跟讀練習: 1 8 What is Causation - 通過影片學習英語口語
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33 句
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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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背景與情境
本影片圍繞“因果關係”展開，通過公雞打鳴與日出、穿衣量與冰淇淋食用量等例項，講解相關關係與因果關係的區別，還介紹了干預主義因果模型。內容邏輯清晰，包含較多學術概念，適合提升英語聽力與思辨能力，也是練習英語影子跟讀的優質素材。
日常交流高頻短語5則
- causally connected：因果相關
- lurking variable：潛在變數
- interventionist model：干預主義模型
- screen off：隔離（變數）
- dependent variable：因變數
影子跟讀分步指南
針對影片特點，可按以下步驟練習shadow speak：
1. 先聽1-2遍，理解“相關≠因果”核心觀點，標記長難句（如干預主義模型的描述）。
2. 逐句跟讀，重點模仿學術詞彙的發音（如“manipulationist”）與邏輯重音（如“並非充分條件”中的“並非”）。
3. 段落跟讀時，關注句間停頓（如舉例後的換氣），提升語流連貫性。
4. 用shadowing site錄音對比，修正節奏與語調，尤其注意“however”“so”等連線詞的語氣。
5. 複述關鍵例項，如“潛在變數”的例子，強化語言輸出與內容理解。
什麼是跟讀法？
跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。