assumption (LinkA)
The argument involves an assumption that links two otherwise distinct or independent concepts, by connecting either a premise to the conclusion or a premise to another premise.
Put another way, the stimulus features some kind of "dangling" concept in the premises or conclusion that's detached from the rest of the argument, and yet the author implicitly treats that concept as though it’s connected to the rest of the argument.
Math
A proper understanding of the stimulus or the author’s reasoning relies on arithmetic or other mathematical concepts such as averages, ratios, percentages, or probabilities.
Net Effect (NetEff)
The question hinges on evaluating the overall effect or outcome of something when there are multiple competing factors to consider. This can take one of two forms:
- Cost-benefit analyses, in which following a certain course of action or making a choice from a set of possible options involves a set of benefits (pros) and/or costs (cons). The benefits and/or costs are weighed in order to decide that, on the whole, the course of action is positive or negative, or one option is better or worse than another.
- Situations in which there are multiple competing effects on a single outcome, and those competing effects are weighed against each other. (For example, exercising consumes energy but also increases circulation. Is the net effect feeling worn out or more energized?)
To learn more about this argument structure, see this question and explanation.
Part v. Whole (PvW)
The reasoning in the stimulus moves between “part” and “whole” in one of two ways:
- The author takes a characteristic of a part of something and applies it to the whole of that thing. (E.g., Water is fat-free. Butter contains 15% water. Therefore, butter is fat-free.)
- The author takes a characteristic of the whole of something and applies it to a single part of that whole. (E.g., Our group is diverse. Therefore, I personally am diverse.)
To learn more about this form of reasoning, see this lesson.
Quantifier (Quant)
Understanding partial quantifiers (some, most, not all, few, usually, sometimes, etc.) is important for understanding the information in the stimulus or the author’s line of reasoning.
To learn more about dealing with quantifiers, see Logic of Intersecting Sets.
Rule-Application (RuleApp)
The stimulus deals with rules (or principles) and their applications, whether by deriving rules from specific cases or by applying rules to specific examples.
To learn more about this argument structure, see Pseudo Sufficient Assumption or Rule-Application Questions.
Sampling (Smpl)
The stimulus involves assigning characteristics to a population on the basis of one or more samples. The sample(s) may or may not be representative of the population of interest.
To learn more about this argument structure, see The Ideal Experiment and Falling Short of Ideal.
Value Judgment (ValJudg)
The author’s reasoning requires assigning subjective values or moral weight to concepts within the stimulus, such as that something “should” or “should not” occur or that a certain outcome is positive or negative.