跟读练习: 1 8 What is Causation - 通过视频学习英语口语
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Two events can behave in a connected way without necessarily being causally connected.
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They might simply be correlated.
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For example, it might seem that a rooster crowing at dawn causes the sun to rise.
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Certainly, the rooster crowing consistently precedes the sunrise in a very systematic way.
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But how can we be sure that the rooster does not actually cause the sun to rise?
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Well, we can intervene in some way on the rooster, making it impossible for him to crow, and nonetheless the sun will rise.
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So, consistent pre-occurrence is not sufficient to distinguish true causation from a mere appearance of causation.
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Other times, there might be what is called a lurking variable
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that can account for the apparent link between events that appear to be causally linked.
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Take for example the amount of clothing that people wear and the amount of ice cream that they eat.
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The more clothing they wear, the less ice cream they eat.
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And the more ice cream they eat, the less clothing they wear.
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Does ice cream cause people to wear less or does wearing more clothing cause people to eat less ice cream?
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No, of course not.
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We can see that there's a lurking variable, in this case temperature, that drives both outcomes.
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As it gets hotter outside, people tend to want to wear less clothing and they also want to eat more ice cream.
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So neither temporal correlation nor any other kind of correlation in outcomes is sufficient to establish true causation between events.
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How can we establish the existence of a genuine causal relationship between two things that both vary in time?
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The most popular models of causation nowadays are called interventionist or manipulationist models of causation,
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defended in recent years by thinkers such as Judea Pearl and John Woodward.
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Their models of causation are rooted in the intuition that
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if some variable associated with event A causes something to change in another event, B, then one should be able to manipulate A in some way
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and see corresponding changes in B that happen after changing A.
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If A is modeled as causing B, then there should be an intervention on A that results in B changing its value.
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These kinds of models of causation basically describe what scientists have done for centuries to determine causal relationships among variables.
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Scientists try to control for all independent variables.
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calls this screening off the other variables besides A that likely partially cause B, by holding their values constant.
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Then we vary the single variable A in order to see the consequences or changes
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expressed by some outcome or dependent variable B.
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If B changes with an intervention on A, it's concluded that A causes B, at least in part.
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For example, if you manipulate the thermostat, and the furnace goes on,
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and it gets warmer, that's a causal relationship because turning on the thermostat makes the furnace turn on.
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But causation is not always so simple, especially in the brain and mind, as we shall see.
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背景与情境
本视频围绕“因果关系”展开，通过公鸡打鸣与日出、穿衣量与冰淇淋食用量等实例，讲解相关关系与因果关系的区别，还介绍了干预主义因果模型。内容逻辑清晰，包含较多学术概念，适合提升英语听力与思辨能力，也是练习英语影子跟读的优质素材。
日常交流高频短语5则
- causally connected：因果相关
- lurking variable：潜在变量
- interventionist model：干预主义模型
- screen off：隔离（变量）
- dependent variable：因变量
影子跟读分步指南
针对视频特点，可按以下步骤练习shadow speak：
1. 先听1-2遍，理解“相关≠因果”核心观点，标记长难句（如干预主义模型的描述）。
2. 逐句跟读，重点模仿学术词汇的发音（如“manipulationist”）与逻辑重音（如“并非充分条件”中的“并非”）。
3. 段落跟读时，关注句间停顿（如举例后的换气），提升语流连贯性。
4. 用shadowing site录音对比，修正节奏与语调，尤其注意“however”“so”等连接词的语气。
5. 复述关键实例，如“潜在变量”的例子，强化语言输出与内容理解。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。