Day 19｜複数段落にまたがる情報を統合する
【メイン課題】
以下の英文を 4分〜5分以内 に読みましょう。
英文：
The relationship between language and thought has fascinated philosophers and linguists for centuries. One of the most influential theories on this topic is the Sapir-Whorf hypothesis, which suggests that the structure of a language influences the way its speakers think and perceive the world. According to this theory, people who speak different languages may actually experience reality in fundamentally different ways.
Evidence supporting this idea comes from research on color perception. Russian speakers, who have separate basic terms for light blue and dark blue, can distinguish between these shades more quickly than English speakers, who use a single word, “blue,” for both. Similarly, the Piraha people of the Amazon, whose language has no words for specific numbers, have difficulty performing tasks that involve exact counting. These findings suggest that language can shape certain aspects of perception and cognition.
However, many linguists reject the strong version of the Sapir-Whorf hypothesis, which claims that language completely determines thought. If this were true, it would be impossible to translate ideas between languages or for speakers of one language to understand concepts that exist only in another. The fact that people can learn new languages and adopt new ways of thinking demonstrates that thought is not imprisoned by language. Most researchers today accept a weaker version of the hypothesis: that language influences but does not determine how we think.
The debate has practical implications for education and communication. Understanding that language shapes thought can help educators develop more effective teaching methods for multilingual students. It also highlights the value of learning foreign languages, which can open up new perspectives and ways of thinking. In international business and diplomacy, awareness of how language affects perception can improve cross-cultural communication and prevent misunderstandings.
Recent advances in neuroscience are providing new insights into the language-thought relationship. Brain imaging studies have shown that different languages activate different neural pathways, suggesting that bilingual speakers may actually process information differently depending on which language they are using. As technology allows researchers to study the brain in greater detail, our understanding of how language and thought interact will continue to deepen.
設問：
（1）サピア＝ウォーフ仮説の「強い版」と「弱い版」の違いを本文に基づいて説明しなさい。
（2）第2段落と第3段落はどのような関係にあるか。
（3）以下の情報は本文のどの段落に書かれているか（複数段落にまたがる場合はすべて答えること）。
a. 言語が思考を完全に決定するわけではないという主張
b. 色の認識に関する実験
c. 言語と思考の関係の教育への応用
（4）文章全体の主張を日本語2文以内でまとめなさい。
模範解答を見る ▼
【模範解答】
各段落の要点：
– 第1段落：サピア＝ウォーフ仮説の紹介（言語が思考に影響する）。
– 第2段落：仮説を支持する証拠（色の認識、数の概念）。
– 第3段落：仮説の強い版への反論と弱い版の受容。
– 第4段落：教育・ビジネス・外交への実用的意味合い。
– 第5段落：神経科学の進歩による新たな知見。
設問の解答：
（1）
– 強い版：言語が思考を完全に決定する（言語が異なれば思考も完全に異なる）。
– 弱い版：言語は思考に影響を与えるが、完全に決定するわけではない。
– 現在の研究者の多くは弱い版を支持している。
（2）第2段落はサピア＝ウォーフ仮説を支持する証拠を提示し、第3段落はその仮説（特に強い版）に反論している。つまり「支持する証拠→反論」の関係にある。
（3）
– a：第3段落（”language completely determines thought” を否定）
– b：第2段落（ロシア語話者の色の区別の実験）
– c：第4段落（教育への応用）
（4）言語は思考に影響を与えるが完全には決定しないというのが現在の主流的な見解である。この理解は教育や異文化コミュニケーションに実用的な示唆を与え、神経科学の進歩によってさらに深まりつつある。
ポイント（速読のコツ⑲）：
– 長文問題では 複数段落にまたがる情報を統合する力 が求められる。各段落を読んだら「この段落は全体の中でどんな役割か？」を一言でメモしよう（例：証拠、反論、応用、結論）。段落の「役割メモ」があれば、設問が「どの段落に書いてあったか」を瞬時に特定できる。
【練習問題】以下の英文を4分〜5分以内に読み、設問に答えなさい。
英文：
Artificial intelligence is transforming the field of healthcare in remarkable ways. Machine learning algorithms can now analyze medical images such as X-rays, CT scans, and MRIs with accuracy that matches or exceeds that of human radiologists. AI-powered diagnostic tools can detect diseases like cancer, diabetic retinopathy, and heart conditions at earlier stages, potentially saving countless lives.
In drug discovery, AI is dramatically reducing the time and cost required to develop new medications. Traditional drug development typically takes 10 to 15 years and costs billions of dollars. AI systems can analyze vast databases of molecular structures to identify promising drug candidates in a fraction of the time. During the COVID-19 pandemic, AI played a crucial role in accelerating vaccine development by helping researchers identify effective protein structures.
AI is also improving patient care through personalized medicine. By analyzing a patient’s genetic information, medical history, and lifestyle data, AI systems can recommend treatments tailored to the individual rather than relying on a one-size-fits-all approach. Wearable devices equipped with AI can monitor vital signs continuously and alert patients and doctors to potential health issues before they become serious.
Despite its promise, the integration of AI into healthcare raises important ethical and practical concerns. Issues of data privacy, algorithmic bias, and the potential for misdiagnosis must be carefully addressed. The medical profession must also determine how to balance AI recommendations with human clinical judgment and maintain the patient-doctor relationship that is central to quality healthcare.
設問：
（1）AIが医療に貢献している分野を3つ挙げ、それぞれどの段落に書かれているか答えなさい。
（2）文章全体の主張を日本語1文でまとめなさい。
練習問題の解答を見る ▼
【練習問題 解答】
設問の解答：
（1）
– ①診断（医療画像の分析・早期発見）→ 第1段落
– ②創薬（新薬開発の時間とコスト削減）→ 第2段落
– ③個別化医療（患者に合わせた治療の推奨）→ 第3段落
（2）AIは診断・創薬・個別化医療の分野で医療を大きく変革しているが、データプライバシーやアルゴリズムのバイアスなどの倫理的・実用的課題に慎重に対処する必要がある。