SAT词汇陷阱：为什么你认识 model 却还是做错题?

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发布时间：2026/06/05 17:09:20

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很多同学在备考SAT时都会有一种错觉：单词我认识，题目却总是做不对。

事实上，SAT最喜欢考的，往往不是那些生僻难词，而是那些你“自以为会”的常见词。你知道它的中文意思，却不了解它在学术语境中的真正含义;你会翻译，却不会利用它理解文章逻辑和出题思路。

今天，我们就通过一个再熟悉不过的单词——model，看看SAT究竟在考什么，以及为什么一个看似简单的词，能够成为很多同学失分的根源。

一、以小见大：你真的掌握model这个词了吗?

SAT阅读中，经常有一些单词，你觉得你会了，但是你对它的掌握只是皮毛，根本没有到达考试要求的理解程度，今天我们就一起来看一个看似很“简单”的单词：model

大家看到model的第一反应就是“模型”。

没错，但模型也分实物模型(比如飞机模型，汽车模型)和抽象的模型(比如供需模型)，而SAT最长考的就是model表示抽象模型这个部分的意思。

在学术语言中，model 指的是一种对现实世界某个方面的简化、抽象、系统化的表征，用于理解、解释、预测或模拟。它不是实物模型，而是概念性/理论性的框架。

而且仅到这一步还不够，你还得知道在SAT考试中，除了理解model作为抽象模型所表达的含义之外，还要知道：

1)model只是理论，不能与实际的观测和研究相混淆;

2)评价一个model的好与坏，经常是通过比对它预测的结果是否和实际的结果相一致

二、你以为会，其实不会：SAT高频词 model 的真正考法

下面我们来看一道题目：

In their study of the steering muscles regulating sclerites (minute hardened structures) in the Drosophila (fruit fly) wing hinge, Johan M. Melis et al. used machine learning to devise a convolutional neural network (CNN) model capable of predicting the pattern of wing motion produced by the maximum activity of the muscles. The CNN model's output aligned with results of prior studies by other researchers measuring muscle activity patterns directly—one of several indications, said Melis et al., that the model accurately represents important biomechanical processes underlying wing motion.

Which choice best states the main purpose of the text?

A. To present evidence from Melis et al.'s study in support of the efficacy of their CNN model

B. To provide an overview of how Melis et al. honed the accuracy of their CNN model

C. To compare results obtained by Melis et al. using their CNN model to prior results obtained from other researchers' models

D. To account for Melis et al.'s reliance in their study on a CNN model in lieu of direct measurement

该段文字的核心内容是：Melis 等人利用机器学习构建了一个 CNN 模型，其预测结果与先前直接测量的结果一致，作者以此作为证据来证明该模型的有效性。因此，主要目的是呈现支持模型有效性的证据，所以选择A选项

这里很多同学会误选C选项，那就需要注意一下，Melis是把自己的模型预测结果与其他人的直接观测结果所比较的(results of prior studies by other researchers measuring muscle activity patterns directly)，所以直接观测结果和model完全是两个对立的概念，因此C选项最后一个单词有严重的错误

SAT阅读误区model1.jpg

Researchers used anonymized location data from the US and Côte d’Ivoire to document people’s daily patterns of mobility, using these results to test the efficacy of the researchers’ predictive computer model. In each country, unidirectional cycles among two, three, or four locations were empirically the most common pattern types; the graph shows each of these pattern types as a proportion of all pattern instances found for that country (e.g., the measured value for CI 3 in the graph, 0.12, indicates that the three-location pattern constituted 12% of all pattern instances in the Côte d’Ivoire data). The researchers ran their model twice under different assumptions, concluding that emphasizing the salience of local population density over personal preferences generally yielded the best results.

Which choice most effectively uses data from the graph to illustrate the researchers’ conclusion?

A. Under the assumption that preferences are more salient than density, the US 2 and CI 2 proportions were predicted to be approximately 0.45 and 0.35, respectively, both below the measured values, whereas under the other assumption, the model overestimated the proportion for US 2 and overestimated that for CI 2.

B. Under the assumption that preferences are more salient than density, the US 2 and CI 2 proportions were predicted to be in the range of 0.3 to 0.5, placing them farther from the measured values than were those predicted under the other assumption.

C. Under the assumption that density is more salient than preferences, the US 2 and CI 2 proportions are approximately 0.65 and 0.85, respectively, significantly higher than the values predicted under the other assumption and thus farther than those predictions from the measured values.

D. Under the assumption that preferences are more salient than density, the two-location patterns (US 2 and CI 2) were predicted to be most frequent in the data even though neither proportion was projected to exceed 0.5, well below the proportion predicted under the other assumption.

这段文字的核心内容是：研究人员利用美国和科特迪瓦的匿名位置数据，识别出两地最常见的日常移动模式(2、3或4个地点间的单向循环)，并用这些真实数据来检验他们的预测模型。通过在不同假设下运行模型，他们得出结论：优先考虑局部人口密度而非个人偏好的设定，通常能获得最佳的模型预测效果。

如果我们要证明优先考虑局部人口密度的模型设定比个人偏好的设定更好，那这个更好的标准是什么呢?

其实就是我们上面讲的哪个模型的预测结果与现实一致，知道了这个标准之后，我们就知道正确答案的方向了，也就是正确选项要指出优先考虑局部人口密度的模型设定比个人偏好的设定更接近实际测量的值，这样一看答案就很清楚了，选B(注：出题人在此还绕了个小弯子，B选项没有直接说优先考虑局部人口密度的模型设定比个人偏好的设定更接近实际测量的值，而是说优先考虑个人偏好的模型设定比局部人口密度的设定更不接近实际测量的值，属于反过来表达了)。

三、一个 model，暴露了多少SAT阅读误区?

通过这个例子，同学们就要记住了，有的时候做不对题目不仅仅是因为单词不认识，还可能因为你对单词认识的不够“深”，这个比仅仅不认识单词更让人抓狂，因此在备考的过程中，多关注这些看似其貌不扬，实则深不见底的单词吧!

从今天的两个例子可以看出，SAT考查词汇，从来不是简单地考察“认不认识这个单词”，而是在考察你是否真正理解它在学术语境中的含义和作用。

对于 model 而言，我们不仅要知道它表示“模型”，更要理解它是一种对现实世界的理论化解释;同时还要明白，学术研究中评价一个模型是否优秀，关键在于它的预测结果能否与真实观测结果相吻合。

欢迎在线咨询澜大教育SAT培训课程，很多阅读题的突破口，恰恰藏在这些看似普通的高频词里。与其一味追求背更多生词，不如深入挖掘这些“熟词”的深层含义。因为真正拉开分差的，往往不是你不认识的单词，而是那些你以为自己已经认识的单词。

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